[1st Edition] 9780128121160, 9780128121153

Table of contents :
Content:
Series PagePage ii
CopyrightPage iv
ContributorsPage vii
Chapter One - Intergroup Perception and Cognition: An Integrative Framework for Understanding the Causes and Consequences of Social CategorizationPages 1-80K. Kawakami, D.M. Amodio, K. Hugenberg
Chapter Two - Self-Distancing: Theory, Research, and Current DirectionsPages 81-136E. Kross, O. Ayduk
Chapter Three - Essentially Biased: Why People Are Fatalistic About GenesPages 137-192S.J. Heine, I. Dar-Nimrod, B.Y. Cheung, T. Proulx
Chapter Four - The Intrapersonal and Interpersonal Dynamics of Self-Regulation in the Leadership ProcessPages 193-257K. Sassenberg, M.R.W. Hamstra
Chapter Five - Sex Differences in Jealousy: A 25-Year RetrospectivePages 259-302J.E. Edlund, B.J. Sagarin
IndexPages 303-307
Contents of Other VolumesPages 309-323

Citation preview

SERIES EDITORS JAMES M. OLSON

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CONTRIBUTORS D.M. Amodio New York University, New York, NY, United States O. Ayduk University of California, Berkeley, CA, United States B.Y. Cheung University of British Columbia, Vancouver, BC, Canada I. Dar-Nimrod University of Sydney, Sydney, NSW, Australia J.E. Edlund Rochester Institute of Technology, Rochester, NY, United States M.R.W. Hamstra Maastricht University, Maastricht, The Netherlands S.J. Heine University of British Columbia, Vancouver, BC, Canada K. Hugenberg Miami University, Oxford, OH, United States K. Kawakami York University, Toronto, ON, Canada E. Kross University of Michigan, Ann Arbor, MI, United States T. Proulx University of Cardiff, Cardiff, Wales, United Kingdom B.J. Sagarin Northern Illinois University, DeKalb, IL, United States K. Sassenberg Leibniz-Institut f€ ur Wissensmedien; University of T€ ubingen, T€ ubingen, Germany

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CHAPTER ONE

Intergroup Perception and Cognition: An Integrative Framework for Understanding the Causes and Consequences of Social Categorization K. Kawakami*,1, D.M. Amodio†, K. Hugenberg{ *York University, Toronto, ON, Canada † New York University, New York, NY, United States { Miami University, Oxford, OH, United States 1 Corresponding author: e-mail address: [email protected]

Contents 1. Person Construal 1.1 Perceiving Persons and Groups From the “Bottom-Up” 1.2 Perceiving Persons and Groups From the “Top-Down” 2. Persons Construed 2.1 Activation of Category-Based Knowledge 2.2 Downstream Consequences of the Activation of Category-Based Knowledge 2.3 Strategies to Reduce the Activation of Category-Based Knowledge and Biased Behavior 3. Conclusions References

5 6 20 33 33 41 48 59 59

Abstract The primary aim of this chapter is to provide a framework to understand and synthesize the processes of person construal—early perceptions that lead to initial ingroup/ outgroup categorizations—with the processes involved in intergroup relations. To this end, we review research examining the initial perception and categorization of ingroup and outgroup members and its downstream consequences. We first discuss bottom-up processes in person construal based on visual features (e.g., facial prototypicality and bodily cues), and then discuss how top-down factors (e.g., beliefs, stereotypes) may influence these processes. Next, we examine how the initial categorization of targets as ingroup or outgroup members influences identification, stereotyping, and groupbased evaluations, and the relations between these constructs. We also explore the implications of the activation of these constructs for a range of social judgments

Advances in Experimental Social Psychology, Volume 55 ISSN 0065-2601 http://dx.doi.org/10.1016/bs.aesp.2016.10.001

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2017 Elsevier Inc. All rights reserved.

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including emotion identification, empathy, and intergroup behaviors. Finally, we describe a variety of well established and more recent strategies to reduce intergroup bias that target the activation of category-based knowledge, including intergroup contact, approach orientations, evaluative conditioning, and perspective taking.

It is an exciting time to be an intergroup researcher. New methodologies and ways of thinking about intergroup biases are abundant. Based in part on multidisciplinary work in this area, research on social categorization processes has made robust advances (Amodio, 2014a; Freeman & Ambady, 2011; Hugenberg, Young, Bernstein, & Sacco, 2010; Kawakami, 2014). These advances have been particularly informative about the earliest stages of processing ingroup and outgroup members and have been fueled by work in social neuroscience, social vision, face perception, emotion, and social cognition. Our goal in this chapter is to provide a framework for understanding the initial perception and categorization of ingroup and outgroup members and the downstream consequences of these processes. Our chapter is organized in two major sections: Person Construal and Persons Construed. As depicted in Fig. 1, Section 1, Person Construal, reviews the processes involved in the initial perceptual encoding of others. Drawing on social cognitive and neuroscience evidence, we explore the interaction of bottom-up target effects (e.g., visual cues) and top-down effects (e.g., expectancies and situational factors) as they relate to early attention, affective responses, and memory for ingroup and outgroup members. Section 2, Persons Construed, focuses on how this initial categorization leads the perceiver to imbue a target with a wealth of category-based knowledge. These processes include the activation of self-outgroup associations (identification), group characteristics (stereotypes), and evaluations (prejudice). We then examine the implications of the activation of these constructs for a range of social judgments, including emotion identification, empathy for outgroups, and decision making and behaviors in an intergroup context. Finally, we explore strategies to reduce these biases. The importance of initial categorical processes and the accompanying activation of group-based knowledge to intergroup relations is undeniable (Dovidio, Kawakami, & Gaertner, 2002; Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Fiske, 1998). Yet the way we measure intergroup processes and biases has changed dramatically over the past 20 years (Gawronski & Payne, 2011; Sherman et al., 2008), and these advances, in turn, have significantly influenced how we conceive of these processes.

Implicit identification Bias reduction strategies Approach orientations Perspective-taking Social acceptance Evaluative conditioning Interdependence

Bottom-up factors Facial cues Bodily cues Bodily movement Shared group cues

Implicit stereotypes Categorization as Human/nonhuman Ingroup/outgroup Member of specific group

Bias reduction strategies Perspective-taking Counterstereotypic training Exposure to nonstereotypic exemplars Stereotype inhibition Behavioral control

Top-down factors Social identity Prejudice Stereotypes Individuation Similarity Ingroup homogeneity Economic scarcity Status/Power Outcome dependent Self-relevant

Implicit prejudice Bias reduction strategies Evaluative conditioning Exposure to nonstereotypic exemplars Exposure to positive exemplars Approach orientations Confronting own bias Response inhibition Behavioral control

Fig. 1 A framework for understanding the causes and consequences of social categorization.

Downstream consequences Emotion identification Outgroup empathy Responses to bias Policy endorsement Decision making Intergroup behavior

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Today, the term intergroup bias includes a broad collection of reactions to outgroup category members, ranging from the earliest stages of neural responses associated with face encoding and affective responses, to shifts in attention and eye gaze, to the automatic activation of conceptual associations, to manifold downstream consequences that include deficits in emotion recognition and identification of outgroup faces, and ultimately a lowered willingness to interact with an outgroup member. The list goes on. One important distinction in the conceptualization and measurement of these biases is between explicit and implicit processes (Amodio & Mendoza, 2010; Dovidio, Gaertner, & Kawakami, 2010; Greenwald & Banaji, 1995). Unlike explicit biases, implicit processes can operate outside of conscious awareness. In particular, people may be unaware that they possess specific associations with social categories or unaware of how these associations affect their responses to outgroup members. This distinction is important in an intergroup context because we live in a society with strong norms against racial prejudice that discourage expressions of bias (Crandall, Eshleman, & O’Brien, 2002; Nosek, Hawkins, & Frazier, 2012; Plant & Devine, 1998). Because of these standards, people are motivated to avoid acting in ways that would indicate that they are treating people from other groups differently (Apfelbaum, Sommers, & Norton, 2008; Kawakami, Karmali, et al., under review; Norton, Sommers, Apfelbaum, Pura, & Ariely, 2006). Responses on measures targeting implicit constructs are typically considered to be less controllable and thus are often more negative and show more bias than responses on measures targeting explicit processes (Dovidio, Kawakami, Smoak, & Gaertner, 2009; Nosek, 2007). In this chapter, we focus for the most part on implicit processes. A primary goal of the current framework, however, is also to move beyond the common binary implicit–explicit framework (Amodio, 2014b). By doing so, we can investigate not just whether a process is implicit or explicit, but rather focus more on the specific function served by a mental process in intergroup categorizations and social interactions. This framework allows us to delve deeper into the complex ways in which we discriminate based on initial categorical information. Although early stage processing of categorical features is often considered implicit because of the limited processing time related to these measures (Amodio, HarmonJones, & Devine, 2003; Bean et al., 2012; Ito & Urland, 2005), later stage responding and interacting with category members can also be implicit (Bargh & Williams, 2006). In this chapter, we highlight a process-focused approach related to initial early and later stages of person construal and

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factors that can potentially influence this process. Our goals are to provide a nuanced understanding of intergroup biases and to suggest new ways to reduce the negative impact of perceived category membership.

1. PERSON CONSTRUAL Human survival depends on group living—on the sharing of resources and protection within a group and the ability to manage coalitions and conflicts with other groups. For the social psychologist, this fundamental reliance on group membership raises a crucial question: how do we determine which people belong to which groups? According to classic theory on intergroup relations, the starting point for intergroup relations is social categorization—the cognitive process of classifying people according to their social category (Allport, 1954; Campbell, 1965; Tajfel & Turner, 1979, 1986). However, recent research on person construal (Freeman & Ambady, 2011) and social vision ( Johnson & Adams, 2013; Johnson, Lick, & Carpinella, 2015; Ofan, Rubin, & Amodio, 2011; Ratner & Amodio, 2013) has pushed back the starting point for understanding intergroup relations to the basic perceptual building blocks of explicating how low-level perceptual processing of features of others, such as facial cues, bodily cues, and vocal cues, can be extracted and integrated to categorize them. Put simply, this emerging body of research investigates not only the implications of social categorization, but its determinants as well. The traditional approach to social categorization assumed a “feedforward” process, whereby early perceptual cues of stimuli are spontaneously extracted and lead in a bottom-up manner to a single, dominant categorization of the stimulus (for review, see Freeman & Johnson, 2016; Macrae & Bodenhausen, 2000). Indeed, it is the case that bottom-up features from stimuli play a significant role in categorization; perceivers are very sensitive to category-diagnostic cues of race or sex in others’ faces and bodies. In race categorizations, facial features such as skin color and facial physiognomy are important in category decisions about others; for example, targets with darker skin tone and more Afrocentric facial features are more likely to be categorized as Black (Dunham, Stepanova, Dotsch, & Todorov, 2015; Krosch & Amodio, 2014; MacLin & Malpass, 2001, 2003; Stepanova & Strube, 2009, 2012a, 2012b). Further, targets with more prototypical phenotypic characteristics are ascribed more stereotypic traits, behaviors, and outcomes (Blair, Judd, & Chapleau, 2004; Blair, Judd, Sadler, & Jenkins, 2002; Eberhardt,

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Davies, Purdie-Vaughns, & Johnson, 2006; Maddox, 2004; Maddox & Gray, 2002; but see Wilson & Rule, 2015). More recently, however, a number of theorists have challenged this feed-forward perspective on categorization, arguing instead that categorization is the product of both bottom-up and top-down influences. Indeed, in their influential dynamic interactive theory of person construal, Freeman and Ambady (2011) argue that low-level perception and higher-order social cognition interact over time to create a relatively stable categorization of targets. Thus, whereas perceptual cues of targets can and do feed forward to influence categorization, so too can top-down expectancies and motives feed downward to affect categorization. Finally, from Freeman and Ambady’s perspective, both bottom-up and top-down sources of person construal mutually constrain one another in a connectionist model, which allows categories to be mutually activated (e.g., simultaneous activation of male and female categories) and to change over time, ultimately settling into a stable categorization of a target (e.g., as either male or female). Adopting this same perspective that both bottom-up cues of stimuli and top-down beliefs and motives of perceivers interact to determine person construals, we first address how bottom-up cues of targets can be used to generate social categorizations of others. Specifically, we highlight (1) how people extract key information from the faces and bodies of others, (2) how this information signals whether others are human (or not), and (3) how this information signals a person’s social group membership. We then discuss how top-down characteristics of the perceiver (e.g., expectations, attitudes, stereotypes) or the situation (e.g., intergroup motives, intergroup anxiety) can influence these categorization decisions and feed down into the perceptual stream to alter the meaning or interpretation of the original percept.

1.1 Perceiving Persons and Groups From the “Bottom-Up” Person perception often begins when light reflected off a face hits the retina. This initial percept triggers a chain of bottom-up processes through which the mind encodes it as a face, determines its physical attributes and identity, and begins to infer social categories and its significance to the perceiver. In this section, we describe research on the bottom-up processes through which others’ faces and bodies are resolved into person construals, before moving to a discussion of top-down effects.

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1.1.1 Basic Processes in Face Perception: Cognitive Processes, Neural Structures, and Intergroup Effects Faces, it seems, are special in a number of ways. Perhaps most important for the current work, faces are a rich source of social information, providing key cues to others’ identities, their intentions and goals, and their social group memberships—a point to which we return below (see Hugenberg & Wilson, 2013, for a review). Successful intragroup and interpersonal functioning depends on our ability to read others’ faces. Successful coalition building relies in part on the ability to extract and recall the identities of others. Remembering people who are allies and ingroup members and those who are enemies and outgroup members is a necessary condition for group living and navigating intergroup contexts (Pokorny & de Waal, 2009). Similarly, facial cues are highly valuable in regulating social interactions (Argyle & Cook, 1976; Frischen, Bayliss, & Tipper, 2007). Gazing toward a speaker can indicate interest (Richmond, McCroskey, & Hickson, 2008) and signal impending interaction (Khalid, Deska, & Hugenberg, 2016), whereas gazing away can signal disinterest or even social rejection (Wirth, Sacco, Hugenberg, & Williams, 2010). Others’ faces can tell us what they are thinking, what they are feeling, and help us predict what they are likely to do (Can˜adas, Lupia´n˜ez, Kawakami, Niedenthal, & Rodrı´guez-Bailo´n, 2015; Fridlund, 1994; Nummenmaa, Hy€ on€a, & Hietanen, 2009). Put simply, extracting information from others’ faces appears to be a key skill for a group dwelling species like our own. But faces are special not just because they provide a rich source of information for navigating intra- and intergroup life, but also because of the way that they are processed in the brain. Specifically, faces appear distinct from nonface stimuli in at least two ways: first, faces are processed in a manner that occurs for very few other stimuli. Second, processing appears to be supported by neural structures that are specifically sensitive to faces. Human’s process faces in a manner dissimilar from virtually all other stimuli by integrating the individual features of the face into a unified Gestalt, a process known as configural face encoding (Maurer, Le Grand, & Mondloch, 2002).a Whereas objects are not processed configurally by most perceivers, in that we can identify them easily in different orientations and with variations in features (Tanaka & Gauthier, 1997), most faces are. One can easily see the effects of configural processing using the well-established a

In accordance with Maurer et al. (2002), we define holistic processing as a subset of configural processing.

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Fig. 2 Upright faces, but not inverted faces, are processed configurally.

face inversion paradigm (Yin, 1969). When the typical eyes-over-noseover-mouth configuration of features in faces is disrupted by inverting a face, this dramatically reduces perceivers’ ability to process the stimulus (see Fig. 2). Face inversion undermines memory for faces, but not for nonface objects such as aircraft and houses (see Valentine, 1988, for a review). Additional support for the notion of face-specific processing comes from research on prosopagnosia, a neurological disorder typically associated with damage or congenital malfunction in the fusiform cortex. People with prosopagnosia are unable to recognize faces of known individuals—an impairment rooted in the inability to process faces configurally (Barton, Press, Keenan, & O’Connor, 2002; Riddoch, Johnston, Bracewell, Boutsen, & Humphreys, 2008). Whereas prosopagnosics can typically process the individual features of faces—eyes, noses, mouths—and even recognize faces by distinct features (such as Gorbachev’s prominent birthmark), they cannot fit the features of a face together into a coherent Gestalt. This deficit is striking given the ease with which healthy individuals process faces configurally (Gauthier, Curran, Curby, & Collins, 2003; Tanaka & Curran, 2001), and it highlights the specialized capacity humans have for perceiving faces compared with other stimuli. Faces also appear to be processed in specialized regions of the healthy brain (Kanwisher, McDermott, & Chun, 1997). Haxby, Hoffman, and Gobbini (2000) described the neural process of face perception in terms of a core network for face encoding and an extended network supporting effects of person knowledge, social factors, and emotional expression.

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TPJ

OFA

Fig. 3 Lateral view of brain indicating the occipital face area (OFA) in the inferior occipital cortex, the fusiform face area (FFA) in the fusiform cortex, the posterior superior temporal sulcus (STS), the temporoparietal junction (TPJ), the prefrontal cortex (PFC), and the orbital frontal cortex (OFC).

According to this useful framework (see Fig. 3), the core network includes (but is not limited to) the inferior occipital gyrus (i.e., the occipital face area; OFA), the lateral fusiform gyrus (i.e., the fusiform face area; FFA), and the posterior superior temporal sulcus (pSTS), with greater involvement typically observed in the right hemisphere (Haxby et al., 2001). Research on the neural substrates of face perception has helped to distinguish different major components of this process. The OFA supports the featural encoding of faces, whereby specific features (e.g., eyes, nose, or mouth) are independently identified and processed. The FFA, by comparison, supports the configural processing of faces (Kanwisher et al., 1997; McCarthy, Puce, Gore, & Allison, 1997), whereby separate features are integrated into a single Gestalt and represented as a holistic face (i.e., another person). The FFA may further support the encoding of individual facial identity (Hoffman & Haxby, 2000). This may help explain why prosopagnosics can extract features without integrating them—the brain has structures that uniquely support these two different operations. Further, this feature integration process occurs early in the perceptual stream; research using event-related potentials (ERP) has isolated a characteristic neural signal in the occipital–temporal region (N170) at approximately 170 ms after stimulus onset that likely reflects early configural processing (Bentin, Allison, Puce, Perez, & McCarthy, 1996). Although faces are known to elicit greater attention in comparison with most other visual

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stimuli (Ro, Russell, & Lavie, 2001; Theeuwes & Van der Stigchel, 2006), research has shown that the N170 response is specifically related to the perceptual encoding of faces and not merely to attention (cf. Jacques & Rossion, 2007). Finally, the STS supports inferences of facial dynamics, including expression and gaze direction (Hasson, Nir, Levy, Fuhrmann, & Malach, 2004; Winston, Henson, Fine-Goulden, & Dolan, 2004). Although these regions are most strongly responsive to faces, other proximal regions are also known to contribute to aspects of face identification (Grill-Spector, Knouf, & Kanwisher, 2004; Hanson, Matsuka, & Haxby, 2004; Haxby et al., 2000). Components of this “core network” receive input from regions associated with emotion, including the amygdala, orbital frontal cortex (OFC), and insula, and with social cognition, including the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ), and posterior cingulate cortex (PCC). It is believed that this “extended network” modulates the operations of the FFA and other core structures to facilitate the perception of a target’s personal identity, emotional expression, and intentions (Fairhall & Ishai, 2007; Gauthier et al., 2000). For example, in support of this framework, faces expressing anger and fear have been found to enhance activity in the amygdala, insula, and OFC, as noted earlier, and these activations are believed to shape high-level visual and cognitive processes (Bar et al., 2006; Freeman, Ambady, & Holcomb, 2010). 1.1.2 Configural Face Processing and Intergroup Relations Importantly, research has recently demonstrated that even these earliest stages of face processing can be both cause and consequence of intergroup distinctions and motives. In the current work, we focus on two links between early facial feature integration processes and intergroup processes. First, we discuss how configural face processing (or the lack thereof ) is implicated in dehumanization, and second, we discuss how configural face processing can be influenced by intergroup motives. 1.1.2.1 Perceptual Dehumanization

Configural face processing appears to serve as a cue for whether a face is actually a conspecific. Put simply, configural face processing appears to cue humanness, and conversely, the failure to process a face configurally can trigger or signal dehumanization. This hypothesis that dehumanization and configural face processing are mutually caused has received support from a number of sources. Specifically, there is indirect evidence indicating that dehumanized outgroups are often not processed configurally to the same

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extent as are ingroups, with different types of faces eliciting differential levels of configural processing. For example, research using composite face tasks reliably finds that outgroups are typically afforded less configural face processing than ingroups (Hugenberg & Corneille, 2009; Michel, Rossion, Han, Chung, & Caldara, 2006; see also Ratner & Amodio, 2013, for neural evidence from the N170). Similarly, facially stigmatized individuals also elicit less configural face processing. Because facial stigmas attract visual attention to the specific stigmatizing feature (feature-based processing; Madera & Hebl, 2012), they can undermine perceivers’ ability to process the face (Ackerman et al., 2009). Further, objectified groups, such as sexually objectified women, are perceptually processed more like objects and less like humans, as compared to sexualized men. Specifically, Bernard, Gervais, Allen, Campomizzi, and Klein (2012) employed an inversion task in which they briefly flashed the image of a sexualized man or woman (i.e., nearly nude), either upright or inverted, and then showed participants images of two targets (one actual and one distractor) and asked which they had seen. For male targets, inversion disrupted recognition, which is typical of configurally processed targets (e.g., human faces). For female targets, however, inversion did not influence recognition, a result more typical of objects. Conversely, nonhuman stimuli with humanlike face configurations are spontaneously anthropomorphized. For example, Windhager et al. (2012) found that face-like configurations in the front end of cars (with headlights mapped to eyes and grills mapped to mouths) elicited anthropomorphism. Cars with headlight-to-grill configurations that appeared wider and more angular (i.e., more like a mature face) were seen as more dominant, relative to their rounder (i.e., more like a neotonous face) counterparts. This argument that configural face processing can trigger ascriptions of humanity has received direct support as well. For example, Hugenberg et al. (2016) recently demonstrated that face inversion disrupts the signal that a face is human—in essence, we found that dehumanization can occur from the “bottom-up.” In our first study, participants completed a modified lexical decision task (LDT). In each trial, participants first saw an upright or an inverted face for 100 ms, followed immediately by a letter string that was a word or a pronounceable nonword. Critically, the actual words in the LDT were either related to humans (e.g., human, person) or machines (e.g., machine, device). We found that upright but not inverted faces facilitated recognition of human-related words. Thus, disrupting configural face processing (via inversion) disrupted the ability of the face to activate humanrelated concepts.

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In a second study, participants were tasked with categorizing a series of upright and inverted human and chimpanzee faces as humans and animals, respectively. Whereas inversion inhibited the categorization of human faces, inversion had no effect on the categorization speed of chimpanzee faces. Thus, the signal that a target is human appears to stem in part from the face configuration, whereas the signal that a target is a chimpanzee may be extracted from features alone. Finally, in a third study, participants were tasked with rating upright and inverted human faces on a variety of personality traits indicative of humanness (e.g., humanlike, creative). The results showed that inverted faces were rated as having lower levels of humanlike characteristics; even the most face valid dimension of humanity—humanlike—yielded the same pattern of results (see Fincher & Tetlock, 2016, for similar results). In another recent study, we (Cassidy et al., under review) found that the race of targets also moderated these effects of inversion on ascriptions of humanness. Specifically, for White perceivers, inverted Black faces were especially strongly dehumanized, relative to both upright Black and both inverted and upright White faces. Thus, for an outgroup already targeted with dehumanizing associations and ideologies (Goff, Eberhardt, Williams, & Jackson, 2008), disrupting the perceptual signal of humanness appears to have a particularly potent effect. Finally, recent evidence indicates that this link between configural processing and perceptual dehumanization may actually be the result of a specific facial configuration: facial width-to-height ratio (fWHR). Across 10 studies, we (Deska, Lloyd, & Hugenberg, under review-a) have demonstrated that faces with a larger fWHR are seen as less than fully human in a broad variety of ways. For example, faces with higher fWHR are infrahumanized (as less able to experience secondary emotions; Leyens et al., 2000), are both animalistically and mechanistically dehumanized (Haslam, 2006), are denied agentic characteristics such as the ability to self-regulate (Gray, Gray, & Wegner, 2007), and are overtly likened to humans’ evolutionary ancestors (Kteily, Bruneau, Waytz, & Cotterill, 2015). Further, high fWHR targets are seen as incapable of social roles requiring cognitive and emotional sophistication (e.g., opera critic), but are seen as strong fits to roles that require more brawn than brains (e.g., furniture mover). Although high fWHR targets are not seen as universally bad, they are seen as consistently lacking in human sophistication. Taken together, these data indicate that the signal that a target is a fellow human appears to arise quite early in the perceptual stream, and can be both cause and consequence of dehumanization. Faces that are not processed

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configurally fail to activate human-related concepts, are more difficult to categorize as human, and are seen as lacking in humanlike characteristics. Put simply, the extent to which a person is dehumanized is a product, at least in part, of how faces are perceptually processed, and we believe this underscores how important it is to understand early person construal. 1.1.2.2 Perceptually Unambiguous Categories Are Distinguished Early and Easily From Faces

Most classic models of person perception and intergroup relations argue that some “basic” social categories, such as race, sex, and age, are perceptually obvious and dominant in early social cognition (e.g., Brewer, 1988; Fiske & Neuberg, 1990; Stangor, Lynch, Duan, & Glass, 1992). In fact, social categorization of these perceptually obvious groups does indeed typically occur quickly, effortlessly, and often quite spontaneously in most contexts. Very rapidly after perceiving a face, low-level perceptual characteristics that distinguish among social categories, such as race, sex, and age, are believed to be extracted quickly by the visual system. Evidence for the early categorization of social features has come from ERP studies, which can assess the effects of social categories on neural responses on the order of milliseconds (Amodio & Bartholow, 2011). Ito and Urland (2003) first used ERP measures to examine the early and potentially implicit processing of race and gender. In their studies, participants were exposed to faces of White and Black, male and female individuals, and were tasked with simply categorizing the faces by race or sex. The race of the targets affected ERPs as early as 122 ms after stimulus onset, whereas target sex effects occurred approximately 50 ms later. Strikingly, the early neural response to race occurred even when participants were instructed to categorize based on target gender (a pattern that may have reflected implicit racial associations or participant concerns about appearing prejudiced that led them to attend to race rather than gender). Mouchetant-Rostaing, Girard, Bentin, Aguera, and Pernier (2000) demonstrated that targets’ sex had similarly early effects on processing, with sex effects as early as 65 ms (in negative polarity ERP components) and 165 ms (in parietal regions) after stimulus onset. Similar effects for race and sex have been observed with other paradigms as well (Amodio, 2010; Ito & Urland, 2005; Kubota & Ito, 2007; see Amodio, Bartholow, & Ito, 2014, for a review). The brain also responds to age cues on a face very early in the processing stream. Ebner, He, Fichtenholtz, McCarthy, and Johnson (2011), for example, demonstrated

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that targets’ age (old vs young faces) influenced electrophysiological responses throughout the processing stream, but began as early as 160 ms after stimulus onset. Because these effects occur so early in the processing stream—sometimes even before the brain typically begins to integrate facial features into a meaningful gestalt (approximately 170 ms after stimulus onset)—they are typically interpreted as revealing bottom-up responses to coarse visual differences between race, sex, and age groups, such as in luminance, low-frequency information (e.g., face shape), or contrast patterns (e.g., skin wrinkles). This means that, at this very early stage of processing, psychologically meaningful categories, such as race, sex, and age, are detected independent of holistic face encoding, and thus separate from the detection of a target’s identity. Furthermore, these perceptually “basic” social categories, of sex, race, and age, also appear to be accurately extracted from faces even when they are seen only briefly or suboptimally. For example, research has demonstrated that a variety of suboptimal viewing conditions, such as face inversion, blurring, and rapid presentation, dramatically interfere with the extraction of a target’s identity, but have little effect on the extraction of sex category information (Cloutier, Mason, & Macrae, 2005; Macrae, Quinn, Mason, & Quadflieg, 2005). Finally, it appears that these “basic” social categories are often extracted from faces spontaneously and without intent (Can˜adas, Rodrı´guez-Bailo´n, Milliken, & Lupia´n˜ez, 2013). Although intentions to process faces semantically appear to influence the spontaneous extraction of basic categories from faces (Macrae, Bodenhausen, Milne, Thorn, & Castelli, 1997; Macrae et al., 2005; Quinn, Mason, & Macrae, 2009, 2010), research has demonstrated basic social categorization processes even with subliminally presented faces (e.g., Bargh, Chen, & Burrows, 1996; Chen & Bargh, 1997; Macrae & Martin, 2007), indicating that this process is not dependent on intention or awareness. 1.1.2.3 Social Categorization of “Concealable” Categories From Perceptual Cues

Although facial features can be quite informative about social category memberships (e.g., skin tone, facial neotony), some social categories are not immediately apparent from such cues. Instead, many social categories provide only weak or ambiguous phenotypic signals of category membership. For example, sexual orientation has long been considered a “concealable” social category (Herek & Capitanio, 1996) because of the absence of reliable physical cues related to sexual orientation. Similarly,

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membership in religious categories (highly pertinent for many modern intergroup conflicts; Neuberg et al., 2014) is often not associated with clearcut phenotypic cues. Despite the intuition that these categories have no apparent perceptual features, recent research suggests that people can differentiate between these categories on the basis of surprisingly minimal cues (see Alaei & Rule, 2016; Tskhay & Rule, 2013, for a review). Although it is a matter of some debate, it appears that perceivers are reliably above chance in categorizing the sexual orientation of faces at zero acquaintance (Rule, Ambady, Adams, & Macrae, 2007, 2008; Rule, Ambady, & Hallett, 2009; but see Cox, Devine, Bischmann, & Hyde, 2015). In line with the premise that sexual orientation is concealable, these categorizations are imperfect, with perceiver accuracy at 60–70% relative to a guessing rate of 50%, and are often worse than baserate information in ecological settings (Olivola & Todorov, 2010). Nonetheless, stimulus exposures as low as 50 ms appear sufficient to elicit the above-chance accuracy observed in most studies (Rule & Ambady, 2008), and gay vs straight stereotypes are activated spontaneously upon presentation of gay and straight male faces (Rule, Macrae, & Ambady, 2009), suggesting a process that is efficient and that can occur without explicit intentions. Similar effects have been observed with religious categories as well. For example, in a set of studies, Rule, Garrett, and Ambady (2010a) presented participants with White faces with neutral expressions and no facial hair, head hair, or other potential exogenous cues to category membership, and instructed them to categorize the stimuli as Mormon or non-Mormon. Just as with prior work on sexual orientation, perceivers could categorize the faces by religion at better-than-chance levels (58% accuracy relative to a guessing rate of 50%). Related work (Rule, Garrett, & Ambady, 2010b) demonstrated that such categorizations also had implications for face recognition. These findings corroborate effects seen in classic research. For example, Allport and Kramer (1946) reported that perceivers were slightly better than chance (56% accurate relative to a guessing rate of 50%) at distinguishing the religion of pictures of Jewish and non-Jewish faces, an effect that was acutely true for perceivers high in anti-Semitism. Contemporaneously, Lund and Berg (1946) had a group of 18 participants guess the religious tradition of nearly 3000 individuals who they watched walking through a room and heard being interviewed. These judges showed approximately 87% accuracy in categorizing the religious background of the targets. Although such effects have not always been replicated, there appears to be a small but significant

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effect in meta-analyses for accuracy in such categorizations (Rice & Mullen, 2003; Tskhay & Rule, 2013). Political affiliation, too, can be extracted from the face with abovechance accuracy. For example, Rule and Ambady (2010) had participants view pictures of the faces of self-identified Democrats and Republicans (e.g., men and women who ran as Democratic and Republican candidates for the 2004 and 2006 Senate elections; yearbook photographs of people in College Democrat or College Republican clubs on campus). They found that American Democrats and Republicans could be categorized at zero acquaintance with better-than-chance accuracy based on their faces. Olivola and Todorov (2010) reported similar results in a large sample (N > 1000) using candidates from the 2002 and 2004 House of Representatives elections. How is it that perceivers are able to perceive ostensibly “invisible” identities with above-chance accuracy? Generally speaking, this ability appears to be related to the use of partially accurate facial stereotypes (Prothro & Melikian, 1955). For example, to determine sexual orientation, perceivers appear to use the facial masculinity and femininity of faces; men with more feminine face shapes and skin textures are judged as gay more often than men with more masculine face shapes and skin textures (Freeman, Johnson, Ambady, & Rule, 2010). In this way, sex atypicality appears to be reliably used as a cue to determine sexual orientation (Rieger, Linsenmeier, Gygax, & Bailey, 2008). Rule et al. (2010b) demonstrated that the ability to accurately discriminate between Mormon and non-Mormon faces was due primarily to skin health. Mormons tend to lead healthier lifestyles (e.g., no smoking, no drinking, etc.) than nonMormons, which participants used as a cue for categorization, allowing for above-chance accuracy. Finally, political affiliation, too, has facial correlates related to social categories. Rule and Ambady (2010), for example, demonstrated that stereotypes related to political groups (Republicans stereotyped as powerful; Democrats stereotyped as warm) can facilitate accuracy in categorizations. Not only are faces of Republicans and Democrats more powerful and warm, respectively, but also faces seen as more powerful are more likely to be categorized as Republicans and faces seen as warmer are more likely to be categorized as Democrats. Taken together, these data indicate that early face perception processes, such as the extraction of warmth and dominance cues from faces, can have important downstream consequences for categorization even for apparently ambiguous or concealable categories.

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1.1.2.4 Social Categorization From Bodily Cues

Although a variety of social categories can be extracted with surprising facility from faces, recent research has also demonstrated that basic categories can be extracted from body shape and motion as well (for review, see Johnson & Iida, 2013; Johnson, Pollick, & McKay, 2010). For example, sex and age categorizations can be made with great accuracy from body shape and body motion. The former is perhaps no surprise given sexual dimorphism in body shapes, but the latter—that men and women move in sex-differentiated ways—is perhaps a more nuanced point. Work by Johnson and Tassinary (2005, 2007; see also Cutting, 1978; Johnson & Iida, 2013) investigated how body shape and body motion jointly influence sex categorization and judgments about masculinity and femininity. In this work, participants observed computer-animated human bodies that varied both in their sexually dimorphic shape (waist-to-hip ratio) and in their sex-typical body motion. Although participants’ male/female binary decisions relied heavily on body shape, judgments of targets’ masculinity and femininity relied both on sex-typical body shape and body motion, implicating both form and motion in categorization decisions. A person’s age, too, can be accurately extracted from their bodily movements. Point-light displays of youthful and aged walkers are easily categorized by age (Montepare & ZebrowitzMcArthur, 1998) and lead to stereotype consistent inferences; for example, walkers with youthful gaits are rated as more powerful and happier than walkers with older gaits. Perhaps more surprisingly, other social categories such as sexual orientation and race can also be extracted accurately from bodily cues and dynamic motions. For example, research (Johnson, Gill, Reichman, & Tassinary, 2007) demonstrated that combinations of sex-typical bodily shapes and bodily movements are used to make judgments that a target is homosexual. Specifically, a masculine body (i.e., high waist-to-hip ratio) with a sexatypical gait (i.e., hip sway) was often categorized as a gay man. Analogously, a feminine body (i.e., low waist-to-hip ratio) with a sex-atypical gait (i.e., shoulder swagger) was often categorized as a lesbian. Body shape, too, was used to categorize target women’s sexual orientation, with higher WHR women being categorized as gay more often than their low WHR counterparts. There is also recent evidence that race can be extracted from bodily movements. Lick, Golay, and Johnson (2014) found that point-light displays of Whites and Asians walking on a treadmill could be discriminated by race at better than chance accuracy. Much like past research on inferring sex or sexual orientation from faces and bodily motion, these categorization

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processes appear to rely on partially accurate group stereotypes about Asians having a more feminine gait than Whites (a “kernel of truth” in stereotypes of bodily motion). Taken together, although much of the existing research on the perceptual cues of categorization rely on facial cues, recent evidence indicates that our ability to extract important intergroup distinctions can be surprisingly accurate from bodily cues, including body shape, how a body moves through space, and the interaction of these factors. 1.1.2.5 Mutually Constrained Categories: Shared Perceptual Cues Can Influence Categorization

Finally, recent research indicates certain social categories actually share perceptual cues, and the shared nature of these perceptual cues makes these categories mutually constraining—the presence of cues of one category makes the stimulus appear to also have the presence of the other category. For example, two longstanding gender stereotypes are that men express anger more than women (Fabes & Martin, 1991) and that women smile more than men (LaFrance, Hecht, & Paluck, 2003). Although there have been a variety of explanations for this phenomenon, including social norms (e.g., LaFrance et al., 2003), social role expectations (Brody & Hall, 2000), and power (Hall & Halberstadt, 1994), it appears that this effect is at least partially mediated by sexually dimorphic facial structures (see Adams, Hess, & Kleck, 2015, for a review). The facial features that lead to the perception of facial dominance are more typical for men’s faces than for women’s faces (Becker, Kenrick, Neuberg, Blackwell, & Smith, 2007; Hess, Adams, & Kleck, 2004, 2005). For example, men have a more squared jaw, thicker eyebrows, and more prominent brow ridge than do women, all of which are signals of facial dominance. Conversely, women are more likely to have rounder and more neotonous faces, which are facial signals of warmth (Berry & McArthur, 1986). Importantly, these facial structures that signal dominance and warmth are also the same features central to signaling anger and happiness, respectively. This bottom-up perceptual overlap between sex-typical facial features and expressions has important consequences for categorization. For example, expressions of anger are detected more easily on men’s than on women’s faces, and conversely, expressions of happiness are detected more easily on women’s than on men’s faces, which is true both for posed expressions of actors and for expressions on computer-generated faces (Becker et al., 2007). Similarly, with images of neutral expression, men are often

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miscategorized as angry, whereas women are often miscategorized as happy. In our own work, we have found similar confounding between bottom-up cues of sex and facial expression as well. For example, we have found that faces with babyish features (e.g., large eyes) more efficiently signal submissive expressions such as fear and less efficiently signal dominant expressions such as anger (Sacco & Hugenberg, 2009). More recently, we have extended these effects to the specific sexually dimorphic facial cue of fWHR. Even holding target sex constant, faces with a higher fWHR more efficiently communicated anger and less effectively signaled fear (Deska et al., under review-b). Research by Zebrowitz, Kikuchi, and Fellous (2010) has demonstrated that race, too, has perceptual overlap with some expressions. In this research, Zebrowitz and colleagues trained a connectionist model to distinguish between facial expressions, and then let the model attempt to distinguish between neutral expressions on White, Black, and Asian faces. Of interest was the type of errors the model made. If the model mistook a particular race of face for a particular expression, this error would be strong evidence of perceptual overlap between a race category and an expression. Zebrowitz and colleagues found that whereas White faces objectively resemble angry expressions more than Black or Korean faces, Black faces objectively resemble happy and surprise expressions more than White faces. This may seem surprising given the American cultural stereotype linking Blacks to aggression, and the multiple empirical demonstrations showing a Black-anger link (e.g., Hugenberg & Bodenhausen, 2003, 2004; Hutchings & Haddock, 2008; Kang & Chasteen, 2009; Kubota & Ito, 2014). However, in light of the power of top-down effects in categorization (see later), it is likely that in spite of the objective similarity of Black faces and happiness, stereotypes of African Americans create the illusion of anger on Black faces, even when it is not present. Similarly, bottom-up cues of race may also be perceptually confounded with bottom-up cues of sex. Put simply, race is gendered. Johnson, Freeman, and Pauker (2012) demonstrated that sex categorization of faces is facilitated when the race and sex category share phenotypic cues (Asian women; Black men) and inhibited when the race and sex category have incompatible cues (Asian men; Black women). Further, this occurs in part because of the perceptual overlap between different racial groups and sex. In their data, Johnson and colleagues found that Black faces were more masculine according to objective face measurements than Asian or White faces. Similarly, Asian faces were objectively more feminine than Black faces.

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Taken together, there is strong and accumulating evidence that the way in which we construe others, even in the earliest stages of person perception, can have powerful consequences for categorization and behavior. Indeed, if intergroup relations begin with categorization, these initial categorization processes are critical in determining who is “us” and who is “them.”

1.2 Perceiving Persons and Groups From the “Top-Down” To this point, we have focused on bottom-up aspects of person perception—that is, the ways in which we determine a person’s social group memberships from his or her physical features alone. However, a long history of social psychological research points to the role of top-down effects, such as expectancies, motivations, prejudice, and prior knowledge, in influencing our perceptions and judgments of people (Allport, 1954; Brewer, 1988; Fiske & Neuberg, 1990; Kunda & Thagard, 1996). Furthermore, recent evidence suggests that these top-down influences may even shape the early visual processing of faces such that motivation and cognition may interact with bottom-up signals to shape our perceptions of people based on their group membership (Bernstein, Young, Brown, Sacco, & Claypool, 2008; Ofan et al., 2011; Ratner & Amodio, 2013; Van Bavel, Packer, & Cunningham, 2011). In this section we describe research that has challenged pure bottom-up models of face processing to suggest that our social goals and knowledge might also shape how we see people. 1.2.1 Group-Based Influences on Visual Processing Whereas the top-down effects of intergroup factors on social cognition are well known, scientists have only recently begun to ask whether these factors can also affect our visual perceptions. Mounting evidence supports the idea that social and motivational factors can alter aspects of visual processing such as the size of, or distance to, a target (Balcetis & Dunning, 2006; Bruner & Postman, 1949; Dunning & Balcetis, 2013; Proffitt, Stefanucci, Banton, & Epstein, 2003). Findings such as these have inspired the recent social vision movement in person perception (Adams, Ambady, Nakayama, & Shimojo, 2010; Balcetis & Lassiter, 2010; Freeman & Johnson, 2016), along with a new focus on the motivated perception of race (Amodio, 2010). Research in this area investigates whether social and motivational factors can alter the earliest perceptual processes such that it changes the way we actually see someone. There is currently much debate on whether top-down factors such as social identity, motives, or attitudes can influence visual perception per se

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(i.e., we actually see the stimuli differently), as opposed to cognitions and behaviors that contribute to perceptual judgments (i.e., we judge the stimuli differently; see Deska, Lloyd, & Hugenberg, 2016; Firestone & Scholl, in press; Xiao, Coppin, & Van Bavel, 2016). Thus, obtaining clear evidence for top-down intergroup effects on visual processing has been a major challenge. Researchers have approached this issue with methods from visual psychophysics and neuroscience to assess low-level components of face processing. These studies have generally addressed two broad questions: do social factors influence the initial configural encoding of a face? And to what extent do social factors affect our perception of facial features and expressions? There is abundant research demonstrating the own group bias (OGB)— the pervasive effect whereby face recognition is worse for outgroup relative to ingroup members. An early fMRI study by Golby, Gabrieli, Chiao, and Eberhardt (2001) addressed whether the OGB, as observed with White American participants, was associated with differences in low-level face processing, as indicated by activity in the fusiform cortex. Participants viewed a series of Black and White faces and, as in prior research, showed better recognition memory for ingroup White faces than outgroup Black faces. Moreover, participants exhibited stronger activity in the fusiform cortex when viewing White faces than Black faces, suggesting that the OGB may be due, in part, to reduced visual processing of outgroup faces (beyond differences in attention). Subsequent research explored the possibility that group membership affects the configural processing of a face—that is, the initial encoding of an object as a human face. Research by Michel and colleagues (Michel, Corneille, & Rossion, 2007; Michel et al., 2006) borrowed methods from visual psychophysics to test whether configural face processing is impaired for outgroup faces. One method makes use of the face composite effect. When the top half of one face is paired with the bottom halves of two different faces, to create novel face stimuli, participants typically perceive the (identical) top halves of each pairing to represent different identities. However, if the top and bottom faces are offset even slightly, the top halves are perceived to be the same person. The explanation for this effect is that when faces are perfectly aligned, the mind processes the face configurally as a whole, and so the top half is perceived in the context of the bottom half. When the faces are offset, configural processing is disrupted. Because the perceiver then relies on featural processing, the separate identities of the top and bottom halves can be distinguished. These researchers found that the split face illusion

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occurred more strongly for ingroup White faces than outgroup Asian faces, suggesting a reduced tendency to process Asian outgroup faces configurally. Although these effects have been attributed to the potential motivational effects of intergroup contexts or the differential familiarity with ingroup and outgroup faces, it turns out that both factors play an important role in the differential processing of ingroup and outgroup faces (see Hugenberg et al., 2010, for a review), and both provide initial evidence for top-down effects of group membership on face perception. 1.2.2 Novel Group Effects on Face Encoding Processes To more directly assess whether group membership can have top-down effects on how we perceive faces, researchers have turned to neuroimaging methods, such as ERP and fMRI, to assess patterns of neural activity associated with early stages of face processing. The N170 component of the ERP, in particular, provides a relatively precise index of early configural face processing. Because the N170 occurs at approximately 170 ms after face onset, it is believed to represent the precise moment when an object is encoded as a human face. Because this effect occurs so quickly, and in the occipitotemporal cortex, it is assumed to represent an implicit and automatic process. Thus, group membership effects on the N170 constitute strong evidence of top-down social category effects in vision. Early investigations of race effects on ERP responses were promising but produced somewhat mixed results. Whereas some studies observed no differences (Caldara, Rossion, Bovet, & Hauert, 2004; Caldara et al., 2003; He, Johnson, Dovidio, & McCarthy, 2009; Wiese, Stahl, & Schweinberger, 2009), others observed larger N170 effects for the ingroup (Ito & Urland, 2005), and still others observed larger effects for the outgroup (Walker, Silvert, Hewstone, & Nobre, 2008). These inconsistencies appear to be due to differences in the experimental tasks employed across the various studies, which in turn may have led participants to approach the presented faces differently. For example, tasks that involve the categorization of race (or gender) may focus participants on categorical differences (e.g., Ito & Urland, 2005), whereas memory tasks (e.g., n-back tasks) do not (Walker et al., 2008). In addition, early N170 studies of race perception used either full color or grayscale pictures that did not control low-level visual factors, such as luminance and contrast, which could also influence N170 responses, especially for White and Black target faces. These issues were dealt with in two ways: by using more rigorously controlled visual stimuli and by examining patterns

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of N170 response as a function of theoretically meaningful psychological variables, such as individual differences and situational factors. In one such example, fMRI research on novel group effects revealed stronger fusiform activity in response to ingroup than outgroup members’ faces (Van Bavel et al., 2011), which suggested the possibility that group membership may influence the perceptual processing of faces. In order to more precisely determine whether mere group membership can affect the perceiver’s ability to configurally encode a face, we (Ratner & Amodio, 2013) used the classic minimal group paradigm to create novel group identities in the lab (Tajfel, Billig, Bundy, & Flament, 1971). Using this procedure, participants were introduced to a novel social distinction—in this case, a bogus trait called numerical estimation style, whereby some people purportedly overestimate the number of objects in their visual field and others underestimate this number. After completing a test to ostensibly assess their own numerical estimation style, participants completed a task in which their goal was to identify another person’s numerical estimation style from facial appearance alone. To “help them out,” the background color in the image indicated the group membership. We were able to do this because the N170 is not sensitive to background color but only to facial configuration. Thus, while viewing faces of college-aged White males during EEG recording, participants indicated whether the person was more likely to be an underestimator or overestimator. Not surprisingly, participants nearly always made judgments consistent with the background color. Importantly, we observed significantly larger N170 amplitudes in response to novel ingroup faces than outgroup faces, indicating that even this very minimal social categorization was sufficient to influence the initial encoding of faces. That is, ingroup faces were perceived as more face-like than outgroup faces in the brain, suggesting that social categories can penetrate the earliest stages of face processing. Moreover, because all face images were of young White males and the group distinction was arbitrary, the effect could not be explained by low-level perceptual features. If mere group membership alters the perception of faces, how might these differences appear to the perceiver? In an attempt to answer this question, Ratner, Dotsch, Wigboldus, van Knippenberg, and Amodio (2014) used a reverse correlation image classification method to visualize participants’ spontaneous mental images of minimal ingroup and outgroup faces. This technique was borrowed from Dotsch, Wigboldus, Langner, and van Knippenberg (2008; see also Mangini & Biederman, 2004), who demonstrated its ability to reveal participants’ mental images of a variety of existing

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social groups (e.g., African American, Dutch, and Chinese faces). In our study (Ratner et al., 2014), participants were first induced to identify with the novel group of either overestimators or underestimators. Immediately following this induction, participants were instructed to complete a face categorization task in which they were presented with a pair of faces. Half of the participants decided which of the two was an overestimator; the other half decided which was an underestimator. Importantly, these faces were created by superimposing quasi-random visual noise onto a single base face image (see Fig. 4). This noise created subtle distortions that made each face seem slightly different, and the assumption was that participants would choose the face in each pair that more closely matched the image of an overestimator (or underestimator) in their minds. The selected facial images across 400 trials were averaged into a composite image such that the average noise patterns would reveal an approximation of participants’ mental image of either an ingroup or an outgroup face. When these composite images of ingroup and outgroup faces were presented to a new participant sample, naı¨ve to their origin, the ingroup face was judged to appear significantly more attractive, intelligent, and trustworthy than the outgroup face. Moreover, subsequent studies showed that ingroup face images produced more implicit positive attitudes and elicited greater trust behavior relative to outgroup faces. In an additional study, participants classified face pairs according to which appeared more trustworthy, and pixel-by-pixel comparisons revealed that the trustworthy composite face was highly correlated with the ingroup face but not with the outgroup Base image + noise

Noise pattern

+

=

Noise pattern (inverse)

Base image

+

Base image + noise (inverse)

=

Fig. 4 Stimuli creation in a reverse correlation paradigm.

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Ingroup

Outgroup

Trustworthy

Fig. 5 Face representations rendered by reverse correlations (Ratner et al., 2014).

face (see Fig. 5). Interestingly, these effects were primarily driven by similarity in the eyes rather than the nose or mouth—a finding consistent with evidence that group membership affects attention to the eyes of ingroup vs outgroup members (Kawakami et al., 2014). Although the reverse correlation method cannot provide a direct readout of a person’s perceptual experience, it offers a clue to how, in visual terms, group membership may influence the perception of faces in a top-down fashion. Whereas the configural processing of ingroup member faces is enhanced in minimal group contexts, interracial contexts are more complex. To the extent that the outgroup is considered a threat, outgroup faces may receive enhanced attention and visual processing. By contrast, to the extent that the outgroup is considered irrelevant or objectified, it may receive reduced attention and visual processing. In order to study differences in the early visual processing of White and Black faces, we (Ofan et al., 2011) created two-tone face stimuli, in which the images were composed of only white and black pixels, and the proportion of black to white pixels was equated across faces stimuli. Using these highly controlled stimuli, we found larger N170 responses to Black faces among White participants with high implicit prejudice (Ofan et al., 2011) when they felt anxious about revealing prejudices to others (Ofan, Rubin, & Amodio, 2014) and when they were induced to experience feelings of high power (Schmid & Amodio, 2016). In each of these cases, early visual encoding of the racial outgroup was enhanced in the context of outgroup threat. Other research has examined the visual processing of racial outgroup faces in contexts where they may be objectified. Much prior evidence shows that resource scarcity and competition increases prejudice (Butz & Yogeeswaran, 2011; Esses, Jackson, & Armstrong, 1998; King, Knight, & Hebl, 2010; Quillian, 1995; Stephan et al., 2002; Stephan, Renfro, Esses, Stephan, & Martin, 2005; Stephan, Ybarra, & Bachman, 1999) and discrimination (Brewer & Silver, 1978; LeVine & Campbell, 1972;

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Sherif & Sherif, 1953; Taylor, Kochhar, & Fry, 2011) toward the outgroup. In a series of studies, we (Krosch & Amodio, 2014, under review) tested whether perceptions of scarcity lead White Americans to view Black faces in ways that might somehow justify their worse treatment. In an ERP study, participants completed a resource allocation task, in which they decided how much money to give Black and White recipients. Participants were led to believe that the amount of money available for their allocations was scarce or abundant. We found that scarcity produced a selective delay in the N170 response for Black faces compared with White faces, relative to the control condition—a pattern of impaired configural processing typically observed for inverted faces that suggests a dehumanized perception (Hugenberg et al., 2016; Rossion et al., 2000). Moreover, mediation analysis showed that the degree of this effect predicted the extent to which perceived scarcity caused anti-Black disparities in participants’ money allocations. In other words, scarcity induced the dehumanized perception of Blacks, which was in turn related to worse treatment. We further probed this pattern of visual dehumanization under scarcity in an fMRI study. Using the same experimental task, we (Krosch & Amodio, under review) found that scarcity produced a selective decrease in fusiform cortex activity to Black faces but not Whites faces. Moreover, this reduction in fusiform activity was linked to decreased activity in the striatum—a neural structure associated with reward and valuation—which in turn mediated the effect of the fusiform activity on anti-Black money allocations. In other words, the visual dehumanization effect for Black faces under scarcity was associated with decreased reward processing, which then predicted reduced allocations. In related work, we (Krosch & Amodio, 2014) proposed that perceived resource scarcity may lead people to see Blacks as “Blacker” and more “stereotypical,” which may facilitate the tendency to discriminate. This proposal builds on research showing that Black people with darker skin tone and more stereotypical (e.g., Afrocentric) features are subjected to greater racism (Eberhardt, Dasgupta, & Banaszynski, 2003; Eberhardt et al., 2006; Maddox, 2004), as well as research showing that cues to a biracial person’s high or low status influences whether they are categorized as Black or White (Freeman, Penner, Saperstein, Scheutz, & Ambady, 2011). We (Krosch & Amodio, 2014) found that perceived scarcity reduced participants’ threshold for deciding that a mixed-race face was Black, as opposed to White. Furthermore, reverse correlation methods revealed that scarcity led participants to visualize Black faces as appearing more stereotypically Black and darker in

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skin tone, which in turn predicted lower cash allocations to Black than White recipients. That is, scarcity led people to view Black faces as “Blacker.” Together, these studies reveal that perceived economic scarcity had a top-down influence on visual representations of Black people in a manner that propagated discrimination. Recent research by Fincher and Tetlock (2016) has also demonstrated a top-down link between dehumanization and reduced configural processing in the context of norm violation within a group. Their core hypothesis was that the faces of norm violators—people who broke the social contract—would be processed less configurally than norm followers. To test this hypothesis, these researchers manipulated the extent to which targets were seen as immoral norm violators (e.g., individuals who stole money) or moral actors (e.g., individuals who donated money). They then measured the extent to which the faces of these immoral and moral individuals were processed configurally using multiple measurement paradigms. Across the studies, the authors found that norm violators were processed less configurally than were norm followers. Importantly, this failure to process the faces of norm violators configurally had downstream consequences for social judgment. Processing a face configurally—as we normally process our fellow humans—reduced the drive to punish perpetrators. Just as we (Krosch & Amodio, under review) found that reduced configural processing of Black faces was related to lower money allocation, Fincher and Tetlock (2016) found that it was easier to punish faces that were processed in perceptually different ways than typical human faces in the context of norm violations. It is interesting to note that, in our studies, intergroup bias in visual processing is typically related to implicit attitudes and motivations to promote the ingroup’s interests, even in cases where a perceiver may consciously endorse egalitarianism. In the case of intergroup bias (Krosch & Amodio, 2014; Ratner et al., 2014), it appears that participants’ implicit motivation is to discriminate, and perceptual processes serve to facilitate this motive. In the context of within-group norm violations (Fincher & Tetlock, 2016), the motivation is to maintain pro-ingroup benefits through punishment (Mendoza, Lane, & Amodio, 2014). Because visual perception is a largely implicit process, it appears that group-based effects are especially conducive to implicit attitudes and motives (Dovidio, Gaertner, Kawakami, & Hodson, 2002; Dovidio et al., 1997). Indeed, because attentional preferences are difficult for a perceiver to detect, implicit biases expressed through a visual pathway may be resistant to self-regulation and thus particularly pernicious.

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1.2.3 Top-Down Effects on Body Perception Most research on intergroup perception has focused on the way we process faces. However, group membership and intergroup attitudes may also influence our perception of bodies. For example, researchers have used an effect known as the rubber hand illusion to examine the tendency to feel bodily ownership over limbs that are not our own (Botvinick & Cohen, 1998). To create the rubber hand illusion, a realistic-looking rubber hand is placed on the table in front of a participant, with his/her real hand off to the side and out of sight. Tactile stimulation (e.g., a small paintbrush stroke) is applied to the real and fake hands in synchrony, which typically causes the participant to experience the rubber hand as his/her own. Farmer, Tajadura-Jimenez, and Tsakiris (2012) examined whether the skin color of the rubber hand influenced the experience of body ownership. Indeed, they found that White participants reported a reduced illusion experience when the rubber hand was dark skinned rather than light skinned (i.e., Caucasian). Interestingly, the degree of this effect was stronger for participants with greater implicit anti-Black prejudice (Maister, Sebanz, Knoblich, & Tsakiris, 2013), suggesting a top-down effect on body perception based on racial attitudes. These findings provide additional evidence for the top-down effect of racial group membership on perception. Moreover, they suggest new ideas for prejudice reduction interventions. For example, the experience of being represented by a Black avatar in a virtual reality environment may reduce implicit bias (Peck, Seinfeld, Aglioti, & Slater, 2013). Recently, we have also explored the relation between stereotypes related to Blacks and aggression and perception of the size of African Americans’ bodies (Wilson, Rule, & Hugenberg, under review). Across multiple studies, we found that both White and Black perceivers believe that Black compared to White men are larger (taller, heavier, more muscular), and that this belief distorted judgments of the bodies of Black men. This is true even when Black and White targets are matched for physical size (i.e., actual height and weight) and upper body strength (e.g., max bench press strength). Indeed, even the same physical body (color inverted to disguise target race) when paired with a name typical of Blacks is rated as larger and more physically formidable than when paired with a name typical of Whites. Whereas both Whites and Black perceivers rate Black targets as larger, only White perceivers experienced the larger Black male as threatening or potentially harmful. Thus, whereas stereotypes about Blacks’ physicality seem to affect body perceptions for both White and Black perceivers, these stereotypes translate into threat differently across perceiver race. Furthermore, this

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experienced threat has important downstream consequences. The tendency for Whites to perceive Black male physiques as large and muscular facilitates Whites’ justification for the use of force against Black suspects of crimes. When Whites are asked whether using force is necessary to restrain crime suspects, the White participants believe that the Black (relative to White) targets are larger, and that force is more justified to restrain Black (relative to White) targets. Importantly, this size bias for Black targets partially mediated the greater likelihood related to a need for force to detain Black relative to White targets. 1.2.4 Top-Down Influences on Face Categorization and Memory There is now ample evidence for the influence of perceivers’ goals, emotions, and stereotypes on how they categorize faces of ingroup vs outgroup members. Research on race categorization, for example, has examined factors that influence perceivers’ classification of a mixed-race face as being of one race or another. Much of this work has examined North American participants’ classifications of faces as White or Black. Across studies, there is a general tendency to show a pattern of hypodescent—the policy of assigning multiracial individuals to their lowest status group—such that biracial faces are more likely to be classified as Black than White (Halberstadt, Sherman, & Sherman, 2011; Ho, Roberts, & Gelman, 2015; Ho, Sidanius, Cuddy, & Banaji, 2013; Ho, Sidanius, Levin, & Banaji, 2011; Krosch & Amodio, 2014; Krosch, Berntsen, Amodio, Jost, & Bavel, 2013; Peery & Bodenhausen, 2008). Early work on this effect showed that racial markers, such as hairstyle, influenced the racial categorization of specific facial features (e.g., noses, eyes, and mouths) which then determined their classification (MacLin & Malpass, 2001), and the presentation of a race label along with a biracial face has been shown to influence the racial categorization and subsequent memory of the face (Eberhardt et al., 2003). Contemporary theories have also emphasized the role of attention to outgroup characteristics in this effect such that White participants’ strategic attention to outgroup racial markers biased them toward classifying biracial faces as Black more often than White (Halberstadt et al., 2011). However, recent findings suggest that an individual’s prejudices, motivations, and ideologies can influence race categorization beyond purely attentional effects. For example, Ho et al. (2011) demonstrated that hypodescent in race categorization occurs even in the absence of visual cues, such as when participants judged a target individual with two Black and two White grandparents as more likely to be Black than White. This bias is

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increased when targets exhibited more hostile expressions and was attenuated when targets show happier expressions—an effect linked to the stereotype of African Americans as hostile—and this effect is associated with an independent measure of the perceivers’ implicit prejudice (Hugenberg & Bodenhausen, 2004). Other research has shown that the tendency to categorize biracial faces as Black is enhanced under conditions of economic scarcity (Krosch & Amodio, 2014; Rodeheffer, Hill, & Lord, 2012) and associated with stronger right-wing ideology and social dominance orientation (Ho et al., 2013; Krosch et al., 2013). These patterns have been established by examining the simple frequencies of biracial face classifications, as well as with methods for assessing perceptual thresholds (e.g., assessments of point of subjective equality) adapted from visual psychophysics (Krosch & Amodio, 2014; Krosch et al., 2013). Top-down effects on the processing of race are also evident in the previously mentioned OGB, in which there is better recognition of ingroup than outgroup faces. Originally studied in the context of racial ingroup and outgroup members (Meissner & Brigham, 2001; Sporer, 2001), this bias has been observed for a variety of group identities, including religion (Rule et al., 2010b), sexual orientation (Rule et al., 2007), political affiliation (Ray, Way, & Hamilton, 2010), social class (Shriver, Young, Hugenberg, Bernstein, & Lanter, 2008), and even seemingly arbitrary group memberships such as university affiliation and personality types (Bernstein, Young, & Hugenberg, 2007; Van Bavel & Cunningham, 2012). Much of the interest in this phenomenon stems from its potential effects in cross-race errors in eyewitness identifications, which account for a disproportionately high number of wrongful convictions (Scheck, Neufeld, & Dwyer, 2000; Wilson, See, Bernstein, Hugenberg, & Chartier, 2014). Whereas early research hypothesized that these intergroup effects may be due to prejudice, more recent metaanalytic data indicate that prejudice does not fully account for the OGB (Meissner & Brigham, 2001). Instead, in the categorization–individuation model, we have proposed that the OGB is caused by reduced motivation to individuate the faces of outgroup members (Hugenberg et al., 2010; see also Hugenberg, Miller, & Claypool, 2007; Hugenberg, Wilson, See, & Young, 2013; Pauker et al., 2009; Van Bavel, Swencionis, O’Connor, & Cunningham, 2012). Indeed, in many intergroup situations, seeing outgroups as relatively homogeneous entities are a commonplace default method of processing social targets (Ostrom & Sedikides, 1992). However, ingroup memberships can serve as a cue for who is self-relevant and worthy of attention and processing

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(Correll & Park, 2005). Unless someone has a motivation to move beyond the simple perceptual cues of outgroup faces, these faces are unlikely to receive extensive processing (Ratner & Amodio, 2013). Importantly, some of our recent work has demonstrated just how this ingroup/outgroup distinction can translate into differential perception of homogeneity. Put simply, the eyes have it. Specifically, Kawakami et al. (2014) found across four studies that perceivers differentially attend to the eyes of ingroup and outgroup members based on racial categories (Whites attend more to the eyes of White faces than Black faces), as well as arbitrary categories (targets believed to share personality types). Importantly, this tendency to favor ingroup eyes when encoding faces has consequences: when spontaneous attention to the eyes was manipulated, greater attention reduced OGB effects and increased willingness to interact with outgroup members (Kawakami et al., 2014; Kawakami, Williams, et al., under review). Considering the OGB as the confluence of individuation experience and individuation motivation provides an important window into how intergroup motives can exacerbate or reduce the OGB. In the most straightforward way, the OGB can be eliminated when perceivers are informed about the existence of the OGB and instructed to attend to features that differentiate category members (Hugenberg et al., 2007; Rhodes, Locke, Ewing, & Evangelista, 2009; Young, Bernstein, & Hugenberg, 2010). This motivation to individuate can also come from reward structures in the task or environment itself: paying participants for superior face memory (Kawakami et al., 2014) or rewarding participants with points (at least under some conditions) can also generate sufficient individuation motivation to attenuate the OGB (DeLozier & Rhodes, 2015). This motivation can also arise from changing the apparent category affiliation of the targets. For example, a number of recent studies have demonstrated that even when the same faces are categorized as ingroup or outgroup members, face memory is influenced according to category (Bernstein et al., 2007; MacLin & Malpass, 2001; Pauker et al., 2009; Young & Hugenberg, 2012). An increased motivation to individuate outgroup faces can also come from the relationship between the self and the outgroup members. For example, perceived interpersonal similarity can reduce the OGB, as can status and power. For example, our research has demonstrated a linear effect in which Black targets that were ostensibly more similar to participants based on a personality test were subsequently better recognized than less similar targets (Kawakami, Williams, et al., under review). Furthermore, Shriver

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and Hugenberg (2010) found that racial outgroup members who were in a high status role (e.g., doctor, CEO) or were engaged in behaviors that signified power (e.g., physical threat, demonstrating wealth) were remembered as well as ingroup members. However, low status or low power outgroup members were quite poorly recognized. Similarly, when perceivers were outcome dependent on outgroup members or when they believed that an interaction with outgroup members may be impending, the OGB was eliminated (Baldwin, Keefer, Gravelin, & Biernat, 2013). These findings are consistent with evidence for enhanced neural encoding of Black relative to White faces when participants were induced to worry about appearing prejudiced (Ofan et al., 2014). Perhaps most provocatively, the presence or absence of the OGB can be dictated entirely by the relationship between the ingroup and the outgroup. For example, Van Bavel and Cunningham (2012) demonstrated that the structure of the intergroup context can determine whether an OGB occurs. In their research, participants’ roles were manipulated within groups. Here, participants were randomly assigned to mixed-race groups (the “Moons” and the “Suns”), but were told either that they were “soldiers” who would “remain loyal to the Moons [Suns]” and that their goal would be “to serve the needs of” the ingroup or that they were “spies” who would “remain loyal to the Moons [Suns]” but that their goal would be to “infiltrate” the outgroup. Whereas “soldiers” showed the typical OGB, “spies” showed strong recognition for both the ingroup and outgroup faces. In other words, in a situation structured to make outgroup members functionally interchangeable (soldiers), an OGB was observed, but where one’s role demanded individuation of both the ingroup and the outgroup members (spies), the OGB was eliminated. Put simply, when the intergroup context makes outgroup members less relevant, we fail to individuate them, but when the intergroup context makes outgroup members self-relevant, individuation can occur. Taken together, although the exact relation between individuation experience and individuation motivation is still a matter of some debate (Hugenberg et al., 2013), it is clear that OGBs are driven to a great extent by intergroup motives. Merely categorizing targets as ingroup and outgroup members is sufficient to generate OGBs, recategorizing former outgroup members as ingroup members is sufficient to eliminate OGBs, and being motivated to overcome outgroup homogeneity can overcome OGBs. Top-down intergroup motives play a powerful role in who we remember and who we forget.

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2. PERSONS CONSTRUED Once person construal has stabilized, the categorization of a target individual into a social group has a host of downstream consequences. These consequences include the activation of corresponding knowledge structures related to identification, stereotypes, and attitudes/affect. The importance of these associations to intergroup relations is undeniable (Dovidio, Gaertner, et al., 2002; Dovidio, Kawakami, et al., 2002; Dovidio et al., 1997; Greenwald, Smith, Sriram, Bar-Anan, & Nosek, 2009). Once activated, these processes have implications for a range of social judgments, including emotion identification, empathy for others, and a variety of behaviors. Given the potential for these processes to influence intergroup interactions without our intention, research has also examined mechanisms involved in the control and potential reduction of this category-based knowledge.

2.1 Activation of Category-Based Knowledge Once a person is construed as a member of a particular social group, that person is imbued with a wealth of category-based information. This information includes associations with the self (identification), group characteristics (stereotypes), and evaluations (prejudice). For example, if a person is initially categorized as Asian, White perceivers may be unlikely to activate associations with the self, and likely to activate schemas related to Asian stereotypes such as being terrible drivers, cheap, and math smart, as well as overall negative evaluations of both the person and of Asians in general. Although these three types of associations are typically considered to be distinct and studied in isolation, research has recently started to investigate the relations between these constructs. 2.1.1 Implicit Identification: Associations Between the Self and Social Categories How we conceive of the self determines how we understand, perceive, and interact with our social environment (Kihlstrom & Cantor, 1984; Markus & Kunda, 1986). The self is especially important in our relationships with outgroup members. Although we typically think of identification as associations between the self and a particular ingroup (Luhtanen & Crocker, 1992), identifying with groups of which we are not members has important implications for intergroup relations (Allport, 1954; Shteynberg & Galinsky, 2011; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987; Walton, Cohen,

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Cwir, & Spencer, 2012). One of the most basic forms of bias is to perceive outgroup members as essentially or deeply different from the self. Whether we believe that members of other groups have different physical or personality characteristics, different cultural practices, different goals or values, or whether we simply do not associate outgroups with the self, this lack of correspondence between “us” and “them” can have important consequences: disidentification can induce negative attitudes, promote destructive intergroup behaviors, and decrease support for interventions that can reduce discrimination. We (Phills, Kawakami, Tabi, Nadolny, & Inzlicht, 2011) have found that, in general, people consider themselves distinct from outgroup categories and typically do not associate the self with a variety of outgroups. For example, in a series of experiments, we utilized two types of Implicit Association Tests (IATs, Greenwald et al., 2002; Greenwald & Farnham, 2000) and a psychophysiological measure of brain activation to assess outgroup identification. In the first study, an IAT measure of self-associations with White and Black faces revealed that non-Black participants implicitly associated the self more strongly with Whites than Blacks. A second study extended this effect to show that non-Black participants more strongly associated self-descriptive traits with White compared with Black faces. In an additional experiment, we (Phills, Kawakami, et al., 2011) measured the extent to which participants perceived themselves to be distinct from Blacks by measuring electrophysiological brain activity during an oddball task. In this paradigm, participants were presented with a series of photographs, with the majority of images related to the self. Within this context, participants were also presented with oddball stimuli consisting of photographs different from the self (i.e., Blacks or Whites). While participants were categorizing each image as “me” or “not me,” the amplitude of a stimulus-locked ERP component, the P300, was monitored. As expected, based on previous research (Ito & Urland, 2003), non-Black participants responded to Blacks as psychologically more different from the self than Whites by exhibiting a larger P300 response to Black than White faces in the context of self-categorizations. In fMRI research by Mitchell, Macrae, and Banaji (2006), participants made judgments about the self, political ingroup members, and political outgroup members while their brain activity was recorded. The authors found that judgments of ingroup members and the self activated a similar region in the ventral mPFC, whereas judgments of outgroup members activated a

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different, more dorsal area of the mPFC. This finding suggests that, at least in part, different regions of the brain may be used for processing ingroup and outgroup members. Thus, “I” and “we” may be fundamentally different than “them,” even very early in the processing stream. 2.1.2 Implicit Stereotypes: Associations Between Specific Characteristics and Social Categories Stereotypes are characteristics that we associate with people in a social category (Amodio, 2014a; Fiske, 1998). These characteristics include personality traits (e.g., fun, unassertive) and physical features (e.g., dark skin, long hair), as well as beliefs about behaviors (e.g., bad drivers, slow), emotions (e.g., happy, fearful), and life circumstances (e.g., poor, well educated). These characteristics may be positive, negative, or neutral (Esses, Haddock, & Zanna, 1993). An abundance of research has demonstrated that exposure to a category representation, whether it be an actual ingroup or outgroup member, a photograph, or a category label, is sufficient to activate associated conceptual characteristics (Blair, 2002; Blair & Banaji, 1996; Kawakami & Dovidio, 2001; Macrae, Bodenhausen, & Milne, 1995). For example, in her seminal research, Devine (1989) demonstrated that when participants were subliminally primed with characteristics and labels related to Blacks, they spontaneously activated the concept of aggression and evaluated an unrelated target as more hostile. Likewise, we have found evidence for the spontaneous activation of stereotypes associated with a variety of categories including Blacks, the elderly, women, and skinheads (Kawakami, Dion, & Dovidio, 1998; Kawakami & Dovidio, 2001; Kawakami, Dovidio, Moll, Hermsen, & Russin, 2000; Kawakami, Young, & Dovidio, 2002). These studies and others have used multiple methods to measure implicit stereotypes, as well as implicit prejudice, including the pronunciation task, Stroop task, person categorization task, (primed) LDTs, IAT, Extrinsic Affective Simon Task (EAST), sequential priming task, evaluative priming task, and the affect misattribution procedure (Amodio & Devine, 2006; Amodio & Hamilton, 2012; Donders, Correll, & Wittenbrink, 2008; Fazio, Sanbonmatsu, Powell, & Kardes, 1986; Greenwald & Banaji, 1995; Greenwald et al., 2002; Payne & Lundberg, 2014; Rudman, Ashmore, & Gary, 2001; Wittenbrink, Judd, & Park, 2001). Stereotypes are assumed to be represented in memory, though traditional models of stereotyping have not distinguished the specific forms of memory underlying stereotypes or their implications for judgment and behavior. To better understand how intergroup biases function and influence behavior,

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we have proposed a memory systems model, whereby different aspects of intergroup processes are supported by different systems of learning and memory, such as semantic, instrumental, and Pavlovian associations, among others (Amodio, 2008; Amodio & Devine, 2006, 2008; Amodio et al., 2003). According to this model, stereotypes are rooted in mechanisms of semantic memory and selection that are underpinned in the brain by the temporal lobe and PFC, respectively. By linking stereotypes to this more specific memory process, researchers can apply findings from the memory literature to derive more precise predictions for how stereotypes are formed, expressed, and potentially changed (Amodio & Ratner, 2011). For example, whereas affective associations are learned quickly and are difficult to extinguish, semantic associations may be learned and unlearned through a process of repeated pairings and nonpairings. Moreover, as compared with affective associations, semantic associations are more likely to be expressed in trait impressions, goal representations, and goal-directed behaviors, and are also more likely to emerge in verbal responses (Amodio & Devine, 2006; Amodio & Mendoza, 2010; Amodio & Ratner, 2011). More recently, fMRI studies of social stereotypes have also begun to illuminate the key neural substrates (Amodio, 2014a). For example, the anterior temporal lobe (ATL; i.e., the temporal poles) has been shown to represent knowledge about people and social groups (Olson, McCoy, Klobusicky, & Ross, 2013). The dorsal part of the ATL, which is implicated more specifically in the representation of social objects (i.e., people), is densely interconnected with the regions of the mPFC that are associated with trait judgment and impression formation. This suggests that social information represented in the ATL is selected into the mPFC to support the process of social cognition. Not surprisingly, the ATL is consistently implicated in studies of stereotype representation. In one fMRI study we (Gilbert, Swencionis, & Amodio, 2012) used multivoxel pattern analysis (MVPA) to examine neural activity representing judgments of Black and White individuals on the basis of stereotypic traits or evaluations. Results showed that both forms of person judgment were represented in the left ATL, and that these neural representations correlated with behavioral measures of implicit racial stereotypes and implicit racial attitudes, respectively. That is, the ATL supported independent conceptual representations of Black stereotypes and evaluations that are uniquely related to behavioral expressions of stereotypes and attitudes. Other research has shown that judgments concerning stereotypes of human targets recruited greater activity in the ATL than category judgments of inanimate objects (Contreras, Banaji, & Mitchell, 2012). Finally, it has been

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shown that disruption of ATL activity by transcranial magnetic stimulation attenuates the application of implicit gender stereotypes (Gallate, Wong, Ellwood, Chi, & Snyder, 2011), providing converging evidence for the important role of this region in representing stereotype knowledge. The integration of stereotype knowledge into online person impression is believed to involve the activation of stored stereotypes, drawn from the ATL, into the mPFC, where it is also combined with working memory processes supported by the lateral PFC (Amodio, 2014a). Researchers are just beginning to understand the neural substrates of stereotyping, but it is already clear that stereotyping depends on multiple mechanisms operating in a coordinated network and not a single underlying process. 2.1.3 Implicit Prejudice: Associations Between Evaluations and Social Categories Whereas stereotypes are considered to be the cognitive component of intergroup processes, prejudice (group attitude) is the evaluative component. Specifically, an intergroup attitude may comprise a general positive or negative association with a social category, as well as one’s affective responses to the category and its members. Although attitudes are considered to be central in social psychology (Brin˜ol & Petty, 2012), this statement is particularly true in intergroup contexts, given that our evaluations of ingroups and outgroups predict a variety of important downstream consequences (Allport, 1954; Dovidio, Gaertner, & Kawakami, 2003; Kawakami, 2014). Whereas evaluative associations related to outgroups tend to be negative (Dovidio et al., 1997; Dunham, 2011; Gabriel, Kawakami, Bartak, Kang, & Mann, 2010; Greenwald, McGhee, & Schwartz, 1998; Kawakami, Phills, Steele, & Dovidio, 2007), this is not always the case; in some instances, people may be more positive toward an outgroup than the ingroup. For example, implicit prejudice toward the elderly is typically negative and does not vary as a function of age of respondent (Levy & Banaji, 2002). Both younger (e.g., 18 years) and older (e.g., 70 years) adults tend to associate negative compared to positive concepts more with the elderly on an IAT. Similarly, both men and women tend to like women better than men (Eagly & Mladinic, 1994), and some Black Americans show more negative implicit prejudice toward Blacks than Whites (Ashburn-Nardo, Knowles, & Monteith, 2003; Mandalaywala, Amodio, & Rhodes, under review). As with stereotyping, prejudice refers to a complex set of processes linked to multiple neural structures. Early research on the neural basis of prejudice focused on the amygdala, a small structure located bilaterally in the medial

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temporal lobes (Amodio et al., 2003; Hart et al., 2000; Phelps et al., 2000). The amygdala receives direct (or nearly direct) input from every sensory organ, which allows it to respond very rapidly to both learned threats and rewards (Holland & Gallagher, 1999; LeDoux, 2000). For this reason, it was initially believed to be the neural substrate of implicit prejudice. However, evidence for the amygdala’s role in prejudice has been mixed, with fewer positive results than null findings in neuroimaging studies of race (Amodio, 2014a). Nevertheless, it is likely that the amygdala plays a role in the acquisition and expression of learned social threats (Amodio, 2014a; Olsson, Ebert, Banaji, & Phelps, 2005). Importantly, although the amygdala supports basic threat and reward processing, it cannot process conceptual information such as stereotypes. This distinction provided an important early clue that different cognitive mechanisms underlie implicit stereotyping and prejudice. Based on our theorizing (Amodio & Devine, 2008; Amodio et al., 2003), we (Amodio & Hamilton, 2012) found that anxiety about appearing prejudiced selectively amplified the expression of implicit attitudes but not stereotypes. In other research, we (Amodio & Devine, 2006) found that individual differences in participants’ implicit racial attitudes and implicit stereotype associations were largely independent and predicted unique outcomes. Implicit attitudes uniquely predicted self-reported affective responses to Blacks and participants’ seating distance from a Black study partner, whereas implicit stereotyping uniquely predicted trait impressions of a Black person and participants’ expectations of a Black partner’s academic test performance. More recent neuroscience models of prejudice identify the role of the striatum in supporting ingroup favoritism and approach-related intergroup responses (Stanley, Sokol-Hessner, Banaji, & Phelps, 2011), the insula in visceral emotional reactions to both ingroup and outgroup members (Cikara, Botvinick, & Fiske, 2011), and the orbital cortex for representing the integration of inputs from these regions into an evaluative representation that drives decisions (Gilbert et al., 2012). Finally, although these areas support more affective forms of prejudice, cognitive components of attitudes (e.g., associations with positive or negative concepts), and explicit beliefs about social groups further contribute to many expressions of prejudice. 2.1.4 Relations Between Implicit Identification, Stereotyping, and Prejudice Most people typically assume that identification, stereotyping, and prejudice are related. Specifically, one might predicate that the more you identify with an outgroup, the less you would associate negative characteristics with its

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members (e.g., hostile, lazy, untrustworthy), and the more you would like them (Allport, 1954). However, empirical evidence in support of these relations has been mixed. In particular, as noted earlier, the relation between stereotyping and prejudice has been found to be of only moderate strength. For example, when examining both implicit and explicit indices of prejudice and stereotyping, Dovidio, Brigham, Johnson, and Gaertner (1996) found a correlation of r ¼ 0.25. When only focusing on indices of implicit stereotyping and prejudice, this relation did not strengthen, r ¼ 0.19. More recently, using IATs that more directly dissociated valence associations from stereotype concepts, the correlation between implicit prejudice and stereotyping was even weaker, r ¼ 0.06 (Amodio & Devine, 2006)—a pattern consistent with the idea that these two implicit intergroup processes are rooted in different underlying cognitive and neural systems (Amodio et al., 2003; Amodio & Ratner, 2011). In contrast, there is some evidence for a causal link between self-outgroup associations and stereotyping and prejudice, respectively. With regard to the relation between identification and implicit prejudice, for example, perspective-taking strategies, such as imagining a day in the life of a target individual (Galinksy & Moskowitz, 2000; Galinsky, Ku, & Wang, 2005) or imagining the victim’s feelings while watching a series of incidents of racial discrimination, have been found to increase self-other overlap and also decrease negative outgroup attitudes. Likewise, increasing self-outgroup overlap with practice in associating the self with a group that included Blacks can reduce implicit prejudice (Woodcock & Monteith, 2013). Recent research related to training in approaching outgroup members has also provided evidence for a close link between self-outgroup associations and implicit prejudice. Using multiple methods of approaching social categories and several ways of measuring outgroup identification, we (Phills, Kawakami, et al., 2011; Phills, Santelli, Kawakami, Struthers, & Higgins, 2011) provided converging evidence that training in approaching social categories can increase self-outgroup associations. Specifically, training participants either to move a joystick toward or away from themselves in reference to a particular category, such as Blacks or Whites (Kawakami, Phills, et al., 2007), or to move circles representing the self and a target category closer together or farther apart (Aron, Aron, & Smollan, 1992), resulted in reduced bias in self-outgroup associations on an IAT and in brain activity. Furthermore, we found that increased associations between the self and Blacks, in turn, lowered implicit prejudice. These findings suggest that one reason why approach orientations increase positive attitudes is because they foster identification with the target.

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With regard to the link between identification and implicit stereotyping, the evidence for a causal relation is mixed. Whereas research on perspective taking indicates that increasing self-other overlap can also reduce outgroup stereotyping (Galinksy & Moskowitz, 2000; Galinsky et al., 2005), research on the conditioning of self-outgroup links does not (Woodcock & Monteith, 2013). Although research, as noted earlier, has found evidence for the impact of changes in outgroup identification on prejudice, other work has examined whether this relation is bidirectional by investigating the impact of prejudice on outgroup identification (Phills, Kawakami, Krusemark, & Nyguen, under review). Previous theorizing provides some justification for the possibility that intergroup attitudes may cause identification (Cialdini & Richardson, 1980; Kelley & Thibaut, 1978). In particular, because we believe that simply connecting ourselves with favorable ingroups will make us look more favorable (Cialdini et al., 1976; Snyder, Lassegard, & Ford, 1986), we may try to associate with high status or valued others. One way to improve self-outgroup associations, therefore, may be to use evaluative conditioning to increase the positivity of outgroups. In two studies, we (Phills et al., under review) found that after training in associating positive concepts with Blacks, non-Black participants showed less negative implicit attitudes toward Blacks. Although this basic evaluative conditioning effect on racial attitudes is well established (Lai et al., 2014; Olson & Fazio, 2006), we also found that evaluative conditioning increased the strength of associations between the self and Blacks. Furthermore, mediation analyses provided consistent evidence for a close causal link between changes in implicit prejudice and changes in outgroup identification. Together, these findings are consistent with the balanced identity theory (BIT; Cvencek, Greenwald, & Meltzoff, 2012; Greenwald et al., 2002), which purports a causal relation between attitudes and identification. Much like classic consistency theories in social psychology (Festinger, 1957; Gawronski & Strack, 2012), this model proposes that identities, attitudes, and self-esteem coordinate to maintain affective–cognitive consistency and that the interrelations among these constructs constrain each other. In particular, the BIT suggests that an association between two concepts should strengthen when both concepts are associated with the same third concept. Because we typically maintain strong associations between the self and good (self-esteem; Bosson, Swann, & Pennebaker, 2000; Zhang & Chan, 2009), increasing associations between a stigmatized outgroup (e.g., Blacks) and good (attitudes), such as in the Phills et al. research

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(under review), should and does increase associations between the outgroup and the self (identities). Likewise, increasing associations between stigmatized outgroups and the self, such as in the Phills, Kawakami, et al. research (2011), should and does increase associations between the stigmatized outgroup and good (attitudes). Together, these findings provide evidence for a causal, bidirectional link between implicit group identification and attitudes. In summary, a broad literature implicates the spontaneous activation of category-based knowledge once a person is construed as a member of a social group. Furthermore, these activations have been shown to have wideranging implications for how we respond to outgroups. In the next section, we will explore some of these downstream consequences.

2.2 Downstream Consequences of the Activation of Category-Based Knowledge Importantly, implicit identification, stereotypes, and prejudice can influence variety of downstream consequences, including our ability to identify emotions on outgroup faces, the extent to which we care when outgroup members are treated unfairly or are in pain, and our support for government policies to improve the situation of minorities. These constructs also impact a variety of behaviors, from basic fight/flight responses and the shooter bias, to the willingness to interact with outgroup members and a host of other forms of discrimination. 2.2.1 Emotion Identification To avoid discordance and to facilitate communication, the quick and precise identification of emotional expressions is crucial in both interpersonal (Adolphs, 2002; Baron-Cohen, Wheelwright, & Jolliffe, 1997; Haxby et al., 2001; Keltner & Haidt, 1999) and intergroup contexts (Dovidio et al., 2003; Mackie & Smith, 2002; Stephan & Stephan, 1985). However, research has demonstrated that people are better at recognizing emotional expressions on ingroup faces relative to outgroup faces (Izard, 1971). For example, in a metaanalysis of 97 studies, Elfenbein and Ambady (2002) found that although cross-cultural emotion recognition was better than chance guessing, accuracy was significantly diminished when individuals were from different ethnic groups. Theorists suggest that because cultures may differ in the use of cues, and that members of one culture are less familiar with the emotional dialects and processing styles of cultures different from their own, people are less accurate in emotion recognition across societies

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(Elfenbein, Beaupre, Levesque, & Hess, 2007; Matsumoto, 1989; Matsumoto, Olide, & Willingham, 2009). Studies conducted within a single culture, however, have also demonstrated an outgroup disadvantage in emotion identification. For example, multiple studies have found that White American perceivers see anger lingering longer and appearing earlier on Black relative to White faces and misread neutral facial expressions of Blacks as conveying anger (Hugenberg, 2005; Hugenberg & Bodenhausen, 2003; Hutchings & Haddock, 2008; Kang & Chasteen, 2009), in spite of the objective greater similarity of White than Black faces to anger (Zebrowitz et al., 2010). Recent research in this context suggests that emotion identification errors may be driven in part by outgroup stereotypes (Bijlstra, Holland, Dotsch, Hugenberg, & Wigboldus, 2014; Hugenberg & Bodenhausen, 2003). Because African Americans are often stereotyped as hostile and aggressive relative to Whites (Devine, 1989), these stereotypes may influence the interpretation of facial expressions. For instance, White perceivers tend to interpret ambiguous facial expressions on Black male faces as angrier than matched White faces (Hugenberg & Bodenhausen, 2003; Hutchings & Haddock, 2008; Kang & Chasteen, 2009), and Black faces with anger expressions more strongly activate Black stereotypes than faces with happy expressions (Kubota & Ito, 2014). It is notable, however, that the ingroup emotion identification advantage has even been found using a minimal group paradigm (Ratner et al., 2014; Young & Hugenberg, 2010). These findings indicate that biases in emotion identification and a readiness to perceive positive ingroup expressions may be due to distinct processing of outgroup relative to ingroup faces (e.g., less configural processing). Because minimal groups by definition are not strongly associated with cultural differences or group stereotypes, these findings suggest that additional factors may be at play in biases in emotion identification. In accordance with this possibility, we found that participants higher in implicit prejudice were more likely to see hostile faces as African American (Hugenberg & Bodenhausen, 2004). Remarkably, Dunham (2011) demonstrated similar effects using experimentally constructed ingroup vs outgroup contexts. Minimal outgroup faces were seen as expressing more hostility than ingroup faces. Put simply, “they” appear to be angry, even in the absence of stereotypes related to aggression or intergroup conflict. Recent research by Friesen et al. (under review) suggests a general process by which prejudice may impact accuracy in emotion identification:

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reduced attention to outgroup eyes. Specifically, these researchers found that White participants distinguished less between true and false smiles on Black compared to White faces. Furthermore, they tested the importance of a deficit in attention to the eyes of Black faces in this process in three ways (see Fig. 6). First, because the only difference in the facial stimuli in targets displaying true and false smiles was the Duchenne markers related to the eyes, differential emotion identification on Black and White faces by participants was most likely related to attention to this feature. Second, the results

Fig. 6 Sample stimuli showing Duchenne smiles (A), non-Duchenne smiles (B), areas of interest (eyes, nose, mouth) marked for measuring eye-tracking gaze (C), and presentation of eyes only (D).

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demonstrated that participants spent less time attending to the eyes of Black than White targets and that attention to the eyes predicted the ability to differentiate between emotions. Finally, when participants were presented with only the eyes of Black and White targets and therefore forced to focus on this feature, they demonstrated no difference in their ability to distinguish between Duchenne and non-Duchenne smiles between these races. Together these findings provide strong evidence for intergroup bias in the identification of a variety of emotions. They also highlight the role of multiple categories, prejudice, and stereotypes in this process. 2.2.2 Caring About Outgroups It is perhaps not surprising that the activation of category-based knowledge can influence whether we care about the circumstances of outgroup members. For example, an implicit measure of Black identification (i.e., a selfother overlap) among White perceivers predicted support for government aid to Blacks and affirmative action policies that improve the situation of minorities (Craemer, Shaw, Edwards, & Jefferson, 2013), and implicit prejudice has been modestly related to support for affirmative action in corporate and educational settings (Hardin & Banaji, 2013; Lai et al., 2016). Although endorsing these types of social policies is one way to demonstrate that you care about outgroups, recent research has also examined participants’ responses to perceiving outgroup racism. Kawakami, Dunn, Karmali, and Dovidio (2009) suggest that one reason why racism may be so prevalent is because people are indifferent when witnessing derogatory comments against members of categories to which they do not belong and do not socially reject racists. In our experiments, participants either experienced a White confederate making a racist comment about a Black confederate or imagined themselves in this situation. The results demonstrated that anticipated affective responses were more negative for people who imagined themselves in the situation than people who actually observed the racism. Furthermore, when asked to choose either the Black or White confederate for a partner task, those who imagined themselves in the situation overwhelmingly predicted that they would avoid the White racist (choosing him only 20% of the time), whereas people who actually experienced the racist situation chose the White racist approximately 70% of the time. Surprisingly, experiencers’ affect ratings and partner choice after hearing a racist comment did not differ from a condition in which there was no racist comment. Follow-up research (Karmali, Kawakami, & Page-Gould, under review) showed that after witnessing racism, people displayed a

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physiological profile indicative of an orienting response (decreased heart rate and increased skin conductance) rather than a stress response and demonstrated less cognitive impairment on a Stroop task than people who imagined this situation. Further research demonstrated that differential responding when directly observing compared to imagining racism was related to negative perceptions of the Black target and not the White racist (Karmali, Kawakami, & Shim, under review). We (Kawakami et al., 2009) propose that although people draw on their conscious explicit attitudes, which are typically more egalitarian, when imagining their responses to racism, their more negative implicit attitudes and stereotypes shape their complacency when faced with a racist act (Dovidio et al., 2009; Dunn & Ashton-James, 2008). In line with recent research (McConnell, Dunn, Austin, & Rawn, 2011), we speculate that implicit category-based knowledge determines the extent to which people overestimate how much they care about outgroup racism and other negative outcomes for outgroup members. Recent research has demonstrated that people may not only be less empathic when bad things happen to outgroup members (Hein, Silani, Preuschoff, Batson, & Singer, 2010; Xu, Zuo, Wang, & Han, 2009), but that they may actually experience pleasure in response to outgroup members’ misfortunes (i.e., schadenfreude; see Cikara et al., 2011; Cikara & Fiske, 2011, 2013; Hoffman, Trawalter, Axt, & Oliver, 2016; Zaki & Cikara, 2015). Specifically, this research has demonstrated that people feel pleasure in response to outgroup adversities and pain in response to outgroup triumphs (i.e., gluckschmerz). Furthermore, this pattern of schadenfreude and gluckschmerz was attenuated when outgroup members were portrayed as less distinct from ingroup members based on a set of prior questions, thereby suggesting that increasing outgroup identification can increase empathy (Cikara, Bruneau, Van Bavel, & Saxe, 2014). In other words, the more you perceive outgroup members as similar to the self, the more you care about their welfare. 2.2.3 Intergroup Behaviors Although there is a dearth of research investigating interpersonal behavior in social psychology (Baumeister, Vohs, & Funder, 2007), and particularly in an intergroup context (Fiske, 1998), behavior toward outgroup members is considered to be perhaps the most critical component of intergroup relations (Allport, 1954; Dovidio, Gaertner, et al., 2002). Not surprisingly, given the importance of the attitude–behavior link to our field, the majority of

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research examining the downstream consequences of the activation of category-based knowledge has been in the area of prejudice and discrimination, whereas very few studies have investigated the link between identification or stereotyping and behavior. Research has demonstrated that in interracial interactions, implicit prejudice predicts nonverbal friendliness (Dovidio et al., 1997; Fazio, Jackson, Dunton, & Williams, 1995), visual eye contact and blinking (Dovidio, Gaertner, et al., 2002; Dovidio, Kawakami, et al., 2002), speaking time, speech errors, and hesitations (McConnell & Leibold, 2001), and interpersonal distance (Amodio & Devine, 2006). Implicit prejudice has also been found to predict voting behavior (Knowles, Lowery, & Schaumberg, 2010). For example, negative attitudes toward Blacks on an IAT predicted intentions to vote for McCain over Obama in the 2008 presidential election (Greenwald, Poehlman, Uhlmann, & Banaji, 2009; Greenwald, Smith, et al., 2009). Implicit prejudice has also predicted hiring preferences (Agerstrom & Rooth, 2011; Rooth, 2010) and a willingness to work with a Black or White partner (Ashburn-Nardo et al., 2003). It is important to note that the vast majority of this evidence has been correlational. Recent research, however, has demonstrated that interventions targeting outgroup attitudes can influence both implicit prejudice and overt behaviors, such as outgroup immediacy behaviors associated with closeness and forming social bonds (Word, Zanna, & Cooper, 1974). For example, our research (Kawakami, Dovidio, & van Kamp, 2007; Kawakami, Phills, et al., 2007) has demonstrated that training in approaching Blacks reduced the relative preference for Whites on an IAT and increased positive body orientations to a Black interaction partner. Although these experiments show that the same strategy can be effective in improving intergroup attitudes and behavior, these attitudinal and behavioral biases were not measured in the same experiment. However, when we (Mann & Kawakami, 2012) have examined the influence of an intervention on both implicit attitudes and discriminatory behaviors within a single study, we found no relations between these two types of processes. Instead, we found that feedback that participants were progressing toward the goal to be egalitarian actually resulted in a disengagement from the focal goal of egalitarianism, and it ironically increased both biased racial associations on an IAT and increased seating distance from a Black student. Further, these attitudinal and behavioral responses were not correlated. More research on the causal

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relation between intergroup attitudes and behaviors is clearly necessary to better understand these connections (Lai, Hoffman, & Nosek, 2013). Research by Rudman and Ashmore (2007) on the relation between implicit stereotyping and behavior, alternatively, is interesting for two reasons. First, these studies focused on self-reports by Whites on overt hostility toward outgroup targets and on economic discrimination (i.e., decisions to fund student ethnic organizations). Second, they compared the ability of implicit stereotypes to predict such behaviors independent of implicit attitudes (cf. Amodio & Devine, 2006). These findings demonstrated that implicit stereotypes predicted overtly hostile and discriminatory intergroup behaviors. Research has also demonstrated that implicit stereotyping, as indexed by a stereotypic explanatory bias (SEB), can predict the use of biased questions during a job interview (Sekaquaptewa, Espinoza, Thompson, Vargas, & von Hippel, 2003). Specifically, in a separate task, the SEB— the extent to which White participants provided an explanation for stereotypic inconsistent behaviors for Blacks (e.g., got a job at Microsoft; refused to dance) relative to stereotypic consistent behavior (e.g., easily made the team; blasted loud music in his car)—predicted the number of stereotypic questions they chose to ask when interviewing a Black but not White confederate. However, a recent metaanalysis (Greenwald, Poehlman, et al., 2009) shows only a moderate relation between implicit attitudes/stereotypes and racially discriminatory behavior (Blacks and Whites, r ¼ 0.24) and an even weaker relation between implicit attitudes/stereotypes and behavior toward other social categories (i.e., ethnicity, age, weight, r ¼ 0.20; sexual orientation, r ¼ 0.18). Identification with outgroups relative to ingroups has been associated with a variety of responses, including support for pro-Black government policies and the criterion to shoot Blacks in the Payne (2001) weapons task (Craemer et al., 2013; Kenworthy, Barden, Diamond, & del Carmen, 2011; Woodcock & Monteith, 2013). Notably, Galinsky and colleagues (Galinsky et al., 2005; Galinsky, Wang, & Ku, 2008) found that perspective taking, which typically increases self-outgroup overlap, leads to synchronizing behavior with target groups. For example, this research has shown that when taking the perspective of an elderly man, participants were more likely, than in nonperspective taking control conditions, to act cooperatively on a prisoner’s dilemma game, in line with the stereotype of the elderly as kind and generous. Likewise, when taking the perspective of a Black man, participants were less likely, than in nonperspective taking control conditions,

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to act cooperatively, in line with the stereotype of Blacks as aggressive and competitive. Together this research provides evidence for the important implications that the activation of category-based knowledge can have in determining how we respond to social category members. In particular, research has demonstrated that implicit outgroup identification, stereotyping, and prejudice impact our ability to decode outgroup emotions, as well as our empathy for and responses to the misfortunes of outgroup members. Although research suggests that these associations can also influence behavior, further studies investigating actions in an intergroup context are clearly necessary.

2.3 Strategies to Reduce the Activation of Category-Based Knowledge and Biased Behavior For many people, implicit prejudice contradicts explicitly held egalitarian attitudes, beliefs, and values. For others with explicit prejudices, the public expression of prejudice may incur social sanctions. In both of these cases, people are often motivated to control their expressions of bias. Recent research has examined a variety of strategies to regulate intergroup responses. In contrast to earlier theorizing and research that focused largely on the Contact Hypothesis (Allport, 1954; Dovidio et al., 2003; Pettigrew & Tropp, 2006) or the affiliated Jigsaw classroom (Aronson, Blaney, Stephan, Rosenfeld, & Sikes, 1977; Aronson & Bridgeman, 1979) as a means of reducing bias, in recent years a number of creative alternative approaches have been advanced. Although research has demonstrated a positive impact of traditional conceptualizations of contact on implicit prejudice (Aberson, Porter, & Gaffney, 2008; Dasgupta & Rivera, 2008; Shook & Fazio, 2008; Tam, Hewstone, Harwood, Voci, & Kenworthy, 2006; Turner, Hewstone, & Voci, 2007), new interventions have extended this theorizing by examining a broad array of intergroup interaction contexts. As depicted in Fig. 1, these interventions aim to increase self-outgroup overlap, change characteristics associated with a target group, and/or decrease negative outgroup evaluations. In accordance with the primary expectations related to Contact Theory, theorists propose that increased experience with outgroups will change the activation of category-based knowledge by enhancing identification with the outgroup, thereby reducing stereotyping and prejudice toward category members. For example, exposure to counterstereotypic or positive outgroup exemplars (Brauer, Er-rafiy, Kawakami, & Phills, 2012; Dasgupta & Greenwald, 2001a), learning to associate new characteristics or evaluations with outgroup members (Kawakami

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et al., 2000; Olson & Fazio, 2006; Phills et al., under review), approaching outgroup members (Kawakami, Dovidio, et al., 2007; Kawakami, Phills, et al., 2007; Kawakami, Steele, Cifa, Phills, & Dovidio, 2008; Page-Gould, Mendoza-Denton, Allegre, & Sij, 2010; Page-Gould, Mendoza-Denton, & Tropp, 2008; Phills, Kawakami, et al., 2011; Phills, Santelli, et al., 2011), perceiving similarity between outgroup members and the self (Kawakami, Williams, et al., under review; Walton & Cohen, 2007), and perceiving outgroup members in a new and positive context (Rudman et al., 2001; Wittenbrink et al., 2001) are all closely tied to the main tenants of Contact Theory. In this section, we will describe research related to these and other interventions and their capacity to reduce various forms of intergroup bias. 2.3.1 Increasing Implicit Identification Integrating outgroups into the ingroup has been demonstrated to reduce biases against outgroup members. Although research on the Common Ingroup Identity Model (Gaertner & Dovidio, 2000) has shown that perceptions of overlapping social identities (i.e., ingroup–outgroup associations) can decrease favoritism toward the ingroup, increasing implicit outgroup identification (i.e., self-outgroup associations) has also been shown to be important to intergroup relations (Greenwald et al., 2002). Researchers have therefore examined several ways to increase self-outgroup overlap, which include perspective taking (Galinsky et al., 2005), feeling socially accepted by the outgroup (Kunstman, Plant, Zielaskowski, & LaCosse, 2013), and building associations between the self and a group comprised of outgroup members (Woodcock & Monteith, 2013). One strategy that we have used to directly target self-outgroup associations is approach training (Phills, Kawakami, et al., 2011; Phills, Santelli, et al., 2011). Because the intent of approach training is to bring the outgroup closer to the self, it is expected to increase psychological closeness between the self and the target category (Liberman, Trope, & Stephan, 2007; Nussinson, Seibt, Hafner, & Strack, 2010). Other strategies such as evaluative conditioning, however, may also change outgroup identification but through a less direct route. In particular, we (Phills et al., under review) found that practice in associating positive concepts with Blacks reduced implicit prejudice, which in turn increased implicit self-Black associations. Importantly, our results indicate that prejudice in this case had an intervening variable effect (Pek & Hoyle, 2016) in that evaluative conditioning did not directly impact outgroup identification but only reduced this bias through racial attitudes.

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A related phenomenon in intergroup behavior is social tuning or selfsynchronization—the extent to which people modify their behaviors and other aspects of the self to fit their social environment (Chartrand, Maddux, & Lakin, 2005; Gabriel et al., 2010). Just like research on mimicry has demonstrated that people match the physical gestures (Bernieri, 1988; Chartrand & Bargh, 1999) and vocal rhythms (Cappella & Panalp, 1981) of interaction partners, experiments have shown that people also sometimes synchronize themselves to outgroup social categories (Kawakami, Dovidio, & Dijksterhuis, 2003; Kawakami et al., 2002; Lowery, Hardin, & Sinclair, 2001; Sinclair, Lowery, Hardin, & Colangelo, 2005). Specifically, recent research suggests that factors that induce a focus on interconnectedness can influence synchronization to outgroup categories (Kawakami et al., 2012). For example, when participants were exposed to stimuli related to interdependent self-construals, they associated themselves more with their social environment, in this case, Blacks. Because this type of social tuning is strongly implicated in smoothing interactions and creating strong social bonds, it is particularly relevant in intergroup contexts, which are often fraught with misunderstandings and misperceptions (Dovidio, Gaertner, et al., 2002; Dovidio, Kawakami, et al., 2002; Holoien, Bergsieker, Shelton, & Alegre, 2015; Vorauer, 2005; Vorauer & Sakamoto, 2006). One potentially fruitful avenue for future research, therefore, is to investigate whether interventions that strengthen self-outgroup associations also increase social tuning and thereby facilitate coordinated intergroup interactions. 2.3.2 Changing Implicit Stereotypes Although some interventions that increase implicit identification also decrease implicit stereotypes (e.g., perspective taking, Blair, 2002; Galinsky et al., 2005, 2008), others do not (e.g., conditioning self-other overlap, Woodcock & Monteith, 2013). One method directly targeting implicit stereotyping is extensive practice in negating stereotypic concepts and associating nonstereotypic concepts. This strategy has proven to be successful in decreasing the activation of stereotypes (Kawakami et al., 2000), the application of stereotypes (Kawakami, Dovidio, et al., 2007), and discrimination in hiring decisions (Kawakami, Dovidio, & van Kamp, 2005) related to a range of social categories, including Blacks, women, and skinheads. Follow-up studies demonstrated that if participants were solely required to affirm counterstereotypes vs negate typical cultural associations, only the former strategy in which they responded positively to counterstereotypes

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was effective in reducing implicit stereotyping (Gawronski, Deutsch, Mbirkou, Seibt, & Strack, 2008). It is unclear in this latter research, however, whether completing both types of responses simultaneously as in the initial experiments (Kawakami et al., 2000, 2005; Kawakami, Dovidio, et al., 2007; Kawakami, Phills, et al., 2007) is even more powerful, as suggested by current models of bias reduction (Devine & Monteith, 1993), than solely affirming counterstereotypes. Being exposed to counterstereotypic exemplars can also decrease implicit stereotyping. In particular, contact with female leaders or women and Blacks in high status positions can decrease typical group associations. For example, research has shown that both presenting women with information about female leaders in a lab setting and preexisting differences in exposure to female professors in naturally occurring environments decreased gender stereotypes (Dasgupta & Asgari, 2004). Likewise, research has demonstrated that students enrolled in a 14-week prejudice and conflict seminar that was taught by a Black professor demonstrated decreased implicit stereotyping and prejudice over time (Rudman et al., 2001). Importantly, a control group enrolled in a research methods course taught by a White professor did not show a similar reduction. 2.3.3 Decreasing Implicit Prejudice Because understanding attitudes is considered essential to understanding social change and modifying behaviors (Brin˜ol & Petty, 2012), the vast majority of research on strategies to reduce intergroup bias has focused on prejudice (Paluk & Green, 2009). Although it is beyond the scope of this chapter to adequately describe all types of interventions targeting implicit prejudice, this section will highlight a few distinct strategies that are representative of current research in the field, including evaluative conditioning, exposure to positive outgroup exemplars and positive characteristics, progress on egalitarian goals, and social context. Importantly, some of the strategies that have been effective in reducing other types of bias, such as outgroup stereotypes, are also effective in improving outgroup attitudes. For example, as already mentioned, exposure to counterstereotypic exemplars (Dasgupta & Asgari, 2004; Dasgupta & Greenwald, 2001a, 2001b; Rudman et al., 2001) and training in approaching social categories can increase positive evaluative associations with the outgroup. Interestingly, approaching a specific intergroup orientation can also influence group attitudes. For example, we (Phills, Santelli, Kawakami, Struthers, & Higgins, 2011) found that an

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approach strategy in which participants were instructed to respond “yes to equality” was more effective in subsequently reducing implicit prejudice on an IAT when placed within the context of positive images related to racial harmony (e.g., Dr. Martin Luther King, interracial friends, families, and couples) than when imbedded in negative racial images (e.g., the Ku Klux Klan, burning crosses, and lynchings). Alternatively, an avoidance strategy in which participants were instructed to respond “no to prejudice” was more effective in reducing implicit prejudice when placed in the context of negative rather than positive racial images. Similarly, an alternative approach strategy in which participants were instructed to “be egalitarian” was more successful in subsequently reducing implicit prejudice when associated with a promotion than prevention prime. In contrast, an avoidance strategy in which participants were instructed to “not be prejudiced” was more effective when associated with a prevention than promotion prime. Together, these results suggest that matching approach and avoidance strategies to the contextual valence of the context and to regulatory focus orientations may impact whether this strategy is effective in reducing bias (Cesario, Grant, & Higgins, 2004). One of the most direct routes to changing implicit attitudes is evaluative conditioning (Hofmann, De Houwer, Perugini, Baeyens, & Crombez, 2010). In an intergroup context, evaluative conditioning has been shown to reduce implicit prejudice (French, Franz, Phelan, & Blaine, 2013; Olson & Fazio, 2006; Phills et al., under review), and if such methods are used over an extended period (e.g., 12 weeks); they can create effective long-term reductions (Devine, Forscher, Austin, & Cox, 2012). On an applied level, recent research suggests that advertising campaigns associating outgroup category members with only positive characteristics may not be as effective in reducing negative implicit attitudes as advertisements that present both positive and negative characteristics (Brauer et al., 2012). Specifically, in this research, we created posters in collaboration with an advertising firm that paired some images of Arab men and women with positive traits (e.g., sociable) and some members with negative traits (e.g., stingy). In line with previous theorizing that suggests that the portrayal of outgroups as variable and heterogeneous can reduce negative attitudes (Brauer & Er-rafiy, 2011; Ryan, Judd, & Park, 1996), we found that posters that associated a mix of valenced traits with an outgroup were more effective in decreasing prejudice than posters that associated only positive traits. Furthermore, think-aloud data suggest that one reason for this strategy’s success

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is that the mixed message is more acceptable to respondents and results in less resistance to the communication. Not surprisingly, research has shown that contexts are also important to the activation of negative or positive group-based evaluations. Specifically, there is evidence that non-Blacks’ implicit racial attitudes vary depending on the valence of the context (Wittenbrink et al., 2001). When target outgroup members were presented in a positive environment (e.g., church), participants demonstrated lower implicit prejudice than when they were presented in a negative environment (e.g., street corner). Further, research has found that in a prison setting, Black targets dressed as lawyers rather than prisoners reduced implicit prejudice (Barden, Maddux, Petty, & Brewer, 2004). Thus, the social roles implied by a specific context and features of the target can attenuate negative outgroup attitudes. Research has also demonstrated that perceptions of and affective reactions to intergroup situations can influence implicit prejudice. In contrast to previous studies, however, we (Mann & Kawakami, 2012) investigated whether such interventions increase rather than decrease negative racial attitudes. In particular, based on recent theorizing related to social goals, we examined whether perceived progress on the goal to be egalitarian would lead participants to disengage from this goal. As described earlier, we found that when motivated to be egalitarian, participants showed greater racial bias on an implicit measure of prejudice and sat farther away from Blacks after receiving feedback that they were becoming more positive toward Blacks on a (bogus) psychophysiological index of bias than when they received feedback that they were becoming less positive or when they received no feedback. Like making progress on dieting or study goals (Fishbach, Dhar, & Zhang, 2006; Fishbach & Zhang, 2008), advancing on the goal to be egalitarian can lead people to disengage from the focal goal which can result in behaviors inconsistent with that goal (e.g., eating a piece of cake, going to a movie, or distancing yourself from outgroup members). Recent work on the impact of colorblindness on subsequent racial attitudes provides further evidence that initially acting in seemingly nonprejudiced ways may increase rather than decrease subsequent bias (Kawakami, Karmali, et al., under review). In particular, research indicates that because of current social norms, people are often wary of acknowledging race, especially in an ambiguous situation (Apfelbaum et al., 2008; Norton et al., 2006). However, not mentioning race or acknowledging negativity in an interracial context can have adverse consequences. Specifically, if participants believe that acting in colorblind ways is related to

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imposed societal norms or mandatory goals such as in our previous research (Mann & Kawakami, 2012), these behaviors may lead to disengagement and less effort to be nonprejudiced. However, if the same actions are perceived as commitment toward a goal, colorblindness may lead to a sustained interest in the goal and complementary actions. This theorizing suggests that the implications of initially acting in nonbiased ways may be different depending on the reasons for why people avoided the use of race or performed other seemingly egalitarian actions. To test these hypotheses, participants were presented with a novel Ambiguous Photograph Task in which they were instructed to describe a photograph that depicted a Black and White man bumping in a crowded stairwell. We found across three studies, as expected, that when describing this ambiguous interracial interaction in which race was not obviously relevant, a large majority of participants did not mention race, or acknowledge any potential negativity in this situation (over 80%). More importantly, we found that after demonstrating colorblindness (not mentioning race), people high but not low in implicit prejudice responded with more bias. Notably, this difference was not found in a control condition in which participants were not given the opportunity to act in nonprejudiced ways (i.e., no opportunity to demonstrate colorblindness). Because people high in implicit prejudice conceptualize their avoidance of racial labels as behavior imposed by society, we expected and found that they subsequently showed higher levels of bias than people low in implicit prejudice, who presumably do not use racial labels because of a sincere desire to live up to personal egalitarian standards. 2.3.4 The Short- and Long-Term Efficacy of Strategies Targeting Implicit Bias Our review provides exciting new evidence for the potential of some strategies to reduce implicit bias. In two innovative projects, Lai and colleagues (2014, 2016) compared the relative efficacy of a number of interventions in improving negative outgroup attitudes. In particular, in the first project (2014), researchers were invited to enter a contest to test the impact of a proposed intervention to reduce implicit preferences on an IAT. The criteria required that the strategy was amenable to being run on the Project Implicit website and that participants were able to complete the intervention in 5 min or less. Of the 17 strategies examined, 8 led to reductions in implicit prejudice. The successful approaches from the most effective to the least effective in terms of meta-analytic effect sizes were related to

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exposure to positive or counterstereotypic outgroup category members, priming multiculturalism, evaluative conditioning, and implementation intentions. Interventions that were not effective were related to perspective taking, appeals to egalitarian values, imagining interracial contact, and emotion induction. In a second, highly powered project (Lai et al., 2016), the same eight strategies found to be successful in the first project were once again tested for their capacity to reduce implicit prejudice both immediately and over time. In particular, participants were presented with a pretest IAT, the intervention, an immediate posttest IAT, and a follow-up IAT approximately 2 days later. Although all strategies were once more found to be effective in decreasing intergroup bias immediately after the intervention, none of them showed a significant effect after the 2-day delay. Although together these findings provide important information on the relative efficacy of recent interventions aimed at reducing intergroup bias, it should be noted that these effects are preliminary and related to a specific testing environment and set of interventions. In particular, most of the interventions and data collection across these projects occurred online, a methodological choice that could have important implications. In accordance with a recent examination of experimental studies using online samples Zhou & Fishbach, 2016), the attrition rate (from those who began the study but did not complete it) in the first project was approximately 30% and may have varied according to experimental conditions, which can bias results. Also, it is possible that intergroup interventions may be less impactful online compared to in lab. For example, Phills et al. (under review) found that a similar manipulation related to evaluative conditioning was more effective in reducing implicit prejudice when participants completed the study in the laboratory compared to online. Second, all studies in the two projects focused on one type of bias, implicit prejudice, and used (for the most part) the same measure of this construct, the IAT. It is therefore not clear if other approaches related to ameliorating other types of bias such as implicit stereotyping and identification are effective or durable, and if similar effects are found with other measures of implicit prejudice. Furthermore, the experimental design in which the same IAT was presented at the pretest, posttest, and the follow-up may have reduced the impact of the interventions over time. Third, with regard to the interventions, although a variety of methods were sampled, these projects had specific criteria for inclusion—the strategy had to be amenable to an online manipulation and had to be very short in

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duration (5 min or less). The effect of longer interventions or more complex manipulations, such as many of those described in this chapter, was not examined. For example, experiments related to implicit stereotyping training can have a duration of approximately 1 h (Kawakami et al., 2000) and contexts can include elaborate cover stories (Kawakami et al, 2009; Mann & Kawakami, 2012) or more specialized equipment (Kawakami et al., 2014). Furthermore, all interventions in these projects were presented only once. Research suggests that including multiple sessions related to a particular strategy over an extended period can dramatically increase its impact and its efficacy over time (Devine et al., 2012). So although recent results suggest that many short and simple interventions may have limited value in reducing implicit prejudice over time, more involved techniques have the potential to have lasting consequences. A further important avenue of investigation is related to a more general approach to behavioral control and cues that trigger self-regulation processes (Amodio & Devine, 2010; Monteith, 1993). Rather than targeting the direct reduction of associations, an alternative strategy focuses on blocking the expression of bias in behavior. This approach describes an “override” or “replacement” model of control that has the potential to be more enduring. Devine’s (1989) initial tests of this model suggested that whereas both high- and low-prejudiced individuals exhibited a similar degree of implicit stereotype accessibility, low-prejudiced participants actively inhibited the expression of stereotypes in behavior. Devine argued that although this form of control may not immediately change one’s mental associations, repeated exertions of control could eventually lead to changes in these associations (Devine, Plant, Amodio, Harmon-Jones, & Vance, 2002). Early research on the behavioral control of bias assumed a relatively deliberative strategy. In particular, Monteith (1993; Devine & Monteith, 1993) proposed a self-regulatory model of prejudice control whereby one’s feeling of guilt, caused by an unintended prejudiced response, initiates plans for egalitarian future responses, and increases vigilance for cues to engage an egalitarian response. This model has been supported in multiple behavioral and psychophysiological studies (Amodio, Devine, & Harmon-Jones, 2007; Monteith, 1993; Monteith, Ashburn-Nardo, Voils, & Czopp, 2002) and has been shown to effectively reduce the expression of bias in future behaviors. In an effort to understand how the cognitive control of bias may operate very rapidly in a single unfolding response, we (Amodio, Devine, & Harmon-Jones, 2008; Amodio et al., 2004) tested a model influenced by

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theories in cognitive neuroscience (Botvinick, Braver, Barch, Carter, & Cohen, 2001). This model addressed how a biased tendency is rapidly detected within the brain during the course of a single response, and how top-down control is then recruited to guide behavior toward an intended response. In the brain, the dorsal ACC is sensitive to conflict between a goal representation (e.g., to classify stimuli accurately, as in a Stroop or sequential race priming task) and a motor tendency toward an alternative, unintended response. In the case of intergroup bias, the unwanted tendency may be driven by the activation of implicit stereotypes or prejudices. As this conflict is detected, the ACC recruits regions of the PFC to exert top-down control over behavior to promote the intended response. Using ERPs, we (Amodio et al., 2004) demonstrated the role of the ACC in the control of implicit bias on the weapons identification task. On stereotype-incongruent trials—that is, trials that required control—stronger ACC activity was observed within approximately 350 ms of the target stimulus. Furthermore, participants’ ACC response to such trials predicted their degree of stereotype control in behavior. Similar patterns of ACC activity have been shown using the Shooter Task (Correll, Urland, & Ito, 2006), stereotype priming (Bartholow, Dickter, & Sestir, 2006), and the evaluative race IAT (Beer et al., 2008). The role of detection processes has also been supported by multinomial modeling (Gonsalkorale, Sherman, Allen, Klauer, & Amodio, 2011; Sherman et al., 2008). Together, this work reveals that the control process involves two different components—detection of bias and implementation of intended behavior—and that the detection of bias can occur rapidly and without deliberation. This model of prejudice control has helped to explain how we reduce expressions of implicit bias in behavior, as well as individual differences in people’s ability to exert control. For example, individuals with equally positive explicit attitudes toward Blacks can vary widely in their ability to control implicit bias (Devine et al., 2002; Payne, 2005), and we (Amodio et al., 2008) found that this variability is due to differences in their ACCrelated sensitivity to bias activation. Given other research suggesting that a different region of the frontal cortex—the mPFC/rostral ACC—is important for understanding external social cues (Amodio & Frith, 2006), this model also helps explain why people sensitive to external social pressures are especially prone to control failures (Amodio, Kubota, Harmon-Jones, & Devine, 2006) and why intergroup social anxiety can impair control (Amodio, 2009). This approach has even been extended to examine

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differences in regulatory ability associated with political ideology (Amodio, Jost, Master, & Yee, 2007) and social power (Schmid, Kleiman, & Amodio, 2015). Our prejudice control model is consistent with bias reduction strategies that target the behavioral expression of advantage. For example, an implementation intention strategy links a specific situational cue to a specific action plan such that when the cue is encountered, the action can be implemented in behavior rapidly and without deliberation (Gollwitzer, 1993). This kind of strategy has been shown to be effective in a range of selfcontrol contexts (Gollwitzer & Sheeran, 2006). In the context of implicit racial bias, we (Mendoza, Gollwitzer, & Amodio, 2010), for example, found that the use of implementation intentions for responding to the Shooter Task (e.g., if I see a Black person, then I will ignore his race in Study 1 or if I see a gun, then I will shoot; if I see an object, then I will not shoot in Study 2) completely eliminated racial bias in shooting behavior. Importantly, it did so by increasing participants’ accuracy in shoot decisions, thereby blocking the expression of bias in behavior. Similar results were observed by Stewart and Payne (2008), although their strategy manipulation and interpretation focused on altering a mental association (between Black and dangerous) rather than a behavioral response. EEG research by Amodio (2010) demonstrated the role of PFC activity in producing a controlled response on the weapons task, further supporting the idea that once the need for control is detected, the PFC can guide the implementation of intended behavior effectively, rapidly, and with little deliberation. Thus, although some brief interventions have proven to be effective immediately but not over time in changing implicit biases (e.g., Lai et al., 2016), other more sustained interventions may be more durable (Allport, 1954; Devine et al., 2012; Kawakami et al., 2000). Furthermore, strategies that focus on control and motivational factors may also be important to reducing bias in the long term. Notably, even interventions designed to eliminate associations in a specific context may be valuable tools for improving intergroup relations. For example, if the negative influence of social categorization processes can be reduced in an upcoming job-hiring situation, this short-term targeted intervention can have long-term and impactful consequences. Thus, despite initial claims that implicit biases are inevitable, these findings suggest that social categorization processes need not always have negative implications for outgroup members and that interventions can lead to momentary and potentially long-term improvements in intergroup relations (Devine et al., 2002).

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3. CONCLUSIONS The way we see and categorize another person can have profound implications for our social interactions and intergroup relations. In this chapter, we focused on two broad aspects of intergroup social cognition: how we perceive and categorize people according to their social categories, and how these categories then influence the way we judge them and interact. This growing body of work reveals that social perception operates through both bottom-up and top-down processes. That is, a broad range of visual cues from the face and body shape the way we initially perceive a person and bias the way in which we categorize them and form impressions. At the same time, a host of top-down effects, driven by our goals, expectancies, attitudes, and contextual cues, shape these bottom-up processes. The wealth of evidence for these bottom-up and top-down person perception processes reveals that ingroup advantages are not merely a product of categorization, but they also shape the categorization process itself. Once we categorize a person as a member of a social group, a broad set of influences—prejudices, stereotypes, and associations with the self—further shape the manner in which we respond to outgroup members. In the current framework, we bring together a diverse array of theoretical approaches and methodologies from a variety of fields to describe the many ways in which category-based processes can lead to bias, as well as the strategies that may be used to reduce these processes and their negative impact. Considered as a whole, our framework offers a comprehensive account of the perceptual and categorical processes that drive intergroup social cognition and relations, combining research from social psychology, neuroscience, and visual psychophysics. Although the interplay of bottom-up and top-down processes and their joint expressions in social behavior are extremely complex, efforts to understand them in an integrative, multidisciplinary, theoretical framework promises to advance our knowledge of social cognition while informing intervention strategies aimed at reducing bias in society.

REFERENCES Aberson, C. L., Porter, M. K., & Gaffney, A. M. (2008). Friendships influence Hispanic students’ implicit attitudes toward white non-Hispanics relative to African Americans. Hispanic Journal of Behavioral Sciences, 30, 544–556. Ackerman, J. M., Becker, D. V., Mortensen, C. R., Sasaki, T., Neuberg, S. L., & Kenrick, D. T. (2009). A pox on the mind: Disjunction of attention and memory in processing physical disfigurement. Journal of Experimental Social Psychology, 45, 478–485.

60

K. Kawakami et al.

Adams, R. B., Jr., Ambady, N., Nakayama, K., & Shimojo, S. (2010). The science of social vision. New York: Oxford University Press. Adams, R. B., Hess, U., & Kleck, R. E. (2015). The intersection of gender-related facial appearance and facial displays of emotion. Emotion Review, 7, 5–13. Adolphs, R. (2002). Recognizing emotion from facial expressions: Psychological and neurological mechanisms. Behavioral and Cognitive Neuroscience Reviews, 1, 21–62. Agerstrom, J., & Rooth, D. (2011). The role of automatic obesity stereotypes in real hiring discrimination. Journal of Applied Psychology, 96, 790–805. Alaei, R., & Rule, N. O. (2016). Accuracy of perceiving social attributes. In J. A. Hall, M. Schmid Mast, & T. V. West (Eds.), The social psychology of perceiving others accurately (pp. 125–142): Cambridge, United Kingdom: Cambridge University Press. Allport, G. W. (1954). The nature of prejudice. Washington, DC: Perseus. Allport, G. W., & Kramer, B. M. (1946). Some roots of prejudice. Journal of Psychology, 22, 9–39. Amodio, D. M. (2008). The social neuroscience of intergroup relations. European Review of Social Psychology, 19, 1–54. Amodio, D. M. (2009). Intergroup anxiety effects on the control of racial stereotypes: A psychoneuroendocrine analysis. Journal of Experimental Social Psychology, 45, 60–67. Amodio, D. M. (2010). Coordinated roles of motivation and perception in the regulation of intergroup responses: Frontal cortical asymmetry effects on the P2 event-related potential and behavior. Journal of Cognitive Neuroscience, 22, 2609–2617. Amodio, D. M. (2014a). The neuroscience of prejudice and stereotyping. Nature Reviews Neuroscience, 15, 670–682. Amodio, D. M. (2014b). Dual experiences, multiple processes: Looking beyond dualities for mechanisms of the mind. In J. S. Sherman, B. Gawronski, & Y. Trope (Eds.), Dual process theories of the social mind (pp. 560–576). New York: Guilford Press. Amodio, D. M., & Bartholow, B. D. (2011). Event-related potential methods in social cognition. In A. Voss, C. Stahl, & C. Klauer (Eds.), Cognitive methods in social psychology (pp. 303–339). New York: Guilford Press. Amodio, D. M., Bartholow, B. D., & Ito, T. A. (2014). Tracking the dynamics of the social brain: ERP approaches for social cognitive and affective neuroscience. Social Cognitive and Affective Neuroscience, 9, 385–393. Amodio, D. M., & Devine, P. G. (2006). Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior. Journal of Personality and Social Psychology, 91, 652–661. Amodio, D. M., & Devine, P. G. (2008). On the interpersonal functions of implicit stereotyping and evaluative race bias: Insights from social neuroscience. In R. E. Petty, R. H. Fazio, & P. Brin˜ol (Eds.), Attitudes: Insights from the new wave of implicit measures (pp. 193–226). Hillsdale, NJ: Erlbaum. Amodio, D. M., & Devine, P. G. (2010). Regulating behavior in the social world: Control in the context of intergroup bias. In R. R. Hassin, K. N. Ochsner, & Y. Trope (Eds.), Self control in society, mind and brain (pp. 49–75). New York: Oxford University Press. Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2007). A dynamic model of guilt: Implications for motivation and self-regulation in the context of prejudice. Psychological Science, 18, 524–530. Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2008). Individual differences in the regulation of intergroup bias: The role of conflict monitoring and neural signals for control. Journal of Personality and Social Psychology, 94, 60–74. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7, 268–277. Amodio, D. M., & Hamilton, H. K. (2012). Intergroup anxiety effects on implicit racial evaluation and stereotyping. Emotion, 12, 1273–1280.

Intergroup Perception and Cognition

61

Amodio, D. M., Harmon-Jones, E., & Devine, P. G. (2003). Individual differences in the activation and control of affective race bias as assessed by startle eyeblink responses and self-report. Journal of Personality and Social Psychology, 84, 738–753. Amodio, D. M., Harmon-Jones, E., Devine, P. G., Curtin, J. J., Hartley, S. L., & Covert, A. E. (2004). Neural signals for the detection of unintentional race bias. Psychological Science, 15, 88–93. Amodio, D. M., Jost, J. T., Master, S. L., & Yee, C. M. (2007). Neurocognitive correlates of liberalism and conservatism. Nature Neuroscience, 10, 1246–1247. Amodio, D. M., Kubota, J. T., Harmon-Jones, E., & Devine, P. G. (2006). Alternative mechanisms for regulating racial responses according to internal vs. external cues. Social Cognitive and Affective Neuroscience, 1, 26–36. Amodio, D. M., & Mendoza, S. A. (2010). Implicit intergroup bias: Cognitive, affective, and motivational underpinnings. In B. Gawronski & B. K. Payne (Eds.), Handbook of implicit social cognition (pp. 353–374). New York: Guilford. Amodio, D. M., & Ratner, K. G. (2011). A memory systems model of implicit social cognition. Current Directions in Psychological Science, 20, 143–148. Apfelbaum, E. P., Sommers, S. R., & Norton, M. I. (2008). Seeing race and seeming racist? Evaluating strategic colorblindness in social interaction. Journal of Personality and Social Psychology, 95, 918–932. Argyle, M., & Cook, M. (1976). Gaze and mutual gaze. Cambridge, UK: Cambridge University Press. Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of the other in the self scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63, 596–612. Aronson, E., Blaney, N. T., Stephan, C., Rosenfeld, R., & Sikes, J. (1977). Interdependence in the classroom: A field study. Journal of Educational Psychology, 69, 121–128. Aronson, E., & Bridgeman, D. (1979). Jigsaw groups and the desegregated classroom: In pursuit of common goals. Personality and Social Psychology Bulletin, 5, 438–446. Ashburn-Nardo, L., Knowles, M. L., & Monteith, M. J. (2003). Black Americans’ implicit racial associations and their implications for intergroup judgment. Social Cognition, 21, 61–87. Balcetis, E., & Dunning, D. (2006). See what you want to see: Motivational influences on visual perception. Journal of Personality and Social Psychology, 91, 612–625. Balcetis, E., & Lassiter, G. D. (2010). Social psychology of visual perception. New York: Psychology Press. Baldwin, M., Keefer, L. A., Gravelin, C. R., & Biernat, M. (2013). Perceived importance of cross-race targets facilitate recall: Support for a motivated account of race memory. Group Processes & Intergroup Relations, 16, 505–515. Bar, M., Kassam, K., Ghuman, A., Boshyan, J., Dale, A., H€am€al€ainen, M., et al. (2006). Topdown facilitation of visual recognition. Proceedings of the National Academy of Sciences, 103, 449–454. Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004). Contextual moderation of racial bias: The impact of social roles on controlled and automatically activated attitudes. Journal of Personality and Social Psychology, 87, 5–22. Bargh, J. A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71, 230–244. Bargh, J. A., & Williams, L. E. (2006). The automaticity of social life. Current Directions in Psychological Science, 15, 1–4. Baron-Cohen, S., Wheelwright, S., & Jolliffe, T. (1997). Is there a “language of the eyes”? Evidence from normal adults, and adults with autism or Asperger syndrome. Visual Cognition, 4, 311–331.

62

K. Kawakami et al.

Bartholow, B. D., Dickter, C. L., & Sestir, M. A. (2006). Stereotype activation and control of race bias: Cognitive control of inhibition and its impairment by alcohol. Journal of Personality and Social Psychology, 90, 272–287. Barton, J. J. S., Press, D. Z., Keenan, J. P., & O’Connor, M. (2002). Lesions of the fusiform area impair perception of facial configuration in prosopagnosia. Neurology, 58, 71–78. Baumeister, R. F., Vohs, K. D., & Funder, D. C. (2007). Psychology as the science of selfreports and finger movements: Whatever happened to actual behavior? Perspectives on Psychological Science, 2, 396–403. Bean, M., Slaten, D., Horton, W., Murphy, M., Todd, A., & Richeson, J. (2012). Prejudice concerns and race-based attentional bias: New evidence from Eye tracking. Social Psychological and Personality Science, 3, 722–729. Becker, D. V., Kenrick, D. T., Neuberg, S. L., Blackwell, K. C., & Smith, D. M. (2007). The confounded nature of angry men and happy women. Journal of Personality and Social Psychology, 92, 179–190. Beer, J. S., Stallen, M., Lombardo, M. V., Gonsalkorale, K., Cunningham, W. A., & Sherman, J. W. (2008). The quadruple process model approach to examining the neural underpinnings of prejudice. NeuroImage, 43, 775–783. Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996). Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience, 8, 551–565. Bernard, P., Gervais, S. J., Allen, J., Campomizzi, S., & Klein, O. (2012). Integrating sexual objectification with object versus person recognition: The sexualized-body-inversion hypothesis. Psychological Science, 23, 469–471. Bernieri, F. J. (1988). Coordinated movement and rapport in teacher–student interactions. Journal of Nonverbal Behavior, 12, 120–138. Bernstein, M. J., Young, S. G., Brown, C. M., Sacco, D. F., & Claypool, H. M. (2008). Adaptive responses to social exclusion: Social rejection improves detection of real and fake smiles. Psychological Science, 19, 981–983. Bernstein, M. J., Young, S. G., & Hugenberg, K. (2007). The cross-category effect: Mere social categorization is sufficient to elicit an own-group bias in face recognition. Psychological Science, 18, 709–712. Berry, D. S., & McArthur, L. Z. (1986). Perceiving character in faces: The impact of agerelated craniofacial changes on social perception. Psychological Bulletin, 100, 3–18. Bijlstra, G., Holland, R. W., Dotsch, R., Hugenberg, K., & Wigboldus, D. H. J. (2014). Stereotype associations and emotion recognition. Personality and Social Psychology Bulletin, 40, 567–577. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Blair, I. V., & Banaji, M. R. (1996). Automatic and controlled processes in stereotype priming. Journal of Personality and Social Psychology, 70, 1142–1163. Blair, I. V., Judd, C. M., & Chapleau, K. M. (2004). The influence of Afrocentric facial features in criminal sentencing. Psychological Science, 15, 674–679. Blair, I. V., Judd, C. M., Sadler, M. S., & Jenkins, C. (2002). The role of Afrocentric features in person perception: Judging by features and categories. Journal of Personality and Social Psychology, 83, 5–25. Bosson, J. K., Swann, W. B., Jr., & Pennebaker, J. (2000). Stalking the perfect measure of implicit self-esteem: The blind men and the elephant revisited? Journal of Personality and Social Psychology, 79, 631–643. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652. Botvinick, M., & Cohen, J. (1998). Rubber hands “feel” touch that eyes see. Nature, 391, 756. Brauer, M., & Er-rafiy, A. (2011). Increasing perceived variability reduces prejudice and discrimination. Journal of Experimental Social Psychology, 47, 871–877.

Intergroup Perception and Cognition

63

Brauer, M., Er-rafiy, A., Kawakami, K., & Phills, C. E. (2012). Describing a group in positive terms reduces prejudice less effectively than describing it in positive and negative terms. Journal of Experimental Social Psychology, 48, 757–761. Brewer, M. B. (1988). A dual-process model of impression formation. In T. Srull & R. Wyer (Eds.), Advances in social cognition: Vol. 1. (pp. 1–36). Hillsdale, NJ: Erlbaum. Brewer, M. B., & Silver, M. (1978). Ingroup bias as a function of task characteristics. European Journal of Social Psychology, 8, 393–400. Brin˜ol, P., & Petty, R. E. (2012). The history of attitudes and persuasion research. In A. Kruglanski & W. Stroebe (Eds.), Handbook of the history of social psychology (pp. 285–320). New York: Psychology Press. Brody, L. R., & Hall, J. A. (2000). Gender, emotion, and expression. In M. Lewis & J. M. Haviland (Eds.), Handbook of emotions (2nd ed., pp. 447–460). New York, NY: Guilford Press. Bruner, J. S., & Postman, L. (1949). On the perception of incongruity: A paradigm. Journal of Personality, 18, 206–223. Butz, D., & Yogeeswaran, K. (2011). A new threat in the air: Macroeconomic threat increases prejudice against Asian Americans. Journal of Experimental Social Psychology, 47, 22–27. Caldara, R., Rossion, B., Bovet, P., & Hauert, C. (2004). Event-related potentials and time course of the “other-race” face classification advantage. NeuroReport, 15, 905. Caldara, R., Thut, G., Servoir, P., Michel, C., Bovet, P., & Renault, B. (2003). Face versus non-face object perception and the “other-race” effect: A spatio-temporal ERP study. Clinical Neurophysiology, 114, 515–528. Campbell, D. T. (1965). Ethnocentric and other altruistic motives. Lincoln, NE: University of Nebraska Press. Can˜adas, E., Lupia´n˜ez, J., Kawakami, K., Niedenthal, P. M., & Rodrı´guez-Bailo´n, R. (2015). Perceiving emotions: Cueing social categorization processes and attentional control through facial expressions. Cognition and Emotion, 30, 1149–1163. Can˜adas, E., Rodrı´guez-Bailo´n, R., Milliken, B., & Lupia´n˜ez, J. (2013). Social Categories as a context for the allocation of attentional control. Journal of Experimental Psychology: General, 142, 934–943. Cappella, J. N., & Panalp, S. (1981). Talk and silence sequences in informal conversations: III Interspeaker influence. Human Communication Research, 7, 117–132. Cassidy, B. S., Krendl, A. C., Stanko, K. A., Rydell, R. J., Hugenberg, K., & Young, S. G. (under review). Configural face processing impacts race disparities in humanization and trust. Unpublished manuscript. Cesario, J., Grant, H., & Higgins, E. T. (2004). Regulatory fit and persuasion: Transfer from “feeling right.” Journal of Personality and Social Psychology, 86, 388–404. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-behaviorlink and social interaction. Journal of Personality and Social Psychology, 76, 893–910. Chartrand, T. L., Maddux, W. W., & Lakin, J. K. (2005). Beyond the perception-behavior link: The ubiquitous utility and motivational mediators of nonconscious mimicry. In R. R. Hassin, J. S. Uleman, & J. A. Bargh (Eds.), The new unconscious (pp. 334–361). New York: Oxford University Press. Chen, M., & Bargh, J. A. (1997). Nonconscious behavioral confirmation processes: The selffulfilling consequences of automatic stereotype activation. Journal of Experimental Social Psychology, 33, 541–560. Cialdini, R. B., Borden, R. J., Thorne, A., Walker, M. R., Freeman, S., & Sloan, L. R. (1976). Basking in reflected glory: Three (football) field studies. Journal of Personality and Social Psychology, 34, 366–375. Cialdini, R. B., & Richardson, K. D. (1980). Two indirect tactics of image management: Basking and blasting. Journal of Personality and Social Psychology, 39, 406–415.

64

K. Kawakami et al.

Cikara, M., Botvinick, M. M., & Fiske, S. T. (2011). Us versus them: Social identity shapes neural responses to intergroup competition and harm. Psychological Science, 22, 306–313. Cikara, M., Bruneau, E. G., Van Bavel, J. J., & Saxe, R. (2014). Their pain gives us pleasure: How intergroup dynamics shape empathic failures and counter-empathic responses. Journal of Experimental Social Psychology, 55, 110–125. Cikara, M., & Fiske, S. T. (2011). Bounded empathy: Neural responses to ougroup targets’ (mis)fortunes. Journal of Cognitive Neuroscience, 23, 3791–3803. Cikara, M., & Fiske, S. T. (2013). Their pain, our pleasure: Stereotype content and schadenfreude. Annals of New York Academy of Sciences, 1299, 52–59. Cloutier, J., Mason, M. F., & Macrae, C. N. (2005). The perceptual determinants of person construal: Reopening the social-cognitive toolbox. Journal of Personality and Social Psychology, 88, 885–894. Contreras, J. M., Banaji, M. R., & Mitchell, J. P. (2012). Dissociable neural correlates of stereotypes and other forms of semantic knowledge. Social Cognitive and Affective Neuroscience, 7, 764–770. Correll, J., & Park, B. (2005). A model of the ingroup as a social resource. Personality and Social Psychology Review, 9, 341–359. Correll, J., Urland, G., & Ito, T. (2006). Event-related potentials and the decision to shoot: The role of threat perception and cognitive control. Journal of Experimental Social Psychology, 42, 120–128. Cox, W. T. L., Devine, P. G., Bischmann, A. A., & Hyde, J. (2015). Inferences about sexual orientation: The role of stereotypes, faces, and the gaydar myth. Journal of Sex Research, 53, 157–171. Craemer, T., Shaw, T. C., Edwards, C., & Jefferson, H. (2013). ‘Race still matters, however…’: Implicit identification with blacks, pro-black policy support and the Obama candidacy. Ethnic and Racial Studies, 36, 1047–1069. Crandall, C. S., Eshleman, A., & O’Brien, L. (2002). Social norms and the expression and suppression of prejudice: The struggle for internalization. Journal of Personality and Social Psychology, 82, 359–378. Cutting, J. E. (1978). Generation of synthetic male and female walkers through manipulation of a biomechanical invariant. Perception, 7, 393–405. Cvencek, D., Greenwald, A. G., & Meltzoff, A. N. (2012). Balanced identity theory: Review of evidence for implicit consistency in social cognition. In B. Gawronski, F. Strack, B. Gawronski, & F. Strack (Eds.), Cognitive consistency: A fundamental principle in social cognition (pp. 157–177). New York, NY: Guilford Press. Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40, 642–658. Dasgupta, N., & Greenwald, A. G. (2001a). Exposure to admired group members reduces automatic intergroup bias. Journal of Personality and Social Psychology, 81, 800–814. Dasgupta, N., & Greenwald, A. G. (2001b). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. Dasgupta, N., & Rivera, L. M. (2008). When social context matters: The influence of longterm contact and short-term exposure to admired group members on implicit attitudes and behavioral intentions. Social Cognition, 26, 112–123. DeLozier, S., & Rhodes, M. G. (2015). The impact of value-directed remembering on the own-race bias. Acta Psychologica, 154, 62–68. Deska, J. D., Lloyd, E. P., & Hugenberg, K. (2016). Advancing our understanding of the interface between perception and intergroup relations. Psychological Inquiry, 27, 286–289. Deska, J. D., Lloyd, E. P., & Hugenberg, K. (under review-a). Facing humanness: Facial width-to-height ratio predicts ascriptions of humanness. Invited for resubmission to Journal of Personality and Social Psychology. Unpublished manuscript.

Intergroup Perception and Cognition

65

Deska, J. D., Lloyd, E. P., & Hugenberg, K. (under review-b). Facial width-to-height ratio influences the extraction of anger and fear signals. Unpublished manuscript. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Devine, P. G., Forscher, P. S., Austin, A. T., & Cox, W. T. L. (2012). Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. Journal of Experimental Social Psychology, 48, 1267–1278. Devine, P. G., & Monteith, M. J. (1993). The role of discrepancy associated affect in prejudice reduction. In D. M. Macke & D. L. Hamilton (Eds.), Affect cognition and stereotyping: Interactive processes in intergroup perception (pp. 317–344). San Diego, FL: Academic Press. Devine, P. G., Plant, E. A., Amodio, D. M., Harmon-Jones, E., & Vance, S. L. (2002). The regulation of explicit and implicit race bias: The role of motivations to respond without prejudice. Journal of Personality and Social Psychology, 82, 835–848. Donders, N. C., Correll, J., & Wittenbrink, B. (2008). Danger stereotypes predict racially biased attentional location. Journal of Experimental Social Psychology, 44, 1328–1333. Dotsch, R., Wigboldus, D. H., Langner, O., & van Knippenberg, A. (2008). Ethnic outgroup faces are biased in the prejudiced mind. Psychological Science, 19, 978. Dovidio, J., Brigham, J., Johnson, B., & Gaertner, S. (1996). Stereotyping, prejudice, and discrimination: Another look. In N. Macrae, C. Stangor, & M. Hewstone (Eds.), Stereotypes and stereotyping (pp. 276–319). New York: Guilford Press. Dovidio, J. F., Gaertner, S. L., & Kawakami, K. (2003). Intergroup contact: The past, present and the future. Group Processes & Intergroup Relations, 6, 5–21. Dovidio, J. F., Gaertner, S. L., & Kawakami, K. (2010). Racism. In J. F. Dovidio, M. Hewstone, P. Glick, & V. M. Esses (Eds.), Handbook of prejudice, stereotyping, and discrimination (pp. 312–327). Thousand Oaks, CA: Sage. Dovidio, J. F., Gaertner, S. L., Kawakami, K., & Hodson, G. (2002). Why can’t we just get along? Interpersonal biases and interracial distrust. Cultural Diversity & Ethnic Minority Psychology, 8, 88–102. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Dovidio, J. F., Kawakami, K., Smoak, N., & Gaertner, S. L. (2009). The roles of implicit and explicit processes in contemporary prejudice. In R. E. Petty, R. H. Fazio, & P. Brin˜ol (Eds.), Attitudes: Insights from the new implicit measures (pp. 165–192). New York: Psychology Press. Dunham, Y. (2011). An angry ¼ outgroup effect. Journal of Experimental Social Psychology, 47, 668–671. Dunham, Y., Stepanova, E. V., Dotsch, R., & Todorov, A. (2015). The development of race-based perceptual categorization: Skin color dominates early category judgments. Developmental Science, 18, 469–483. Dunn, E. W., & Ashton-James, C. E. (2008). On emotional innumeracy: Predicted and actual affective responses to grand-scale tragedies. Journal of Experimental Social Psychology, 44, 692–698. Dunning, D., & Balcetis, E. (2013). Wishful seeing: How preferences shape visual perception. Current Directions in Psychological Science, 22, 33–37. Eagly, A. H., & Mladinic, A. (1994). Are people prejudiced against women? Some answers from research on attitudes, gender stereotypes, and judgments of competence. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology: Vol. 5 (pp. 1–35). New York: Wiley.

66

K. Kawakami et al.

Eberhardt, J. L., Dasgupta, N., & Banaszynski, T. L. (2003). Believing is seeing: The effects of racial labels and implicit beliefs on face perception. Personality and Social Psychology Bulletin, 29, 360–370. Eberhardt, J. L., Davies, P. G., Purdie-Vaughns, V. J., & Johnson, S. L. (2006). Looking deathworthy: Perceived stereotypicality of black defendants predicts capital-sentencing outcomes. Psychological Science, 17, 383–386. Ebner, N. C., He, Y., Fichtenholtz, H. M., McCarthy, G., & Johnson, M. K. (2011). Electrophysiological correlates of processing faces of younger and older individuals. Social Cognitive and Affective Neuroscience, 6, 526–535. Elfenbein, H. A., & Ambady, N. (2002). On the universality and cultural specificity of emotion recognition: A meta-analysis. Psychological Bulletin, 128, 203–235. Elfenbein, H. A., Beaupre, M., Levesque, M., & Hess, U. (2007). Toward a dialect theory: Cultural differences in the expression and recognition of posed facial expressions. Emotion, 7, 131–146. Esses, V. M., Haddock, G., & Zanna, M. P. (1993). Values, stereotypes, and emotions as determinants of intergroup attitudes. In D. M. Mackie & D. L. Hamilton (Eds.), Affect, cognition and stereotyping: Interactive processes in group perception (pp. 137–166). San Diego: Academic Press. Esses, V. M., Jackson, L. M., & Armstrong, T. L. (1998). Intergroup competition and attitudes toward immigrants and immigration: An instrumental model of group conflict. Journal of Social Issues, 54, 699–724. Fabes, R. A., & Martin, C. L. (1991). Gender and age stereotypes of emotionality. Personality and Social Psychology Bulletin, 17, 532–540. Fairhall, S. L., & Ishai, A. (2007). Effective connectivity within the distributed cortical network for face perception. Cerebral Cortex, 17, 2400–2406. Farmer, H., Tajadura-Jimenez, A., & Tsakiris, M. (2012). Beyond the color of my skin: How skin color affects the sense of body-ownership. Consciousness and Cognition, 21, 1242–1256. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Festinger, L. (1957). A theory of cognitive dissonance. California: Stanford University Press. Fincher, K. M., & Tetlock, P. E. (2016). Perceptual dehumanization of faces is activated by norm violations and facilitates norm enforcement. Journal of Experimental Psychology: General, 145, 131–146. Firestone, C., & Scholl, B. J. (in press). Cognition does not affect perception: Evaluating the evidence for ‘top-down’ effects. Behavioral and Brain Sciences. Fishbach, A., Dhar, R., & Zhang, Y. (2006). Subgoals as substitutes or complements: The role of goal accessibility. Journal of Personality and Social Psychology, 91, 232–242. Fishbach, A., & Zhang, Y. (2008). Together or apart: When goals and temptations complement versus compete. Journal of Personality and Social Psychology, 94, 547–559. Fiske, S. T. (1998). Stereotyping, prejudice, and discrimination. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology: Vol. 2 (4th ed., pp. 357-411). New York: McGraw-Hill. Fiske, S. T., & Neuberg, S. L. (1990). A continuum of impression formation, from categorybased to individuating processes: Influences of information and motivation on attention and interpretation. In J. M. Olson & M. P. Zanna (Eds.), Advances in experimental social psychology: Vol. 44 (pp. 1–74). New York: Academic Press. Freeman, J. B., & Ambady, N. (2011). A dynamic interactive theory of person construal. Psychological Review, 118, 247–279.

Intergroup Perception and Cognition

67

Freeman, J., Ambady, N., & Holcomb, P. (2010). The face-sensitive N170 encodes social category information. NeuroReport, 21, 24–28. Freeman, J. B., & Johnson, K. L. (2016). More than meets the eye: Split-second social perception. Trends in Cognitive Sciences, 20, 362–374. Freeman, J. B., Johnson, K. L., Ambady, N., & Rule, N. O. (2010). Sexual orientation perception involves gendered facial cues. Personality and Social Psychology Bulletin, 36, 1318–1331. Freeman, J. B., Penner, A. M., Saperstein, A., Scheutz, M., & Ambady, N. (2011). Looking the part: Social status cues shape race perception. PLoS One, 6, e25107. French, A. R., Franz, T. M., Phelan, L. L., & Blaine, B. E. (2013). Reducing Muslim/Arab stereotypes through evaluative conditioning. The Journal of Social Psychology, 153, 6–9. Fridlund, A. J. (1994). Human facial expression: An evolutionary view. San Diego, CA: Academic Press. Friesen, J., Kawakami, K., Caprara, R., Sidhu, D., Hugenberg, K., Rodriguez-Bailo´n, R., et al. (under review). Emotion identification bias: Impact of attention to the eyes on decoding true and false smiles on ingroup and outgroup faces. Unpublished manuscript. Frischen, A., Bayliss, A. P., & Tipper, S. P. (2007). Gaze cueing of attention: Visual attention, social cognition, and individual differences. Psychological Bulletin, 133, 694–724. Gabriel, S., Kawakami, K., Bartak, C., Kang, S., & Mann, N. (2010). Negative selfsynchronization: Will I change to be like you when it is bad for me? Journal of Personality and Social Psychology, 98, 857–871. Gaertner, S. L., & Dovidio, J. F. (2000). Reducing intergroup bias: The common ingroup identity model. Philadelphia: Psychology Press. Galinksy, A. D., & Moskowitz, G. B. (2000). Perspective-taking: Decreasing stereotype expression, stereotype accessibility, and ingroup-favoritism. Journal of Personality and Social Psychology, 78, 708–724. Galinsky, A. D., Ku, G., & Wang, C. S. (2005). Perspective-taking and self-other overlap: Fostering social bonds and facilitating social coordination. Group Processes & Intergroup Relations, 8, 109–124. Galinsky, A. D., Wang, C. S., & Ku, G. (2008). Perspective-takers behave more stereotypically. Journal of Personality and Social Psychology, 95, 404–419. Gallate, J., Wong, C., Ellwood, S., Chi, R., & Snyder, A. (2011). Noninvasive brain stimulation reduces prejudice scores on an implicit association test. Neuropsychology, 25, 185–192. Gauthier, I., Curran, T., Curby, K. M., & Collins, D. (2003). Perceptual interference supports a non-modular account of face processing. Nature Neuroscience, 6, 428–523. Gauthier, I., Tarr, M. J., Moylan, J., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). The fusiform “face area” is part of a network that processes faces at the individual level. Journal of Cognitive Neuroscience, 12, 495–504. Gawronski, B., Deutsch, R., Mbirkou, S., Seibt, B., & Strack, F. (2008). When “Just Say No” is not enough: Affirmative versus negation training and the reduction of automatic stereotype activation. Journal of Experimental Social Psychology, 44, 370–377. Gawronski, B., & Payne, K. B. (Eds.), (2011). Handbook of implicit social cognition: Measurement, theory, and applications. New York: Guildford Press. Gawronski, B., & Strack, F. (Eds.), (2012). Cognitive consistency: A fundamental principle in social cognition. New York, NY: Guilford Press. Gilbert, S. J., Swencionis, J. K., & Amodio, D. M. (2012). Evaluative vs. trait representation in intergroup social judgments: Distinct roles of anterior temporal lobe and prefrontal cortex. Neuropsychologia, 50, 3600–3611. Goff, P. A., Eberhardt, J. L., Williams, M. J., & Jackson, M. C. (2008). Not yet human: Implicit knowledge historical dehumanization and contemporary consequences. Journal of Personality and Social Psychology, 94, 292–306.

68

K. Kawakami et al.

Golby, A. J., Gabrieli, J. D. E., Chiao, J. Y., & Eberhardt, J. L. (2001). Differential fusiform responses to same- and other-race faces. Nature Neuroscience, 4, 845–850. Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. European Review of Social Psychology, 4, 141–185. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 69–119): Vol. 38. New York: Academic Press. Gonsalkorale, K., Sherman, J. W., Allen, T. J., Klauer, K. C., & Amodio, D. M. (2011). Accounting for successful control of implicit racial bias: The roles of association activation, response monitoring, and overcoming bias. Personality and Social Psychology Bulletin, 37, 1534–1545. Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of mind perception. Science, 315, 619. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellot, D. S. (2002). A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychological Review, 109, 3–25. Greenwald, A. G., & Farnham, S. D. (2000). Using the implicit association test to measure self-esteem and self-concept. Journal of Personality and Social Psychology, 79, 1022–1038. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R. (2009). Understanding and using the implicit association test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Greenwald, A. G., Smith, C. T., Sriram, N., Bar-Anan, Y., & Nosek, B. A. (2009). Implicit race attitudes predicted vote in the 2008 U. S. Presidential election. Analyses of Social Issues and Public Policy, 9, 241–253. Grill-Spector, K., Knouf, N., & Kanwisher, N. (2004). The fusiform face area subserves face perception, not generic within-category identification. Nature Neuroscience, 7, 555–562. Halberstadt, J., Sherman, S. J., & Sherman, J. W. (2011). Why Barack Obama is black: A cognitive account of hypodescent. Psychological Science, 22, 29–33. Hall, J. A., & Halberstadt, A. G. (1994). “Subordination” and sensitivity to nonverbal cues: A study of married working women. Sex Roles, 31, 3–7. Hanson, S. J., Matsuka, T., & Haxby, J. V. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: Is there a “face” area? NeuroImage, 23, 156–166. Hardin, C. D., & Banaji, M. R. (2013). The nature of implicit prejudice: Implications for personal and public policy. In E. Shafir (Ed.), The behavioral foundations of public policy (pp. 13–31): Princeton, NJ: Princeton University Press. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs. ingroup face stimuli. NeuroReport, 11, 2351–2355. Haslam, N. (2006). Dehumanization: An integrative review. Personality and Social Psychology Review, 10, 252–264. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303, 1634–1640. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects ventral temporal cortex. Science, 287, 643–646.

Intergroup Perception and Cognition

69

Haxby, J., Hoffman, E., & Gobbini, M. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4, 223–232. He, Y., Johnson, M., Dovidio, J., & McCarthy, G. (2009). The relation between race-related implicit associations and scalp-recorded neural activity evoked by faces from different races. Social Neuroscience, 4, 426–442. Hein, G., Silani, G., Preuschoff, K., Batson, C. D., & Singer, T. (2010). Neural responses to ingroup and outgroup members’ suffering predict individual differences in costly helping. Journal of Personality and Social Psychology, 79, 1022–1038. Herek, G. M., & Capitanio, J. P. (1996). “Some of my best friends”: Intergroup contact, concealable stigma, and heterosexuals’ attitudes toward gay men and lesbians. Personality and Social Psychology Bulletin, 22, 412–424. Hess, U., Adams, R. B., Jr., & Kleck, R. E. (2004). Dominance, gender and emotion expression. Emotion, 4, 378–388. Hess, U., Adams, R. B., Jr., & Kleck, R. E. (2005). Who may frown and who should smile? Dominance, affiliation, and the display of happiness and anger. Cognition and Emotion, 19, 515–536. Ho, A. K., Roberts, S. O., & Gelman, S. A. (2015). Essentialism and racial bias jointly contribute to the categorization of multiracial individuals. Psychological Science, 26, 1639–1645. Ho, A. K., Sidanius, J., Cuddy, A. J. C., & Banaji, M. R. (2013). Status-boundary enforcement and the categorization of Black-White biracials. Journal of Experimental Social Psychology, 49, 940–943. Ho, A. K., Sidanius, J., Levin, D. T., & Banaji, M. R. (2011). Evidence for hypodescent and racial hierarchy in the categorization and perception of biracial individuals. Journal of Personality and Social Psychology, 100, 492–506. Hoffman, E. A., & Haxby, J. V. (2000). Distinct representations of eye gaze and identity in the distributed human neural system for face perception. Nature Neuroscience, 3, 80–84. Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, N. (2016). Raical bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences, 113, 4296–4301. Hofmann, W., De Houwer, J., Perugini, M., Baeyens, F., & Crombez, G. (2010). Evaluative conditioning in humans: A meta-analysis. Psychological Bulletin, 136, 390–421. Holland, P. C., & Gallagher, M. (1999). Amygdala circuitry in attentional and representational processes. Trends in Cognitive Sciences, 3, 65–73. Holoien, D. S., Bergsieker, H. B., Shelton, J. N., & Alegre, J. M. (2015). Do you really understand? Achieving accuracy in interracial relationships. Journal of Personality and Social Psychology, 108, 76–92. Hugenberg, K. (2005). Social categorization and the perception of facial affect: Target race moderates the response latency advantage for happy faces. Emotion, 5, 267–276. Hugenberg, K., & Bodenhausen, G. V. (2003). Facing prejudice: Implicit prejudice and the perception of facial threat. Psychological Science, 14, 640–643. Hugenberg, K., & Bodenhausen, G. V. (2004). Ambiguity in social categorization: The role of prejudice and facial affect in race categorization. Psychological Science, 15, 342–345. Hugenberg, K., & Corneille, O. (2009). Holistic processing is tuned for in-group faces. Cognitive Science, 33, 1173–1181. Hugenberg, K., Miller, J., & Claypool, H. (2007). Categorization and individuation in the cross race recognition deficit: Toward a solution for an insidious problem. Journal of Experimental Social Psychology, 43, 334–340. Hugenberg, K., & Wilson, J. P. (2013). Faces are central to social cognition. In D. Carlston (Ed.), The Oxford handbook of social cognition. New York: Oxford University Press. Hugenberg, K., Wilson, J. P., See, P. E., & Young, S. G. (2013). Toward a synthetic model of Own group biases in face memory. Visual Cognition, 21, 1392–1417.

70

K. Kawakami et al.

Hugenberg, K., Young, S., Bernstein, M., & Sacco, D. F. (2010). The categorization individuation model: An integrative account of the cross race recognition deficit. Psychological Review, 117, 1168–1187. Hugenberg, K., Young, S. G., Rydell, R. J., Almaraz, S. M., Stanko, K. A., See, P. E., et al. (2016). The face of humanity: Configural face processing influences ascriptions of humanness. Social Psychological and Personality Science, 7, 167–175. Hutchings, P. B., & Haddock, G. (2008). Look Black in anger: The role of implicit prejudice in the categorization and perceived emotional intensity of racially ambiguous faces. Journal of Experimental Social Psychology, 44, 1418–1420. Ito, T. A., & Urland, G. R. (2003). Race and gender on the brain: Electrocortical measures of attention to the race and gender of multiply categorizable individuals. Journal of Personality and Social Psychology, 85, 616–626. Ito, T. A., & Urland, G. R. (2005). The influence of processing objectives on the perceptions of faces: An ERP study of race and gender perception. Cognitive, Affective, & Behavioral Neuroscience, 5, 21–36. Izard, C. E. (1971). The face of emotion. New York: Appleton-Century-Crofts. Jacques, C., & Rossion, B. (2007). Electrophysiological evidence for temporal dissociation between spatial attention and sensory competition during human face processing. Cerebral Cortex, 17, 1055–1065. Johnson, K. L., & Adams, R. B., Jr. (2013). Social vision: An introduction. Social Cognition, 31, 633–635. Johnson, K. L., Freeman, J. B., & Pauker, K. (2012). Race is gendered: How covarying phenotypes and stereotypes bias sex categorization. Journal of Personality and Social Psychology, 102, 116–131. Johnson, K. L., Gill, S., Reichman, V., & Tassinary, L. G. (2007). Swagger, sway, and sexuality: Judging sexual orientation from body motion and morphology. Journal of Personality and Social Psychology, 93, 321–334. Johnson, K. L., & Iida, M. (2013). Person (mis)perception: On the functional biases that derail construal of others. In K. L. Johnson & M. Shiffrar (Eds.), People watching: Social, perceptual, and neurophysiological studies of body perception (pp. 203–219). New York: Oxford University Press. Johnson, K. L., Lick, D. J., & Carpinella, C. M. (2015). Emergent research in social vision: An integrated approach to the determinants and consequences of social categorization. Social and Personality Psychology Compass, 9, 15–30. Johnson, K. L., Pollick, F., & McKay, L. (2010). Social constraints on the visual perception of biological motion. In R. B. Adams, N. Ambady, K. Nakayama, & S. Shimojo (Eds.), Social vision. New York: Oxford University Press. Johnson, K. L., & Tassinary, L. G. (2005). Perceiving sex directly and indirectly: Meaning in motion and morphology. Psychological Science, 16, 890–897. Johnson, K. L., & Tassinary, L. G. (2007). Compatibility of basic social perceptions determines perceived attractiveness. Proceedings of the National Academy of Sciences, 104, 5246–5251. Kang, S. K., & Chasteen, A. L. (2009). Beyond the double-jeopardy hypothesis: Assessing emotion on the faces of multiply-categorizable targets of prejudice. Journal of Experimental Social Psychology, 45, 1281–1285. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience, 17, 4302–4311. Karmali, F., Kawakami, K., & Page-Gould, E. (under review). Psychophysiological responses to racism. Invited for resubmission to Journal of Experimental Psychology: General. Unpublished manuscript.

Intergroup Perception and Cognition

71

Karmali, F., Kawakami, K., & Shim, J. (under review). Responding to targets and perpetrators of racism. Unpublished manuscript. Kawakami, K. (2014). Studying intergroup biases: Where to begin and where to end? In K. Kawakami (Ed.), Vol. 1. The psychology of prejudice (Four volume set) (pp. xix–xlii). London: Sage. Kawakami, K., Dion, K. L., & Dovidio, J. F. (1998). Racial prejudice and stereotype activation. Personality and Social Psychology Bulletin, 24, 407–416. Kawakami, K., & Dovidio, J. F. (2001). The reliability of implicit stereotyping. Personality and Social Psychology Bulletin, 27, 212–225. Kawakami, K., Dovidio, J. F., & Dijksterhuis, A. (2003). Effect of social category priming on personal attitudes. Psychological Science, 14, 315–319. Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training in the negation of stereotypic associations on stereotype activation. Journal of Personality and Social Psychology, 78, 871–888. Kawakami, K., Dovidio, J. F., & van Kamp, S. (2005). Kicking the habit: Effects of nonstereotypic association training on the application of stereotypes. Journal of Experimental Social Psychology, 41, 68–75. Kawakami, K., Dovidio, J. F., & van Kamp, S. (2007). The impact of counterstereotypic training and correction processes on the application of stereotypes. Group Processes & Intergroup Relations, 10, 139–156. Kawakami, K., Dunn, E., Karmali, F., & Dovidio, J. F. (2009). Mispredicting affective and behavioral responses to racism. Science, 323, 276–278. Kawakami, K., Phills, C. E., Greenwald, A. G., Simard, D., Pontiero, J., Brnjas, A., et al. (2012). In perfect harmony: Synchronizing the self to activated social categories. Journal of Personality and Social Psychology, 102, 562–575. Kawakami, K., Phills, C. E., Steele, J. R., & Dovidio, J. F. (2007). (Close) Distance makes the heart grow fonder: Improving implicit racial attitudes and interracial interactions through approach behaviors. Journal of Personality and Social Psychology, 92, 957–971. Kawakami, K., Steele, J. R., Cifa, C., Phills, C. E., & Dovidio, J. F. (2008). Approaching math increases math ¼ me, math ¼ pleasant. Journal of Experimental Social Psychology, 44, 818–825. Kawakami, K., Williams, A., Sidhu, D., Choma, B. L., Rodriguez-Bailo´n, R., Can˜adas, E., et al. (2014). An eye for the I: Preferential attention to the eyes of ingroup members. Journal of Personality and Social Psychology, 107, 1–20. Kawakami, K., Young, H., & Dovidio, J. F. (2002). Automatic stereotyping: Category, trait, and behavioral activations. Personality and Social Psychology Bulletin, 28, 3–15. Kawakami, K., Karmali, F., Phills, C., Williams, A., Friesen, J., and Vaccarino, E. (under review). I don’t see race: Exploring strategies and implications related to strategic colorblindness. Unpublished manuscript. Kawakami, K., Williams, A., Friesen, J., Vingilis-Jaremko, L., Sidhu, D., Rodriguez-Bailo´n, R., et al. (under review). Similarity and race attract attention to the eyes. Unpublished manuscript. Kelley, H. H., & Thibaut, J. W. (1978). Interpersonal relations: A theory of interdependence (p. 341). New York: Wiley. Keltner, D., & Haidt, J. (1999). Social functions of emotions at four levels of analysis. Cognition and Emotion, 13, 505–521. Kenworthy, J. B., Barden, M. A., Diamond, S., & del Carmen, A. (2011). Ingroup identification as a moderator of racial bias in a shoot–no shoot decision task. Group Processes & Intergroup Relations, 14, 311–318.

72

K. Kawakami et al.

Khalid, S., Deska, J. C., & Hugenberg, K. (2016). The eyes are the windows to the mind: Direct eye gaze triggers the ascription of others’ minds. Personality and Social Psychology Bulletin, 42, 1666–1677. Kihlstrom, J. E., & Cantor, N. (1984). Mental representations of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology: Vol. 15 (pp. 1–47). New York: Academic Press. King, E. B., Knight, J. L., & Hebl, M. R. (2010). The influence of economic conditions on aspects of stigmatization. Journal of Social Issues, 66, 446–460. Knowles, E. D., Lowery, B. S., & Schaumberg, R. L. (2010). Racial prejudice predicts opposition to Obama and his health care reform plan. Journal of Experimental Social Psychology, 46, 420–423. Krosch, A. K., & Amodio, D. M. (2014). Economic scarcity alters the perception of race. Proceedings of the National Academy of Science, 111, 9079–9084. Krosch, A. R. & Amodio, D. M. (under review). Perceptual dehumanization of Black faces promotes racial discrimination under economic stress. Unpublished manuscript. Krosch, A. R., Berntsen, L., Amodio, D. M., Jost, J. T., & Bavel, J. J. V. (2013). On the ideology of hypodescent: Political conservatism predicts categorization of racially ambiguous faces as Black. Journal of Experimental Social Psychology, 49, 1196–1203. Kteily, N., Bruneau, E., Waytz, A., & Cotterill, S. (2015). The ascent of man: A theoretical and empirical case for blatant dehumanization. Journal of Personality and Social Psychology, 109, 901–931. Kubota, J. T., & Ito, T. A. (2007). Multiple cues in social perception: The time course of processing race and facial expression. Journal of Experimental Social Psychology, 43, 738–752. Kubota, J. T., & Ito, T. A. (2014). The role of expression and race in weapon identification. Emotion, 14, 1115–1124. Kunda, Z., & Thagard, P. (1996). Forming impressions using stereotypes, traits, and behaviors: A parallel constraint satisfaction theory. Psychological Review, 103, 284–308. Kunstman, J. W., Plant, E. A., Zielaskowski, K., & LaCosse, J. (2013). Feeling in with the outgroup: Outgroup acceptance and the internalization of the motivation to respond without prejudice. Journal of Personality and Social Psychology, 105, 443. LaFrance, M., Hecht, M. A., & Paluck, E. L. (2003). The contingent smile: A meta-analysis of sex differences in smiling. Psychological Bulletin, 129, 305–334. Lai, C. K., Hoffman, K. M., & Nosek, B. A. (2013). Reducing implicit prejudice. Social and Personality Psychology Compass, 7, 315–330. Lai, C. K., Marini, M., Lehr, S. A., Cerruti, C., Shin, J. L., Joy-Gaba, J. A., et al. (2014). Reducing implicit racial preferences: I. A comparative investigation of 17 interventions. Journal of Experimental Psychology: General, 143, 1765–1785. Lai, C. K., Skinner, A. L., Cooley, E., Murrar, S., Brauer, M., Devos, T., et al. (2016). Reducing implicit racial preferences: II. Intervention effectiveness across time. Journal of Experimental Psychology: General, 145, 1001–1016. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. LeVine, R. A., & Campbell, D. T. (1972). Ethnocentrism: Theories of conflict, ethnic attitudes, and group behavior (Vol. ix). Oxford, England: John Wiley & Sons. Levy, B. R., & Banaji, M. R. (2002). Implicit ageism. In T. D. Nelson (Ed.), Ageism: Stereotyping and prejudice against older persons. Cambridge, MA: the MIT Press. Leyens, J. P., Paladino, P. M., Rodriguez-Torres, R., Vaes, J., Demoulin, S., RodriguezPerez, A., et al. (2000). The emotional side of prejudice: The attribution of secondary emotions to ingroups and outgroups. Personality and Social Psychology Review, 4, 186–197.

Intergroup Perception and Cognition

73

Liberman, N., Trope, Y., & Stephan, E. (2007). Psychological distance. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles. New York: Guildford Press. Lick, D. J., Golay, A. E., & Johnson, K. L. (2014). Race categorizations drawn from dynamic body motions: Accuracy, heuristics, and intersectional biases. In Poster presented at Society for Personality and Social Psychology annual conference, Austin, TX. Lowery, B. S., Hardin, C. D., & Sinclair, S. (2001). Social influence effect on automatic racial prejudice. Journal of Personality and Social Psychology, 81, 842–855. Luhtanen, R., & Crocker, J. (1992). A collective self-esteem scale: Self-evaluation of one’s social identity. Personality and Social Psychology Bulletin, 18, 302–318. Lund, F. H., & Berg, W. C. (1946). Identifiability of nationality characteristics. The Journal of Social Psychology, 24, 77–83. Mackie, D. M., & Smith, E. R. (2002). From prejudice to intergroup emotions: Differentiated reactions to social groups. Philadelphia: Psychology Press. MacLin, O. H., & Malpass, R. S. (2001). Racial categorization of faces: The ambiguous race face effect. Psychology, Public Policy, and Law, 7, 98–118. MacLin, O. H., & Malpass, R. S. (2003). The ambiguous-race face illusion. Perception, 32, 249–252. Macrae, C. N., & Bodenhausen, G. V. (2000). Social cognition: Thinking categorically about others. Annual Review of Psychology, 51, 93–120. Macrae, C. N., Bodenhausen, G. V., & Milne, A. B. (1995). The dissection of selection in person perception: Inhibitory processes in social stereotyping. Journal of Personality and Social Psychology, 69, 397–407. Macrae, C. N., Bodenhausen, G. V., Milne, A. B., Thorn, T. M. J., & Castelli, L. (1997). On the activation of social stereotypes: The moderating role of processing objectives. Journal of Experimental Social Psychology, 33, 471–489. Macrae, C. N., & Martin, D. (2007). A boy primed Sue: Feature-based processing and person construal. European Journal of Social Psychology, 37, 793–805. Macrae, C. N., Quinn, K. A., Mason, M. F., & Quadflieg, S. (2005). Understanding others: The face and person construal. Journal of Personality and Social Psychology, 89, 686–695. Maddox, K. B. (2004). Perspectives on racial phenotypicality bias. Personality and Social Psychology Review, 8, 383–401. Maddox, K. B., & Gray, A. (2002). Cognitive representations of Black Americans: Reexploring the role of skin tone. Personality and Social Psychology Bulletin, 28, 250–259. Madera, J. M., & Hebl, M. R. (2012). Discrimination against facially stigmatized applicants in interviews: An eye-tracking and face-to-face investigation. Journal of Applied Psychology, 97, 317–330. Maister, L., Sebanz, N., Knoblich, G., & Tsakiris, M. (2013). Experiencing ownership over a dark-skinned body reduces implicit racial bias. Cognition, 128, 170–178. Mandalaywala, T. M., Amodio, D. M., & Rhodes, W. (under review). Essentialism promotes racial prejudice by increasing acceptance of the status quo. Unpublished manuscript. Mangini, M., & Biederman, I. (2004). Making the ineffable explicit: Estimating the information employed for face classifications. Cognitive Science, 28, 209–226. Mann, N. H., & Kawakami, K. (2012). The long steep path to equality: Progressing on egalitarian goals. Journal of Experimental Psychology: General, 141, 187–197. Markus, H., & Kunda, Z. (1986). Stability and malleability of the self-concept. Journal of Personality and Social Psychology, 51, 858–866. Matsumoto, D. (1989). Cultural similarities and differences in display rule. Journal of CrossCultural Psychology, 20, 92–105. Matsumoto, D., Olide, A., & Willingham, B. (2009). Is there an ingroup advantage in recognizing spontaneously expressed emotions? Journal of Nonverbal Behavior, 33, 181–191.

74

K. Kawakami et al.

Maurer, D., Le Grand, R., & Mondloch, C. J. (2002). The many faces of configural processing. Trends in Cognitive Sciences, 6, 255–260. McCarthy, G., Puce, A., Gore, J. C., & Allison, T. (1997). Face-specific processing in the human fusiform gyrus. Journal of Cognitive Neuroscience, 9, 605–610. McConnell, A. R., Dunn, E. W., Austin, S. N., & Rawn, C. D. (2011). Blind spots in the search for happiness: Implicit attitudes and nonverbal leakage predict affective forecasting errors. Journal of Experimental Social Psychology, 47, 628–634. McConnell, A. R., & Leibold, J. M. (2001). Relations among the implicit association test, discriminatory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. Meissner, C. A., & Brigham, J. C. (2001). Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review. Psychology, Public Policy, and Law, 7, 3–35. Mendoza, S. A., Gollwitzer, P. M., & Amodio, D. M. (2010). Reducing the expression of implicit stereotypes: Reflexive control through implementation intentions. Personality and Social Psychology Bulletin, 36, 512–523. Mendoza, S. A., Lane, S. P., & Amodio, D. M. (2014). For members only: Ingroup punishment of fairness norm violations in the ultimatum game. Social Psychological and Personality Science, 5, 662–670. Michel, C., Corneille, O., & Rossion, B. (2007). Race categorization modulates holistic encoding. Cognitive Science, 31, 911–924. Michel, C., Rossion, B., Han, J., Chung, C. S., & Caldara, R. (2006). Holistic processing is finely tuned for faces of one’s own race. Psychological Science, 17, 608–615. Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663. Monteith, M. J. (1993). Self-regulation of prejudiced responses: Implications for progress in prejudice-reduction efforts. Journal of Personality and Social Psychology, 65, 469–485. Monteith, M. J., Ashburn-Nardo, L., Voils, C. I., & Czopp, A. M. (2002). Putting the brakes on prejudice: On the development and operation of cues for control. Journal of Personality and Social Psychology, 83, 1029–1050. Montepare, J. M., & Zebrowitz-McArthur, L. A. (1998). Person perception comes of age: The salience and significance of age in social judgments. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 93–163): Vol. 30. New York: Academic Press. Mouchetant-Rostaing, Y., Girard, M. H., Bentin, S., Aguera, P. E., & Pernier, J. (2000). Neurophysiological correlates of face gender processing in humans. European Journal of Neuroscience, 12, 303–310. Neuberg, S. L., Warner, C. M., Mistler, S. A., Berlin, A., Hill, E. D., Johnson, J. D., et al. (2014). Religion and intergroup conflict: Findings from the global group relations project. Psychological Science, 25, 198–206. Norton, M. I., Sommers, S. R., Apfelbaum, E. P., Pura, N., & Ariely, D. (2006). Colorblindness and interracial interaction: Playing the political correctness game. Psychological Science, 17, 949–953. Nosek, B. A. (2007). Implicit–explicit relations. Current Directions in Psychological Science, 16, 65–69. Nosek, B. A., Hawkins, C. B., & Frazier, R. (2012). Implicit social cognition: From measures to mechanisms. Trends in Cognitive Sciences, 15, 152–159. Nummenmaa, L., Hy€ on€a, J., & Hietanen, J. K. (2009). I’ll walk this way: Eyes reveal the direction of locomotion and make passersby look and go the other way. Psychological Science, 20, 1454–1458.

Intergroup Perception and Cognition

75

Nussinson, R., Seibt, B., Hafner, M., & Strack, F. (2010). Come a bit closer: Approach motor actions lead to feeling similar and behaviorally assimilating to others. Social Cognition, 28, 40–58. Ofan, R. H., Rubin, N., & Amodio, D. M. (2011). Seeing race: N170 responses to race and their relation to automatic racial attitudes and controlled processing. Journal of Cognitive Neuroscience, 23, 3152–3161. Ofan, R. H., Rubin, N., & Amodio, D. M. (2014). Situation-based social anxiety enhances the neural encoding of faces: Evidence from an intergroup context. Social Cognitive and Affective Neuroscience, 9, 1055–1061. Olivola, C. Y., & Todorov, A. (2010). Fooled by first impressions? Reexamining the diagnostic value of appearance-based inferences. Journal of Experimental Social Psychology, 46, 315–324. Olson, M. A., & Fazio, R. H. (2006). Reducing automatically activated racial prejudice through implicit evaluative conditioning. Personality and Social Psychology Bulletin, 32, 421–433. Olson, I. R., McCoy, D., Klobusicky, E., & Ross, L. A. (2013). Social cognition and the anterior temporal lobes: A review and theoretical framework. Social Cognitive and Affective Neuroscience, 8, 123–133. Olsson, A., Ebert, J. P., Banaji, M. R., & Phelps, E. A. (2005). The role of social groups in the persistence of learned fear. Science, 309, 785–787. Ostrom, T. M., & Sedikides, C. (1992). Out-group homogeneity effects in natural and minimal groups. Psychological Bulletin, 112, 536–552. Page-Gould, E., Mendoza-Denton, R., Allegre, J. M., & Sij, J. O. (2010). Journal of Personality and Social Psychology, 98, 775–793. Page-Gould, E., Mendoza-Denton, R., & Tropp, L. R. (2008). With a little help from my cross-group friend: Reducing anxiety in intergroup contexts through cross-group friendship. Journal of Personality and Social Psychology, 95, 1080–1094. Paluk, E. L., & Green, D. P. (2009). Prejudice reduction: What works? A review and assessment of research and practice. Annual Review of Psychology, 60, 339–367. Pauker, K., Weisbuch, M., Ambady, N., Sommers, S. R., Adams, R. B., Jr., & Ivcevic, Z. (2009). Not so Black and White: Memory for ambiguous group members. Journal of Personality and Social Psychology, 96, 795–810. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K. (2005). Conceptualizing control in social cognition: How executive control modulates the expression of automatic stereotyping. Journal of Personality and Social Psychology, 89, 488–503. Payne, K., & Lundberg, K. (2014). The affect misattribution procedure: Ten years of evidence on reliability, validity, and mechanism. Social and Personality Psychology Compass, 8, 672–686. Peck, T., Seinfeld, S., Aglioti, M., & Slater, M. (2013). Putting yourself in the skin of a black avatar reduces implicit racial bias. Consciousness and Cognition, 22, 779–787. Peery, D., & Bodenhausen, G. V. (2008). Black + White ¼ Black: Hypodescent in reflexive categorization of racially ambiguous faces. Psychological Science, 19, 973–977. Pek, J., & Hoyle, R. H. (2016). On the (in) validity of tests of simple mediation: Threats and solutions. Social and Personality Psychology Compass, 10, 150–163. Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90, 751–783. Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738.

76

K. Kawakami et al.

Phills, C. E., Kawakami, K., Krusemark, D. R., & Nyguen, J. (under review). Does reducing prejudice increase outgroup identification? The downstream consequences of associating positive concepts with racial categories. Invited for resubmission to Social Psychological and Personality Science. Unpublished manuscript. Phills, C. E., Kawakami, K., Tabi, E., Nadolny, D., & Inzlicht, M. (2011). Mind the gap: Increasing associations between the self and Blacks with approach behaviors. Journal of Personality and Social Psychology, 100, 197–210. Phills, C. E., Santelli, A., Kawakami, K., Struthers, W., & Higgins, E. T. (2011). Reducing implicit prejudice: Matching approach/avoidance strategies to contextual valence and regulatory focus. Journal of Experimental Social Psychology, 47, 968–973. Plant, E. A., & Devine, P. G. (1998). Internal and external motivation to respond without prejudice. Journal of Personality and Social Psychology, 75, 811–832. Pokorny, J., & de Waal, F. B. M. (2009). Monkeys recognize the faces of group mates in photographs. Proceedings of the National Academy of Sciences, 106, 21539–21543. Proffitt, D. R., Stefanucci, J. K., Banton, T., & Epstein, W. (2003). The role of effort in perceiving distance. Psychological Science, 14, 106–112. Prothro, E. T., & Melikian, L. H. (1955). Studies in stereotypes: V. Familiarity and the kernel of truth hypothesis. The Journal of Social Psychology, 41, 3–10. Quillian, L. (1995). Prejudice as a response to perceived group threat: Population composition and anti-immigrant and racial prejudice in Europe. American Sociological Review, 60, 586–611. Quinn, K. A., Mason, M. F., & Macrae, C. N. (2009). Familiarity and person construal: Individual knowledge moderates the automaticity of category activation. European Journal of Social Psychology, 39, 852–861. Quinn, K. A., Mason, M. F., & Macrae, C. N. (2010). When Arnold is “The Terminator”, we no longer see him as a man: The temporal determinants of person perception. Experimental Psychology, 57, 27–35. Ratner, K. G., & Amodio, D. M. (2013). Seeing “us vs. them”: Minimal group effects on the neural encoding of faces. Journal of Experimental Social Psychology, 49, 298–301. Ratner, K. G., Dotsch, R., Wigboldus, D., van Knippenberg, A., & Amodio, D. M. (2014). Visualizing minimal ingroup and outgroup faces: Implications for impressions, attitudes, and behavior. Journal of Personality and Social Psychology, 106, 897–911. Ray, D. G., Way, N., & Hamilton, D. L. (2010). Cross-categorization, evaluation, and face recognition. Journal of Experimental Social Psychology, 46, 449–452. Rhodes, G., Locke, V., Ewing, L., & Evangelista, E. (2009). Race coding and the other-race effect in face recognition. Perception, 38, 232–241. Rice, D. R., & Mullen, B. (2003). Isaac, Ishmael, and Janus: Past and future lessons regarding the ethnic categorization of faces. Applied Cognitive Psychology, 17, 1129–1147. Richmond, V. P., McCroskey, J. C., & Hickson, M. L. (2008). Nonverbal behavior in interpersonal relationships. Boston: Pearson. Riddoch, M. J., Johnston, R. A., Bracewell, R. M., Boutsen, L., & Humphreys, G. W. (2008). Are faces special? A case of pure prosopagnosia. Cognitive Neuropsychology, 25, 3–26. Rieger, G., Linsenmeier, J. A., Gygax, L., & Bailey, J. M. (2008). Sexual orientation and childhood gender nonconformity: Evidence from home videos. Developmental Psychology, 44, 46–58. Ro, T., Russell, C., & Lavie, N. (2001). Changing faces: A detection advantage in the flicker paradigm. Psychological Science, 12, 94–99. Rodeheffer, C., Hill, S. E., & Lord, C. G. (2012). Does this recession make me look Black? The effect of resource scarcity on the categorization of biracial faces. Psychological Science, 23, 1476–1478.

Intergroup Perception and Cognition

77

Rooth, D. (2010). Automatic associations and discrimination in hiring: Real world evidence. Labor Economics, 17, 523–534. Rossion, B., Gauthier, I., Tarr, M. J., Despland, P., Bruyer, R., Linotte, S., et al. (2000). The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: An electrophysiological account of face-specific processes in the human brain. NeuroReport, 11, 69–74. Rudman, L. A., & Ashmore, R. D. (2007). Discrimination and the implicit association test. Group Processes & Intergroup Relations, 10, 359–372. Rudman, L. A., Ashmore, R. D., & Gary, M. L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. Journal of Personality and Social Psychology, 81, 856–868. Rule, N. O., & Ambady, N. (2008). Brief exposures: Male sexual orientation is accurately perceived at 50-ms. Journal of Experimental Social Psychology, 44, 1100–1105. Rule, N. O., & Ambady, N. (2010). Democrats and Republicans can be differentiated from their faces. PLoS One, 5, e8733. Rule, N. O., Ambady, N., Adams, R. B., Jr., & Macrae, C. N. (2007). Us and them: Memory advantages in perceptually ambiguous groups. Psychonomic Bulletin & Review, 14, 687–692. Rule, N. O., Ambady, N., Adams, R. B., Jr., & Macrae, C. N. (2008). Accuracy and awareness in the perception and categorization of male sexual orientation. Journal of Personality and Social Psychology, 95, 1019–1028. Rule, N. O., Ambady, N., & Hallett, K. C. (2009). Female sexual orientation is perceived accurately, rapidly, and automatically from the face and its features. Journal of Experimental Social Psychology, 45, 1245–1251. Rule, N. O., Garrett, J. V., & Ambady, N. (2010a). On the perception of religious group membership from faces. PLoS One, 5, e14241. Rule, N. O., Garrett, J. V., & Ambady, N. (2010b). Faces and places: Geographic environment influences the ingroup memory advantage. Journal of Personality and Social Psychology, 98, 343–355. Rule, N. O., Macrae, C. N., & Ambady, N. (2009). Ambiguous group membership is extracted automatically from faces. Psychological Science, 20, 441–443. Ryan, C. S., Judd, C. M., & Park, B. (1996). The effect of stereotypes on judgments of individuals: The moderating role of perceived group variability. Journal of Experimental Social Psychology, 32, 71–103. Sacco, D. F., & Hugenberg, K. (2009). The look of fear and anger: Facial maturity modulates the recognition of fearful and angry expressions. Emotion, 9, 39–49. Scheck, B., Neufeld, P., & Dwyer, J. (2000). Actual innocence. New York, NY: Signet. Schmid, P. C., & Amodio, D. M. (2016). Power effects on implicit prejudice and stereotyping: The role of intergroup face processing. Social Neuroscience. http://dx.doi. org/10.1080/17470919.2016.1144647. Schmid, P. C., Kleiman, T., & Amodio, D. M. (2015). Power effects on cognitive control: Turning conflict into action. Journal of Experimental Psychology: General, 144, 655–663. Sekaquaptewa, D., Espinoza, P., Thompson, M., Vargas, P., & von Hippel, W. (2003). Stereotypic explanatory bias: Implicit stereotyping as a predictor of discrimination. Journal of Experimental Social Psychology, 39, 75–82. Sherif, M., & Sherif, C. W. (1953). Groups in harmony and tension: An integration of studies of intergroup relations: Vol. xiii. Oxford, England: Harper & Brothers. Sherman, J., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T., & Groom, C. (2008). The self-regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Shook, N. J., & Fazio, R. H. (2008). Interracial roommate relationships: An experimental field test of the contact hypothesis. Psychological Science, 19, 717–723.

78

K. Kawakami et al.

Shriver, E., & Hugenberg, K. (2010). Power, individuation, and the cross-race recognition deficit. Journal of Experimental Social Psychology, 46, 767–774. Shriver, E., Young, S. G., Hugenberg, K., Bernstein, M. J., & Lanter, J. (2008). Class, race, and the face: Social context moderates the cross-race effect in face recognition. Personality and Social Psychology Bulletin, 34, 260–278. Shteynberg, G., & Galinsky, A. D. (2011). Implicit coordination: Sharing goals with similar others intensifies goal pursuit. Journal of Experimental Social Psychology, 47, 1291–1294. Sinclair, S., Lowery, B., Hardin, C., & Colangelo, A. (2005). Social tuning of automatic attitudes: The role of affiliative motivation. Journal of Personality and Social Psychology, 89, 583–592. Snyder, C. R., Lassegard, M., & Ford, C. E. (1986). Distancing after group success and failure: Basking in reflected glory and cutting off reflected failure. Journal of Personality and Social Psychology, 51, 382–388. Sporer, S. L. (2001). The cross-race effect. Psychology. Public Policy, and Law, Band, 7, 170–200. Stangor, C., Lynch, L., Duan, C., & Glass, B. (1992). Categorization of individuals on the basis of multiple social features. Journal of Personality and Social Psychology, 62, 207–218. Stanley, D. A., Sokol-Hessner, P., Banaji, M. R., & Phelps, E. A. (2011). Implicit race attitudes predict trustworthiness judgments and economic trust decisions. Proceedings of the National Academy of Sciences, 108, 7710–7715. Stepanova, E. V., & Strube, M. J. (2009). Making of a face: Role of facial physiognomy, skin tone, and color presentation mode in evaluations of racial typicality. The Journal of Social Psychology, 149, 66–81. Stepanova, E. V., & Strube, M. J. (2012a). The role of skin color and facial physiognomy in racial categorization: Moderation by implicit racial attitudes. Journal of Experimental Social Psychology, 48, 867–878. Stepanova, E. V., & Strube, M. J. (2012b). What’s in a face? The role of skin tone, facial physiognomy, and color presentation mode of facial primes in affective priming effects. The Journal of Social Psychology, 152, 212–227. Stephan, W. G., Boniecki, K. A., Ybarra, O., Bettencourt, A., Ervin, K. S., Jackson, L. A., et al. (2002). The role of threats in the racial attitudes of blacks and whites. Personality and Social Psychology Bulletin, 28, 1242–1254. Stephan, W. G., Renfro, C., Esses, V. M., Stephan, C. W., & Martin, T. (2005). The effects of feeling threatened on attitudes toward immigrants. International Journal of Intercultural Relations, 29, 1–19. Stephan, W. G., & Stephan, C. W. (1985). Intergroup anxiety. Journal of Social Issues, 41, 157–175. Stephan, W. G., Ybarra, O., & Bachman, G. (1999). Prejudice toward immigrants1. Journal of Applied Social Psychology, 29, 2221–2237. Stewart, B. D., & Payne, B. K. (2008). Bringing automatic stereotyping under control: Implementation intentions as efficient means of thought control. Personality and Social Psychology Bulletin, 34, 1332–1345. Tajfel, H., Billig, M. G., Bundy, R. P., & Flament, C. (1971). Social categorization and intergroup behaviour. European Journal of Social Psychology, 1, 149–178. Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Monterey, CA: Brooks/Cole. Tajfel, H., & Turner, J. C. (1986). The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin (Eds.), Psychology of intergroup relations (pp. 7–24). Chicago, IL: Nelson-Hall. Tam, T., Hewstone, M., Harwood, J., Voci, A., & Kenworthy, J. (2006). Intergroup contact and grandparent–grandchild communication: The effects of self-disclosure on

Intergroup Perception and Cognition

79

implicit and explicit biases against older people. Group Processes & Intergroup Relations, 9, 413–429. Tanaka, J. W., & Curran, T. (2001). A neural basis for expert object recognition. Psychological Science, 12, 43–47. Tanaka, J. W., & Gauthier, I. (1997). Expertise in object and face recognition. In R. L. Goldstone, P. G. Schyns, & D. L. Medin (Eds.), Psychology of learning and motivation: Vol. 36 (pp. 83–125). San Diego, CA: Academic Press. Taylor, P., Kochhar, R., & Fry, R. (2011). Twenty-to-one: Wealth gaps rise to record highs between Whites, Blacks, Hispanics. Washington, DC: Pew Research Center: Social & Demographic Trends. July. Theeuwes, J., & Van der Stigchel, S. (2006). Faces capture attention: Evidence from inhibition of return. Visual Cognition, 13, 657–665. Tskhay, K. O., & Rule, N. O. (2013). Accuracy in categorizing perceptually ambiguous groups: A review and meta-analysis. Personality and Social Psychology Review, 17, 72–86. Turner, R. N., Hewstone, M., & Voci, A. (2007). Reducing explicit and implicit outgroup prejudice via direct and extended contact: The mediating role of self-disclosure and intergroup anxiety. Journal of Personality and Social Psychology, 93, 369. Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987). Rediscovering the social group: A self-categorization theory. New York: Basil Blackwell. Valentine, T. (1988). Upside-down faces—A review of the effect of inversion upon face recognition. British Journal of Psychology, 79, 471–491. Van Bavel, J. J., & Cunningham, W. A. (2012). A social identity approach to person memory group membership, collective identification, and social role shape attention and memory. Personality and Social Psychology Bulletin, 38, 1566–1578. Van Bavel, J. J., Packer, D. J., & Cunningham, W. A. (2011). Modulation of the Fusiform face area following minimal exposure to motivationally relevant faces: Evidence of in-group enhancement (not out-group disregard). Journal of Cognitive Neuroscience, 23, 3343–3354. Van Bavel, J. J., Swencionis, J. K., O’Connor, R. C., & Cunningham, W. A. (2012). Motivated social memory: Belonging needs moderate the own-group bias in face recognition. Journal of Experimental Social Psychology, 48, 707–713. Vorauer, J. D. (2005). Miscommunications surrounding efforts to reach out across group boundaries. Personality and Social Psychology Bulletin, 31, 1653–1664. Vorauer, J. D., & Sakamoto, Y. (2006). I thought we could be friends, but…. Psychological Science, 17, 326–331. Walker, P., Silvert, L., Hewstone, M., & Nobre, A. (2008). Social contact and other-race face processing in the human brain. Social Cognitive and Affective Neuroscience, 3, 16–25. Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92, 82–96. Walton, G. M., Cohen, G. L., Cwir, D., & Spencer, S. J. (2012). Mere belonging: The power of social connections. Journal of Personality and Social Psychology, 102, 513–532. Wiese, H., Stahl, J., & Schweinberger, S. (2009). Configural processing of other-race faces is delayed but not decreased. Biological Psychology, 81, 103–109. Wilson, J. P., & Rule, N. O. (2015). Facial trustworthiness predicts extreme criminal sentencing outcomes. Psychological Science, 26, 1325–1331. Wilson, J. P., Rule, N. O., & Hugenberg, K. (under review). Racial bias in judgments of physical size and formidability: Bottom-up and top-down factors. Unpublished manuscript. Wilson, J. P., See, P., Bernstein, M. J., Hugenberg, K., & Chartier, C. (2014). Differences in anticipated interaction drive own group biases in face memory. PLoS One, 9, e90668.

80

K. Kawakami et al.

Windhager, S., Bookstein, F. L., Grammer, K., Oberzaucher, E., Said, H., Slice, D. E., et al. (2012). “Cars have their own faces”: Cross-cultural ratings of car shapes in biological (stereotypical) terms. Evolution and Human Behavior, 33, 109–120. Winston, J. S., Henson, R. N. A., Fine-Goulden, M. R., & Dolan, R. J. (2004). fMRIadaptation reveals dissociable neural representations of identity and expression in face perception. Journal of Neurophysiology, 92, 1830–1839. Wirth, J. H., Sacco, D. F., Hugenberg, K., & Williams, K. D. (2010). Eye gaze as relational evaluation: Averted eye gaze leads to feelings of ostracism and relational devaluation. Personality and Social Psychology Bulletin, 36, 869–882. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Spontaneous prejudice in context: Variability in automatically activated attitudes. Journal of Personality and Social Psychology, 81, 815–827. Woodcock, A., & Monteith, M. J. (2013). Forging links with the self to combat implicit bias. Group Processes & Intergroup Relations, 16, 445–461. Word, C. O., Zanna, M. P., & Cooper, J. (1974). The nonverbal mediation of self-fulfilling prophecies in interracial interaction. Journal of Experimental Social Psychology, 10, 109–120. Xiao, Y. J., Coppin, G., & Van Bavel, J. J. (2016). Perceiving the world through groupcolored glasses: A perceptual model of intergroup relations. Psychological Inquiry, 27, 255–274. Xu, X., Zuo, X., Wang, X., & Han, S. (2009). Do you feel my pain? Racial group membership modulates empathic neural responses. The Journal of Neuroscience, 29, 8525–8529. Yin, R. K. (1969). Looking at upside-down faces. Journal of Experimental Social Psychology, 81, 141–145. Young, S. G., Bernstein, M. J., & Hugenberg, K. (2010). When do Own-Group Biases in face recognition occur? Encoding versus post-encoding. Social Cognition, 28, 240–250. Young, S. G., & Hugenberg, K. (2010). Mere social categorization modulates identification of facial expressions of emotion. Journal of Personality and Social Psychology, 99, 964–977. Young, S. G., & Hugenberg, K. (2012). Individuation motivation and face expertise can operate jointly to produce the Own-Race Bias. Social Psychological and Personality Science, 3, 80–87. Zaki, J., & Cikara, M. (2015). Addressing empathic failures. Current Directions in Psychological Science, 24, 471–476. Zebrowitz, L., Kikuchi, M., & Fellous, J. M. (2010). Facial resemblance to emotions: Group differences, impression effects, and race stereotypes. Journal of Personality and Social Psychology, 98, 175–189. Zhang, H., & Chan, D. K. (2009). Self-esteem as a source of evaluative conditioning. European Journal of Social Psychology, 39, 1065–1074. Zhou, H., & Fishbach, A. (2016). The pitfall of experimenting on the web: How unattended selective attrition leads to surprising (yet false) research conclusions. Journal of Personality and Social Psychology, 111, 493–504.

CHAPTER TWO

Self-Distancing: Theory, Research, and Current Directions E. Kross*,1, O. Ayduk†,1 *University of Michigan, Ann Arbor, MI, United States † University of California, Berkeley, CA, United States 1 Corresponding authors: e-mail address: [email protected]; [email protected]

Contents 1. The Self-Reflection Puzzle 2. Self-Distancing: A Tool to Promote Adaptive Self-Reflection 2.1 Background 2.2 Conceptual Framework 3. Making Meaning From Afar 3.1 Paradigm Overview 3.2 Experimental Results 3.3 Spontaneous Self-Distancing 3.4 Behavioral Implications 3.5 From Adults to Children 3.6 Clinical Generalizability 3.7 Implications for Physical Health 3.8 Neural Correlates 3.9 From the Past to the Future 3.10 Summary 4. Self-Talk 4.1 Initial Studies 4.2 Implications for Emotion Regulation 4.3 Challenge vs Threat Construals 4.4 From the Lab to Daily Life 4.5 An Effortless Form of Self-Control? 4.6 Clinical Implications 4.7 Converging Evidence 4.8 Summary 5. Mental Time Travel 5.1 Experimental Evidence 5.2 Individual Differences 5.3 Converging Evidence 5.4 Summary 6. Self-Distancing Training 6.1 Laboratory Training Intervention

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6.2 Online Training Intervention 6.3 Converging Evidence 6.4 Summary 7. New Extensions 7.1 Wise Reasoning 7.2 A Common Ingredient Underlying Successful Cognitive Interventions? 7.3 Intergroup Relationships 7.4 Social Support 7.5 Summary 8. Concluding Thoughts Acknowledgments References

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Abstract When people experience negative events, they often try to understand their feelings to improve the way they feel. Although engaging in this meaning-making process leads people to feel better at times, it frequently breaks down leading people to ruminate and feel worse. This raises the question: What factors determine whether people’s attempts to “work-through” their negative feelings succeed or fail? In this article, we describe an integrative program of research that has addressed this issue by focusing on the role that self-distancing plays in facilitating adaptive self-reflection. We begin by describing the “self-reflection puzzle” that initially motivated this line of work. Next, we introduce the concept of self-distancing and describe the conceptual framework we developed to explain how this process should facilitate adaptive self-reflection. After describing the early studies that evaluated this framework, we discuss how these findings have been extended to broaden and deepen our understanding of the role that this process plays in self-regulation. We conclude by offering several parting thoughts that integrate the ideas discussed in this chapter.

1. THE SELF-REFLECTION PUZZLE Many people try to understand their feelings when they are upset, under the assumption that doing so will lead them to feel better. Indeed, it would seem that many of us reflexively heed Socrates’ advice to “know thyself ” when we experience emotional pain. But are people’s attempts to work-through their feelings productive? Do they actually lead people to feel better? A great deal of research has addressed these questions over the past 40 years, and the results reveal a puzzle. On the one hand, several studies suggest that it is indeed helpful for people to reflect on their emotions when they experience distress

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(e.g., Pennebaker & Chung, 2007; Wilson & Gilbert, 2008). A guiding assumption behind this work is that to improve the way people feel about a negative event, it is necessary to change the way they think about it (e.g., Ayduk & Mischel, 2011; Foa & Kozak, 1986; Gross, 2013; Pennebaker & Graybeal, 2001; Wilson & Gilbert, 2008). Supporting this idea, converging evidence indicates that interventions and therapeutic practices that lead people to mentally represent emotionally arousing stimuli in less negative terms lead to a number of short- and long-term mental and physical health benefits (e.g., Foa & Kozak, 1986; Gross, 1998; Monson et al., 2006; Pennebaker, Mayne, & Francis, 1997; Ray, Wilhelm, & Gross, 2008; Resick & Schnicke, 1992; Smyth, 1998; Stanton, Kirk, Cameron, & Danoff-Burg, 2000; Wilson & Gilbert, 2008). However, an alternative equally sizeable literature indicates that people’s attempts to understand their painful emotions are often counterproductive (e.g., Mor & Winquist, 2002; Nolen-Hoeksema, Morrow, & Fredrickson, 1993; Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008; Smith & Alloy, 2009). According to this line of work, when people try to analyze their feelings, negative thoughts becomes accessible, which lead people to engage in a vicious cycle of “rumination” that serves to maintain and exacerbate distress in the short term, and undermines people’s health and well-being over time (e.g., Ayduk & Kross, 2008; Brosschot, Gerin, & Thayer, 2006; Denson, Spanovic, & Miller, 2009; Gerin, Davidson, Christenfeld, Goyal, & Schwartz, 2006; Gotlib & Joormann, 2010; Hankin, 2008; Hankin, Stone, & Wright, 2010; McEwen, 1998). Putting these different lines of research together creates a puzzle. We know on the one hand that it is useful for people to work-through their negative feelings, but we also know that their ability to do so effectively is rife with difficulty. So the question is: What conditions promote adaptive vs maladaptive self-reflection?

2. SELF-DISTANCING: A TOOL TO PROMOTE ADAPTIVE SELF-REFLECTION 2.1 Background In our early research, we reasoned that the answer to this question had to do with psychological distance. We hypothesized that people’s attempts to reflect adaptively on their negative feelings often fail because they focus on their experiences from a psychologically immersed perspective, which makes it difficult for people to reason objectively without getting

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caught up in the emotionally arousing details of what happened to them. Thus, we hypothesized that a mechanism was needed to allow people to “take a step back” from their experience so that they could work-through it more effectively. We called this process self-distancing (Kross, Ayduk, & Mischel, 2005). We likened this process to the experience of seeking out a friend’s counsel on a difficult problem. Whereas it is often challenging for the person experiencing a personal dilemma to reason objectively about their own circumstances, friends are often uniquely capable of providing sage advice because they’re not involved in the experience—they are psychologically removed from the event (Grossmann & Kross, 2014). We expected a similar logic to explain how self-distancing would facilitate adaptive selfreflection—i.e., by enhancing a person’s level of psychological distance from the self, we expected people to be increasingly capable of reasoning constructively about their own problems. It is important to note that we were not the first to suggest that selfdistancing might be useful from a self-regulatory perspective. To the contrary, psychologists have written about the self-regulatory benefits of psychological distance for decades (and philosopher’s centuries before them). For example, Mischel’s seminal work on delay of gratification in children demonstrated that cognitive strategies that increase psychological distance enhance children’s delay of gratification ability (Mischel & Ayduk, 2004; Mischel & Rodriguez, 1993)—a set of findings that led him to describe psychological distance as one of the “basic ingredients” that enable self-control (Mischel & Rodriguez, 1993). In work on coping and emotion regulation, Lazarus and Alfert (1964) and Gross (1998) likewise demonstrated the benefits of adopting a distanced perspective for enhancing emotion regulation. Outside of social-personality psychology, Beck, one of the cofounders of cognitive therapy, once described “distancing” as an important prerequisite for allowing patients to benefit from cognitive therapy (Beck, 1970). Ingram and Hollon (1986) later reinforced this point, arguing that cognitive therapy involves “helping individuals switch to a controlled mode of processing that is metacognitive in nature, typically referred to as ‘distancing’.” They went on to suggest, “the long-term effectiveness of cognitive therapy may reside in teaching individuals how to initiate this process on their own” (p. 272). The concept of distancing has also factored prominently into mindfulness practices for centuries. Such work emphasizes the importance of “decentering,” a concept that overlaps conceptually with self-distancing

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and is believed to be one of the active ingredients underlying mindfulness’s benefits (e.g., Bernstein et al., 2015; Hayes, Luoma, Bond, Masuda, & Lillis, 2006; Linehan et al., 2006; Segal, Williams, & Teasdale, 2002). Collectively, these different strands of research and theory were consistent with the idea that encouraging people to reflect on their negative experiences from a psychologically distanced perspective might allow people to reflect on their feelings more constructively. But they raised a critical question: How do you get a person to self-distance while they analyze their feelings?

2.2 Conceptual Framework We reasoned that one way of doing this was to manipulate the vantage point that people adopt when they reflect on negative autobiographical experiences. Specifically, prior research indicates that when people recall negative emotional experiences, they tend to do so from a self-immersed perspective, in which they visualize events happening to them all over again through their own eyes (e.g., Nigro & Neisser, 1983; Robinson & Swanson, 1993). But it is also possible for people to adopt a self-distanced perspective as they reflect on their feelings, in which a person views themselves in their experience from afar, for example, from the perspective of a “fly on the wall” (Libby & Eibach, 2002, 2011; McIsaac & Eich, 2002; Nigro & Neisser, 1983; Pronin & Ross, 2006; Robinson & Swanson, 1993; Vasquez & Buehler, 2007). We predicted that cueing people to analyze their negative experiences from a self-distanced perspective (rather than a self-immersed perspective) should lead them to focus less on recounting the emotionally arousing features of their past experience and focus more on reconstruing it in ways that provide them with a sense of insight and closure. In turn, we predicted that this shift in how people focused on their negative experience—less recounting and more reconstruing—would lead them to experience less distress in the short term, immediately after they analyzed their feelings. Importantly, because we expected self-distancing to lead to changes in the way people mentally represent aversive past experiences that reduce their negativity, we also expected it to buffer individuals against ruminating about their experience over time and becoming increasingly distressed when they thought about their experience in the future. Thus, we predicted selfdistancing would predict long-term benefits as well. Fig. 1 presents these predictions in schematic form.

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Fig. 1 Conceptual framework.

Before we turn to discussing the initial studies that we performed to test these ideas, it is important to emphasize that from its inception this program of research focused on the role that self-distancing plays in allowing people to make sense of their reactions to negative experiences. Thus, in all of the studies we initially performed, participants were asked to do two things: (a) adopt a specific type of self-perspective (e.g., self-immersed vs selfdistanced) and then (b) analyze the reasons underlying their feelings (while maintaining the perspective they initially adopted). Thus, our studies focused on how self-distancing impacts self-regulation in the context of a specific epistemic goal—to make sense of one’s feelings. As we have noted elsewhere (e.g., Ayduk & Kross, in press; Kross, Gard, Deldin, Clifton, & Ayduk, 2012), it is also possible for people to self-distance to achieve different goals. For example, a person could self-distance to avoid thinking about the emotional content of their experiences, as may be the case for patients with posttraumatic stress disorder (Kenny & Bryant, 2007; Kenny et al., 2009; McIsaac & Eich, 2004). Alternatively, they could adopt a self-distanced perspective to simply observe and accept their feelings as mindfulness practices advocate (Bernstein et al., 2015; Segal et al., 2002). In our view, each of these examples demonstrates how people can adopt a self-distanced perspective to achieve different goals. And each of these different goals may have quite different implications for how people think, feel, and behave, a point we return to at the end of this chapter.

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3. MAKING MEANING FROM AFAR 3.1 Paradigm Overview Does self-distancing facilitate adaptive self-reflection? To test the predictions sketched out in Fig. 1, we developed an experimental paradigm that involves having participants first recall and then reflect on an intense negative experience from either a self-immersed or self-distanced perspective. As we describe in more detail later, the specific type of negative experience that participants were asked to recall across studies varied depending on the goals of the experiment. Thus, in some cases, participants were asked to recall experiences in which they felt overwhelming anger and hostility. In other studies, they were asked to recall other types of negative experiences (e.g., those involving sadness, anxiety, guilt, shame, happiness, and embarrassment). Once they brought an event to mind (regardless of what type), however, they were randomly assigned to adopt either a self-immersed or a self-distanced perspective. Participants in the self-immersed group were led to visualize their past experience happening to them all over again through their own eyes (e.g., “Go back to the time and place of the experience you just recalled and see the scene in your mind’s eye. Now see the experiencing unfold through your own eyes as if it were happening to you all over again. Replay the event as it unfolds in your imagination through your own eyes …”). Participants in the self-distanced group were asked to take a few steps back so that they could watch the experience happening to them from the vantage point of a fly on the wall (e.g., “Go back to the time and place of the experience you just recalled and see the scene in your mind’s eye. Now take a few steps back. Move away from the situation to a point where you can now watch the event unfold from a distance and see yourself in the event. As you do this, focus on what has now become the distant you. Now watch the experience unfold as if it were happening to the distant you all over again. Replay the event as it unfolds in your imagination as you observe your distant self …”). After participants adopted one of these two perspectives, they were instructed to analyze their feelings surrounding their recalled experience while maintaining the perspective they were initially told to adopt (e.g., Self-Immersed: “As you continue to see the situation unfold through your own eyes, try to understand your feelings …”; Self-Distanced: “As you continue to watch the situation unfold to your distant self, try to understand his or her feelings …”). We then examined how these instructions

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impacted people’s thoughts, feelings, and behavior across multiple levels of analysis.

3.2 Experimental Results In terms of short-term effects, we find that self-distancing leads people to report reexperiencing their negative emotions less than people who analyze their feelings from a self-immersed perspective (e.g., Ayduk & Kross, 2008; Kross & Ayduk, 2008; Kross et al., 2005, 2012; Mischowski, Kross, & Bushman, 2012; Wisco & Nolen-Hoeksema, 2011). For example, when asked to indicate the degree to which they “relived the negative emotions that had originally felt as they analyzed their feelings during the study” or to rate how they felt immediately after the analysis phase of the study (e.g., “how sad do you feel now?”), participants in the self-distanced group displayed lower levels of negative emotional reactivity. How does self-distancing lead to these changes in emotions? To answer this question, we asked participants to describe in writing the “stream of thoughts that flowed through their mind” as they reflected on their feelings during the study. We then had judges content analyze the essays participants generated for the degree to which participants focused on recounting the emotionally arousing features of their recalled negative experience (i.e., What happened to me? What did I feel?) and the degree to which they focused on reconstruing their experience in ways that provided them with insight and closure.a We find that adopting these different perspectives change the way people think about their experience. Participants in the self-distanced group focus less on recounting the emotionally arousing features of their negative experience and more on reconstruing it in ways that provide them with insight and closure (e.g., Self-Immersed example: “I was appalled that my boyfriend told me he couldn’t connect with me because he thought I was going to hell. I cried and sat on the floor of my dorm hall-way and tried to prove to him that my religion was the same as his …”; Self-Distanced example: “I was able to see the argument more clearly … I initially empathized better with myself but then I began to understand how my friend felt. It may have been irrational but I understand his motivation …”). This shift in thought content, in turn, leads participants in the self-distanced group to report experiencing less a

In later studies (e.g., Ayduk & Kross, 2010b; Kross et al., 2012; Park, Ayduk, & Kross, 2016; White, Kross, & Duckworth, 2015), we developed self-report questions to tap into these constructs and found that the above-mentioned experimental manipulations predict scores on these measures similarly to how they predict these constructs when they are identified through essay content analyses.

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distress (Kross & Ayduk, 2008; e.g., Kross et al., 2005; Mischowski et al., 2012; also see, Schartau, Dalgleish, & Dunn, 2009). Notably, the benefits of self-distancing also extend over time (Ayduk & Kross, 2010b; Kross & Ayduk, 2008; Penner et al., 2016; Verduyn, Van Mechelen, Kross, Chezzi, & Van Bever, 2012). For example, in one study, participants who reflected on their feelings from a self-distanced perspective estimated that they thought about their negative experience less up to 1 week after the study compared to participants who analyzed their feelings from a self-immersed perspective (Kross & Ayduk, 2008). They also reported experiencing less distress when they were asked to think about their negative experience again a week later. We have also compared the short- and long-term effects of selfdistancing against distraction. Distraction provides a particularly attractive strategy to compare self-distancing against because like self-distancing, we expected cueing a person to distract themselves immediately after recalling a negative experience would reduce their negative feelings (e.g., Rusting & Nolen-Hoeksema, 1998). Indeed, as most people who have had the experience of watching a movie to take their mind off a problem can attest, shifting one’s attention away from a distressing memory often provides enormous temporary relief. But unlike self-distancing, we did not expect distraction to change the way people mentally represent their negative experience. Thus, the moment a person stops distracting and refocuses on their painful experience, we expected their negative feelings to return. This is exactly what we found in a study that compared self-distancing against distraction (Kross & Ayduk, 2008). In the short term, we found no differences between the two strategies—both led participants to report experiencing less distress compared to participants in a self-immersed comparison group. Over time, however, the effects of distraction and selfdistancing diverged. Compared to participants in the self-immersion and distraction conditions, those in the self-distancing group reported lower emotional reactivity in a subsequent session during which they were asked to think again about the same negative experience but this time without receiving any instructions about how to think about it. Furthermore, the self-distancing group reported ruminating about this experience less during the time separating the two lab sessions. In contrast, participants in the distraction group were significantly more vulnerable to rumination and emotional reactivity over time. In fact, they were indistinguishable from participants in the self-immersed group on each of these long-term measures.

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Taken together, these early findings provided initial evidence that reflecting over negative experiences from a self-distanced perspective reduces people’s experience of negative emotions in the short term and leads to changes in the way they mentally represent past experiences in ways that facilitate coping over time. However, these findings also raised many other interesting questions. In the next section of the chapter, we discuss how research has attempted to answer these questions to deepen our understanding of how this process operates and explore its translational potential.

3.3 Spontaneous Self-Distancing Our initial results indicated that instructing people to adopt a self-distanced perspective as they analyze their feelings facilitates adaptive self-reflection. But how relevant is this process for explaining why people differ in their level of distress when they reflect on negative experiences during their daily lives? In particular, do some people spontaneously self-distance when they reflect on painful episodes from their past? And if so, do they likewise benefit from engaging in this process? To address these questions, we modified our experimental paradigm for studying self-distancing to assess individual differences. As in previous studies, we asked participants to recall and then analyze their feelings surrounding a negative past experience, but this time, we did not manipulate the vantage point they adopted. Instead, we subsequently asked participants to rate the extent to which they spontaneously adopted a self-distanced (vs self-immersed) perspective as they reflected on their feelings (e.g., “As you thought about this event, to what extent did you feel like you were a distanced observer of what happened (i.e., watched the event unfold as an observer, in which you could see yourself from afar) vs an immersed participant in the experience (i.e., saw the event replay through your own eyes as if you were right there) as you replayed the experience in your minds eye?”; Ayduk & Kross, 2010b). Several studies using this paradigm indicate that spontaneously adopting a self-distanced perspective when analyzing negative emotions leads to a similar profile of benefits as when this process is experimentally manipulated (e.g., Ayduk & Kross, 2010b; Grossmann & Kross, 2010; Park et al., 2016; Penner et al., 2016). For example, higher levels of spontaneous selfdistancing predict lower levels of negative affect, and this relation is mediated by shifts in participants’ tendency to recount vs reconstrue their negative experiences. Moreover, spontaneous self-distancing predicts lower levels of rumination over time, as well as reductions in how distressed people

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report feeling about their negative experience when cued to think about it again in a subsequent session approximately 7 weeks later. Additional studies have explored the relation between spontaneous selfdistancing and other aspects of people’s emotional experiences at the daily level. Specifically, using an innovative experience sampling procedure, Verduyn et al. (2012) showed that the duration of people’s emotional responses to daily positive and negative experiences were shorter when they reflected on their experiences from a self-distanced perspective. These field results are consistent with many of our initial laboratory results concerning the role that spontaneous self-distancing plays in facilitating emotion regulation (Ayduk & Kross, 2010b).

3.4 Behavioral Implications Another question raised by our early findings concerned whether selfdistancing has behavioral implications, particularly in the context of aggression. Aggression became relevant because a number of studies indicate that ruminating about interpersonal transgressions increases the likelihood of engaging in physically violent behavior (Bushman, 2002; Bushman, Bonacci, Pedersen, Vasquez, & Miller, 2005). Given the personal and societal costs associated with aggressive responses, we reasoned that understanding how this behavior can be attenuated represents an important question. Thus, we investigated whether self-distancing could buffer people against aggressive reactions to perceived transgressions, paralleling its effect on attenuating negative affect and rumination. Initial evidence suggesting that self-distancing has implications for curbing aggression came from a daily diary study that asked each member of a dating couple to indicate whether they experienced a conflict with their partner at the end of each day (over the course of 21 consecutive days) and, if so, to rate the extent to which they adopted a self-distanced perspective when we asked them to think about that conflict again at the end of the day (using the spontaneous self-distancing measure described earlier). We found that romantic partners who reported reflecting on their daily relationship conflicts from a self-distanced perspective during a 3-week daily diary study were significantly less likely to behave in a way that escalated hostility during a conflict discussion task with their partner in the laboratory. That is, people higher in spontaneous self-distancing remained relatively constructive (i.e., they demonstrated adaptive problem solving behavior and partner perspective taking) during the conflict discussion, regardless of the degree to which their partners were hostile (Ayduk & Kross, 2010a, 2010b).

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In contrast, the hostility of people lower in spontaneous self-distancing increased linearly with that of their partners, revealing a tit-for-tat interactional style that leads to conflict escalation and predicts poor long-term outcomes in close relationships (Ariaga & Rusbult, 1998; Gottman, Coan, Carrere, & Swanson, 1998). These findings were later conceptually replicated and extended in an experimental context. Specifically, Mischowski et al. (2012) randomly assigned participants to reflect on why they felt the way they did after being provoked by a confederate from either a self-distanced or a self-immersed perspective. A third group of participants was randomly assigned to a no-instruction control condition. After reflecting on their feelings, participants were given the opportunity to retaliate against the confederate who antagonized them by controlling the volume and duration of noise blasts they administered to them during a subsequent task. The findings showed that participants in the self-distancing group administered noise blasts that were shorter and less intense (i.e., less aggressive) compared to participants in both of the other conditions. Furthermore, the self-immersed and control groups did not differ from each other. These findings provide causal evidence suggesting that self-distancing attenuates aggressive behavior.

3.5 From Adults to Children As we pursued the above work, we became aware of work from the developmental domain indicating that children and adolescents’ chronic tendencies to ruminate contribute to the development of a range of emotional disorders (Abela, Brozina, & Haigh, 2002; Broderick & Korteland, 2004; Burwell & Shirk, 2007; Hankin, 2008; Nolen-Hoeksema, Stice, Wade, & Bohon, 2007; Schwartz & Koenig, 1996; Ziegert & Kistner, 2002), which raised another question: Might the benefits of self-distancing extend to this age group? We addressed this issue in one study by randomly assigning middle school children (age 10 on average) to reflect on their feelings surrounding a recent anger-related episode (e.g., a fight with a friend or sibling) from either a self-distanced or a self-immersed perspective, using a version of the manipulations that closely mirrored those used in our studies with young adults (Kross, Duckworth, Ayduk, Tsukayama, & Mischel, 2011). Replicating prior research with adults, children in the self-distancing group displayed significantly lower levels of negative affect after analyzing their feelings

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compared to children in the self-immersed group. Moreover, as in our studies with young adults, we asked children to describe the stream of thoughts that flowed through their mind as they thought about their negative experience. Judges’ ratings of these essays indicated that children in the selfdistanced group focused significantly less on recounting the emotionally arousing features of their memory and relatively more on reconstruing their experience, which partly explained how self-distancing reduced distress (for a conceptual replication, see White, Kuehn, Kross, & Ayduk, under review). Consistent with these findings, a follow-up study demonstrated that the more adolescents spontaneously self-distanced when analyzing their negative feelings, the less emotional reactivity they displayed, the more they focused on reconstruing their experience, and the less they focused on recounting it (White et al., 2015). But perhaps most interestingly, the inverse relation between spontaneous self-distancing and emotional reactivity strengthened with age in this study, suggesting that the benefits associated with self-distancing increase with development. Taken together, these findings provide preliminary evidence highlighting the role of self-distancing in fostering adaptive self-reflection among children and adolescents. They also begin to illuminate the role that development plays in strengthening one’s ability to adopt a self-distanced perspective, suggesting the need for future research to explore this issue further.

3.6 Clinical Generalizability One of the most frequent questions asked about our early research concerned whether the beneficial effects of self-distancing extend to individuals suffering from clinical disorders characterized by extreme forms of rumination and distress. Research has begun to address this issue in several contexts. We describe each in turn. 3.6.1 Dysphoria and Major Depressive Disorder Few conditions are as synonymous with the concept of rumination as depression. Indeed, a large amount of research has identified rumination as a cognitive process that triggers and maintains depression and dysphoria (Nolen-Hoeksema et al., 2008). Given this, we reasoned that examining whether the benefits of self-distancing extend to people suffering from depression and dysphoria would provide an ideal first place to examine the translational potential of our previous findings (for a similar perspective, see Dalgleish & Werner-Seidler, 2014).

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To address this issue, we pooled data across several self-distancing experiments that included unanalyzed Beck Depression Inventory data (Barnhofer et al., 2015; Beck, Steer, Ball, & Ranieri, 1996). We then examined whether participants’ scores on this index of depressive symptoms moderated the benefits of self-distancing for reducing emotional reactivity (Kross & Ayduk, 2009). Our results indicated that the benefits associated with self-distancing increased linearly with depressive symptoms. Specifically, whereas participants who scored low on depressive symptomatology did not benefit from self-distancing, as participants’ depressive symptoms increased, so did the benefits they derived from adopting a self-distanced perspective (as one might expect, we also observed a main effect of depressive symptoms—i.e., the more depressive symptoms participants reported, the worse they felt when they analyzed their feelings). Although these findings provided initial data suggesting that the benefits associated with self-distancing might generalize to participants with depression, the limitations associated with using self-report measures of depressive symptoms to draw inferences about clinical depression are well documented (Coyne, 1994). Therefore, we next examined whether a similar pattern of results would be observed among a sample of individuals diagnosed with major depressive disorder and their age-matched healthy controls (Kross et al., 2012). This was indeed the case. Depressed participants who were instructed to analyze their feelings from a self-distanced perspective reported experiencing less negative affect after analyzing their emotionally upsetting memories, compared to depressed participants in the self-immersed group. They also displayed lower levels of negative thought accessibility. We also examined the links between self-distancing and avoidance in this study to ensure that the above effects were not driven by the distancing manipulation simply leading people to avoid focusing on the emotional content of their recalled negative experiences. Across both implicit and explicit measures of avoidance,b we found no evidence to support this idea. These findings argue against the idea that self-distancing serves a maladaptive, avoidant function when people engage in this perspective to make sense b

Avoidance was assessed explicitly by asking participants to rate whether they “tried to avoid thinking about” their experience when they were prompted to recall it and whether they tried to “suppress [or push] away” their feelings about it. Implicit avoidance was assessed by examining the dissociation between scores on self-report and implicit emotional reactivity measures included in Kross et al. (2012), under the premise that people who repress their emotions (i.e., a sign of avoidance coping) display high scores on implicit measures of emotionality but low scores on self-report measures.

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of their feelings (also see, Ayduk & Kross, 2009; Kross & Ayduk, 2008; Kross, Duckworth, et al., 2011; Kross et al., 2012). Finally, we did not observe any beneficial effects of self-distancing among healthy control participants. The latter finding is consistent with previous work showing that rumination inductions do not lead to changes in mood among individuals who are low in dysphoria (Nolen-Hoeksema et al., 2008), presumably due to the fact that they have little negative affect to downregulate in the first place. More broadly, these findings suggest that a certain level of negative affect may be needed to observe beneficial effects of self-distancing (for similar results, see Pfeiler, Wenzel, Weber, & Kubiak, 2015). Although the earlier findings suggest that the emotion regulation benefits associated with self-distancing may be particularly pronounced for people suffering from moderate to severe symptoms of depression, it is important to note that Wisco and Nolen-Hoeksema (2011) directly replicated our initial study on this topic (Kross & Ayduk, 2009) and did not find evidence indicating that self-distancing was more effective for regulating negative affect among dysphoric participants compared to nondysphoric participants. Instead, they found that self-distancing worked equally well for participants regardless of their level of depressive symptoms. Thus, although emerging evidence suggests that self-distancing predicts beneficial outcomes for depressed individuals, whether they benefit significantly more than healthy controls remains unclear. 3.6.2 Bipolar Disorder Is the regulatory effect of self-distancing unique to reducing negative affect or does engaging in this process attenuate the intensity of emotion regardless of its valence? A number of researchers have begun to explore this idea in a clinical context by studying bipolar disorder, a mood disorder characterized by persistent and abnormally elevated positive mood states (Angst, Stassen, Clayton, & Angst, 2002), as well as periods of depression. For example, Gruber, Harvey, and Johnson (2009) randomly assigned individuals with bipolar disorder and a healthy control group to reflect on their feelings surrounding a time in which they felt intense happiness from either a selfimmersed or a self-distanced perspective. Their results indicated that both bipolar participants and healthy control participants who were instructed to reflect on positive experiences from a self-distanced perspective reported lower levels of positive affect and displayed lower autonomic nervous system reactivity compared to participants in the self-immersed group.

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Park et al. (2014) recently extended these findings by examining whether people with bipolar disorder spontaneously self-distance less than control participants when reflecting on positive experiences with the hypothesis that a relative lack of distancing (or higher self-immersion) may explain why individuals with bipolar disorder become more emotionally reactive than healthy participants when they reflect on positive experiences. Partially supporting their prediction, they found that individuals who had bipolar disorder and a history of psychosis were less likely to spontaneously self-distance when reflecting on their positive experiences compared to healthy control participants or participants with bipolar disorder who had no history of psychosis. Moreover, across all conditions, the more participants reported spontaneously self-distancing while reflecting on their positive memories in this study, the less self-reported and neurophysiological emotional reactivity they displayed. From a basic science perspective, these findings are important because they demonstrate that the effects of self-distancing on dampening emotional reactivity are not restricted to negative experiences; they extend to positive emotional experiences as well. They also provide preliminary data suggesting that self-distancing may provide a useful tool for helping people grapple with intense positive emotional reactions that are the require intervention. 3.6.3 Coping With Trauma Research surrounding posttraumatic stress is one area where circumstantial evidence suggests self-distancing might be harmful. Specifically, prior research indicates that people who are diagnosed with posttraumatic stress tend to spontaneously adopt a self-distanced perspective when they recall trauma experiences. This tendency is often conceptualized as a maladaptive avoidance mechanism—i.e., people with posttraumatic stress reflexively adopt an observer perspective to blunt the pain associated with thinking about traumatic events (Berntsen, Willert, & Rubin, 2003; Kenny & Bryant, 2007; Kenny et al., 2009; McIsaac & Eich, 2004). However, all of this work have focused on the role that self-distancing plays in promoting distress when participants recall negative emotional experiences. Until recently, no work had examined whether self-distancing serves a similar maladaptive function when people actively analyze their negative experiences to work-through traumatic events. To fill this gap in the literature, Wisco et al. (2015) randomly assigned veterans diagnosed with posttraumatic stress disorder to analyze their feelings surrounding a trauma experience from either a self-immersed or

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a self-distanced perspective. Although they found no differences between the two groups on a self-report measure of emotional reactivity, participants in the self-distanced condition displayed lower levels of physiological reactivity (e.g., heart rate and skin conductance). Another study by Penner et al. (2016) asked caregivers of pediatric cancer patients to analyze their feelings surrounding their child’s recent painful cancer treatment experiences and then measured the degree to which participants spontaneously self-distanced while analyzing their feelings. Pediatric cancer caregivers often attend their child’s frequent painful cancer treatment, and such treatments are well known to cause substantial traumatic distress. Thus, they represent a particularly relevant sample to examine issues concerning how self-distancing influences people’s ability to cope with ongoing trauma. Conceptually replicating prior research on depression and bipolar disorder (Kross & Ayduk, 2009; Kross et al., 2012; Park et al., 2014), their findings indicated that self-distancing buffered high (but not low) trait anxious caregivers against elevated levels of anticipatory anxiety during their child’s subsequent painful cancer treatments. Importantly, it also buffered high trait anxious caregivers against elevated levels of psychological distress 3 months after their spontaneous self-distancing levels were initially assessed. Critically, they found no relation between spontaneous self-distancing and avoidance, which suggests that adopting this perspective to analyze (rather than simply recall) negative experiences may represent a distinct psychological process with unique outcomes. Together, the results from these two initial studies provide promising preliminary evidence suggesting that self-distancing may be useful for helping people analyze their feelings surrounding trauma experiences. However, more research is needed to examine this issue to more fully understand the role that this process plays in clinically diagnosed posttraumatic stress disorder.

3.7 Implications for Physical Health It is well established that the experience of psychological pain is often accompanied by symptoms of physical pain and distress as well (e.g., Brosschot et al., 2006; Eisenberger, Lieberman, & Williams, 2003; Gerin et al., 2006; Kross, Berman, Mischel, Smith, & Wager, 2011). One particularly important physical response our bodies show in response to stress is increased blood pressure. From a physical health perspective, acute

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increases in blood pressure in response to stress is adaptive; it shows that the body is mobilizing its resources to meet the demands of a difficult situation. However, when blood pressure levels remain elevated over extended periods of time, the risk of cardiovascular disease increases. Unfortunately, ruminating over negative experiences leads to exactly this pattern—blood pressure levels go up when people recall highly arousing negative past experiences and remain elevated as people continue to think about those events (e.g., Brosschot et al., 2006; Gerin et al., 2006). Does self-distancing attenuate such prolonged cardiovascular reactivity? Several studies indicate that it does. Specifically, regardless of whether people are led to adopt a self-distance perspective in the lab or engage in this process spontaneously, they display less cardiovascular reactivity when they analyze their feelings. More importantly, their blood pressure returns to baseline faster than people who self-immerse, suggesting that self-distancing facilitates physiological recovery from stress (Ayduk & Kross, 2008, 2010b; also see, Gruber et al., 2009; Wisco et al., 2015).

3.8 Neural Correlates Research has also begun to explore the neural correlates of reflecting over negative experiences from a self-distanced perspective. In one study, Kross, Davidson, Weber, and Ochsner (2009) instructed participants to use a distancing strategy that was conceptually similar to those used in our prior work with adults as they reflected on highly arousing negative autobiographical experiences. Results linked the use of this strategy with lower self-reported negative affect, as well as reduced activation in a network of cortical midline regions that support self-referential processing (Berman et al., 2010), including the subgenual anterior cingulate cortex. Identifying a modulatory link between the use of a self-distancing strategy and activation in this latter region was particularly noteworthy, because the subgenual anterior cingulate has been shown to play a key role in depression and rumination. Specifically, depressed individuals display higher levels of activation in this region compared to control participants. Furthermore, various interventions that are effective at treating depression lead to reductions in activation in this area (for a review, see Ressler & Mayberg, 2007). Thus, demonstrating a link between the use of a self-distancing strategy and activation in this region of the brain is broadly consistent with the idea that self-distancing attenuates rumination, facilitating adaptive self-reflection.

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A more recent study by Christian, Parkinson, Macrae, Miles, and Wheatley (2015) also linked the use of a self-distancing strategy while reflecting over a hypothetical negative emotional experience (e.g., stubbing one’s toe in pain) with reduced self-reported negative affect compared to adopting a first-person perspective. However, the brain regions that were modulated by self-distancing in this study differed from those reported in our prior research. Specifically, in this study, self-distancing was associated with reduced activation in a network of limbic regions associated with emotional reactivity and interoception (e.g., the insula), not modulations of cortical midline regions (Christian et al., 2015; also see, Eich, Nelson, Leghari, & Handy, 2009). Finally, a large program of research on reappraisal has examined the implications of cueing people to reinterpret their negative feelings using a distancing strategy that involves adopting the perspective of a clinical, detached observer for reducing self-report and neural markers of distress (e.g., Dorfel et al., 2014; Kalisch et al., 2005; Koenigsberg et al., 2009, 2010; Ochsner et al., 2004). These studies consistently link the implementation of this strategy with reductions in self-report distress. However, in contrast to the previous studies, they tend to link the use of this strategy with reduced activation in the amgydala as opposed to the brain regions described in the previous two studies (Buhle et al., 2014). At a broad level, these studies are consistent with each other insofar as they demonstrate inverse links between self-distancing strategies and selfreported negative affect. However, they are inconsistent in terms of the specific patterns of neural activity they link with distancing. One possible explanation for these inconsistent findings concerns the fact that different instructions are used to manipulate self-distancing and to induce negative affect across these studies. For example, the emotion inductions used in the previous studies ranged from having participants recall painful emotional experiences from their past to imagining physically painful episodes (e.g., cutting a finger) to viewing aversive images. And the distancing strategies that participants were taught to use across these studies were equally heterogeneous. Thus, future research is needed to systematically examine the neural mechanisms that underlie different interactions between distancing strategies and emotion inductions. Addressing this issue is important for advancing our understanding of the neural mechanisms that underlie the emotion-regulatory benefits of distancing.

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3.9 From the Past to the Future The majority of the aforementioned work deals with how people can effectively work-through negative past experiences. But past experiences are not the only kinds of negative events that people struggle to make sense of. People often anxiously worry about future events as well. To further examine the generalizability of our prior research, we recently examined in a large sample of children and young adults (n ¼ 2424) whether self-distancing is likewise useful for helping people cope with future events (White et al., under review). Conceptually replicating our prior research, self-distancing predicted reductions in self-reported distress when people reflected on anticipated future negative experiences (e.g., worrying about failing an exam or having an illness), regardless of whether it was experimentally manipulated or spontaneously assessed. However, in contrast to the bulk of work reviewed earlier, participants’ tendency to recount vs reconstrue their future experience did not explain how self-distancing predicted these reductions in negative emotional reactivity. Instead, imagery vividness did: self-distancing led participants to imagine their future negative experience less vividly, which in turn predicted declines in how distressed people felt. In conjunction with our prior research, these data suggest that selfdistancing facilitates people’s ability to reflect adaptively over both negative past and future events. However, they also suggest that a different set of mechanisms may underlie how self-distancing facilitates adaptive selfreflection across these contexts.

3.10 Summary Collectively, these findings demonstrate that cueing people to reason about negative experiences from a self-distanced perspective leads to changes in the way people cognitively represent negative experiences that have several positive downstream implications for how people think, feel, and behave. However, they also raise several additional questions that are important to address to advance our understanding of how this mechanism operates.

4. SELF-TALK In all of the above-mentioned work, self-distancing was manipulated by asking people to visualize themselves in their past or future experiences from afar. This technique proved useful for helping people work-through

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their feelings surrounding a range of negative past and possible future experiences. However, its utility for helping people manage their emotions in vivo, as negative experiences actively unfold in their daily lives, was unclear. After all, it is not as though people can easily close their eyes and picture themselves from a “fly on the wall perspective” while they’re in the midst of experiencing a stressful event. Often times, people cannot feasibly engage in this visual shift, which raised the question: Can people self-distance in the moment, and if so, how? To address these questions, we shifted to the domain of language. We observed that in everyday life, there are times when people refer to themselves using their name or other non-first-person pronouns (e.g., “you” or “he” or “she”), particularly in contexts that require emotion regulation. For example, consider this quote from Malala Yousafzai. When asked by Jon Stewart to describe how she responded when she discovered that the Taliban were plotting to kill her, Malala Yousafzai, the youngest person to ever win the Nobel Peace Prize, responded, “I used to think that the Tali[ban] would come and he would just kill me. But then I said [to myself], if he comes, what would you do Malala? Then I would reply to myself, Malala just take a shoe and hit him ….” Might this shift from using first-person to non-first-person language when reflecting silently on one’s emotions serve a self-regulatory function? We hypothesized that it would. Specifically, we reasoned that using one’s name and other non-first-person pronouns to refer to oneself during silent introspection would serve a self-distancing function under the premise that people typically use these parts of speech when thinking about and communicating with other people. Thus, we reasoned that when people use these parts of speech to refer to the self, it should lead them to think about the self more objectively, as though they were someone else (albeit another person whose inner thoughts and feelings they have privileged access to). In turn, we expected this enhanced psychological distance to result in adaptive outcomes similar to those we observed in our prior work.

4.1 Initial Studies To test this idea, we first examined the connection between linguistic and visual self-distancing. Research on construal level theory indicates that different types of distancing dimensions are related—i.e., enhancing psychological distance in one domain should lead to enhancements in distance in other domains (Ledgerwood, Trope, & Liberman, 2010; Liberman &

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Trope, 2008; Trope & Liberman, 2003, 2010). Thus, we reasoned that if using one’s name and other non-first-person pronouns to refer to the self serves a distancing function, then cueing people to engage in this type of non-first-person self-talk should also lead them to adopt a self-distanced visual perspective to a greater degree on a subsequent task. We tested this idea by asking participants across two studies to reflect on their feelings surrounding an anger (Study 1) or depression (Study 2)-related event using either their own name and non-first-person pronouns (nonfirst-person self-talk) or first-person singular pronouns (first-person self-talk; see Table 1 for sample instructions). After the manipulation, participants rated the degree to which they adopted a self-distanced visual perspective as they reflected on their past experience by answering the same questions we used to assess spontaneous visual self-distancing in our prior studies. As expected, participants in the non-first-person self-talk group displayed significantly higher levels of visual self-distancing compared to participants in the first-person groups, providing preliminary evidence that using non-first-person pronouns and one’s own name to refer to the self enhances self-distance.

Table 1 Self-Talk Manipulation Instructions First-Person Self-Talk Instructions Non-First-Person Self-Talk Instructions

One of the things we are interested in One of the things we are interested in this study is the language people use to this study is the language people use to understand their feelings understand their feelings Some people try to understand their feelings by thinking about themselves using first-person pronouns, so that is what we would like you to do

Some people try to understand their feelings by thinking about themselves using their own name and other nonfirst-person pronouns, so this is what we would like you to do

Please try to understand why you felt the way you did in the experience you just recalled using the pronouns “I” and “my” as much as possible

Please try to understand why you felt the way you did in the experience you just recalled using the pronouns “you” and “your own name” as much as possible

In other words, ask yourself, “Why did I feel this way? What were the underlying causes and reasons for my feelings?”

In other words, if your name was Jane, you would ask yourself “Why did Jane feel this way? What were the underlying causes and reasons for Jane’s feelings?”

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4.2 Implications for Emotion Regulation Having established that third-person self-talk enhances self-distancing, our next step was to examine whether cueing people to reflect on their emotions using these parts of speech would enhance their ability to control their thoughts, feelings, and behavior under stress. In one experiment, we examined these questions by recruiting participants for a study on the “psychology of first impressions” (Kross et al., 2014, Study 2). Participants were told that the study’s purpose was to identify how good each participant was at making a good first impression on a member of the opposite sex—a potent procedure for inducing social anxiety among young adults (Clark & Arkowitz, 1975; Turner, Beidel, & Larkin, 1986). They were further told that their conversation would be tape-recorded and viewed by psychologists who were trained to evaluate how well they performed. After receiving this cover story, an experimenter went onto explain to participants that “we are interested in the different ways people go about preparing themselves psychologically for meeting new people and what effect each type of self-preparation has on performance.” Half of the participants were then randomly assigned to reflect on their feelings surrounding the upcoming interaction using first-person pronouns, whereas the other half were asked to reflect on their feelings using non-first-person pronouns and their own names. They were then escorted into an adjacent room where a confederate of the opposite sex greeted them, and where they did their best to make a good first impression while their performance was recorded. Judges who were blind to participants’ condition watched these videos and rated the performance of the participants in the non-first-person group to be better overall than the first-person group. Non-first-person participants also reported significantly lower levels of anxiety following their interaction compared to participants in the first-person group. These findings were later conceptually replicated in another laboratory study using a different type of social stress induction (Kross et al., 2014, Study 3). Specifically, participants were brought into the lab for a speechtask study. They were told at the study’s start that they would be asked to deliver a speech on why they were ideally qualified to land their dream job and were given 5 min to prepare their speech. Following this social stress induction procedure, they were again randomly assigned to reflect on their current feelings of anxiety using either first-person pronouns or non-firstperson pronouns. They were then taken to an adjacent room and asked

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to deliver a speech in front of a panel of evaluators while their performance was videotaped. Consistent with the results of the first impressions study, judges blind to condition rated participants in the non-first-person group as more confident, less nervous, and performing better overall than participants in the firstperson group. Furthermore, participants in the non-first-person group reported experiencing less shame and embarrassment after their speech was over and ruminated less about their performance over time.

4.3 Challenge vs Threat Construals The earlier findings demonstrated that small shifts in the language people use to refer to the self during introspection have implications for how people feel and behave under stress. But how do such subtle linguistic shifts impact these outcomes? To tap into how these manipulations influenced people’s thought process, we reran the speech study described earlier. But this time, we asked participants to describe in writing the stream of thoughts that flowed through their mind as they reflected on their feelings using firstor non-first-person pronouns immediately after they engaged in this introspective task. We then coded these stream of thought essays for challenge and threat construals. We focused on these construals for two reasons. First, prior research indicates that when people find themselves in situations that elicit social stress, they automatically appraise the situation along a challenge-threat continuum (Blascovich & Tomaka, 1996; Lazarus & Folkman, 1984). Challenge construals refer to appraisals in which an individual believes that they have the personal resources to cope with the stressful circumstance in which they find themselves. Threat construals capture the opposite—a person takes stock of what is required of them and judges that they do not possesses enough resources to cope with the demands of situation. Second, prior research indicates that self-distancing leads people to focus less on the hot, emotionally arousing features of negative experiences (Ayduk & Kross, 2010a, in press; Fujita, Trope, Liberman, & Levin-Sagi, 2006; Kross, 2009; Trope & Liberman, 2003, 2010). And in the context of the speech-task study, we reasoned that focusing on these emotionally arousing features of the situation (i.e., what the person has to do—give a speech on a topic they were unprepared for) is precisely what should lead them to judge that they do not possess the resources to cope with the situation.

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Condition-blind judges’ content analyses of participants’ essays generated evidence that confirmed these predictions—the essays of participants in the non-first-person condition contained significantly more challenge vs threat construals than the first-person condition. We also asked participants to complete a self-report questionnaire that assessed their level of challenge vs threat appraisals, and scores on this self-report measure were consistent with the essay analyses (Kross et al., 2014) providing converging evidence that linguistic self-distancing shifts a person’s construal of a situation. A follow-up generalizability study asked an older (mean age ¼ 35) ethnically diverse sample of individuals living across the country to write about their deepest thoughts and feelings surrounding a future anxiety-provoking experience using either first-person pronouns or non-first-person pronouns and their name. One advantage of having participants write about their feelings using these parts of speech (rather than first think about their experiences and then recollect back to how they thought about them when they followed the manipulation instructions) is that the writing samples provide a direct window into how the language manipulations influence the way people appraise upcoming social stressors. Thus, at the end of the study, we asked judges to content analyze these writing statements for the same types of thought content (challenge vs threat appraisals) that were coded in our previous study. Consistent with the results of that study, judges rated the essays of participants in the non-first-person group as containing more challenge vs threat appraisals compared to participants in the first-person group (for examples, see Table 2).

4.4 From the Lab to Daily Life The above-mentioned findings suggest that the language people use to refer to the self-influences the way they think, feel, and behave under stress. But do these findings generalize outside the lab when people are forced to grapple with stress in vivo? In the autumn of 2014, the threat of an Ebola epidemic in the United States provided us with a unique opportunity to address this question. During this time period, anxiety surrounding the possibility of a widespread Ebola outbreak gripped the United States, despite public health officials’ repeated announcements that the actual risk of such an event was low. According to one nationally representative poll conducted during this time period, approximately 52% of adults living in the United States were anxious

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Table 2 Sample Threat and Challenge Appraisals as a Function of Self-Talk Manipulation Stream of Thought Essay Samples First-person condition

I thought that I was so nervous because when I give a speech, I need to feel prepared; however, I do not think I am prepared enough to give a speech such as this one I cannot prepare an oral speech in 3 min. It takes days for me to examine my strengths, weaknesses, etc. I need to have my oral speech written down and perfected, and therefore, this is not going to work out Nervousness. Shock. Not much time to prepare. What did I get myself into? Oh, my goodness. My palms are sweating. What are my weaknesses? Think of really good strengths Non-first-person condition

First, I asked myself what was I nervous about? It is not like this will be the first interview or speech I have ever had to give. And even if it does not go perfectly, it won’t be the end of the world. I mostly think reassuring and comforting thoughts to motivate and encourage myself The topic of my speech, specific wording, the times that I have given a speech like this before. The fact that it is not a “speech” and that word is often associated with a scare tactic and panic inducer I told myself that I’m not under a lot of pressure for this. I’m qualified and have worked hard; I have confidence in my abilities Note: Seventy-three percent of participants who received the highest possible score on the challenge-tothreat ratio variable were in the non-first-person group; 67% of participants who received the lowest possible score on the challenge-to-threat ratio variable were in the first-person group. Reproduced from Kross, E., Bruehlman-Senecal, E., Park, J., Burson, A., Dougherty, A., Shablack, H., … Ayduk, O. (2014). Self-talk as a regulatory mechanism: How you do it matters. Journal of Personality and Social Psychology, 106(2), 304–324. http://dx.doi.org/10.1037/a0035173.

about an Ebola epidemic (Harvard School of Public Health Poll, 2014). Given that self-distancing helps people think differently about emotional experiences, might cueing people to reflect on their deepest thoughts and feelings surrounding Ebola allow them to reason more rationally about their risks of contracting the disease and thus reduce their Ebola-related anxiety? We examined this question by randomly assigning 1257 people from across the United States to write about their deepest concerns regarding Ebola using either their own name and non-first-person pronouns or firstperson pronouns as concerns about Ebola swelled (10/24/14–10/26/14). Judges then coded participants’ essays for statements indicating that

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participants focused on fact-based reasons why they should not worry about Ebola (e.g., because the medical infrastructure in the United States is superior to Western Africa; Ebola does not spread by air). To ensure that judges were blind to participants’ condition, all non-first-person essays were converted into first-person essays prior to coding. Analyses of judges’ ratings indicated that participants who were randomly assigned to use their name to think about Ebola generated significantly more fact-based reasons not to worry about an outbreak compared to participants who used first-person singular pronouns. In turn, focusing on fact-based reasons not to worry led participants in the non-first-person group to report experiencing less anxiety about Ebola after the manipulation, and reduced their risk perception surrounding the prospect of them contracting the disease (Kross et al., under review). Perhaps most interestingly, the benefits associated with non-first-person self-talk were most pronounced among participants who scored the highest on a baseline measure of Ebola anxiety completed at the start of the study. Specifically, whereas participants who scored particularly low on a baseline measure of Ebola anxiety did not accrue any benefits from the manipulation, participants who scored high on this measure did. These findings suggest that self-distancing manipulations work as well, and possibly better (Kross & Ayduk, 2009; Kross et al., 2012; Park et al., 2014; Penner et al., 2016), for vulnerable individuals (compared to nonvulnerable individuals). They also highlight the potential “realworld” value that self-talk manipulations may have for helping anxious individuals cope effectively with anxiety-provoking stressors in their daily lives.

4.5 An Effortless Form of Self-Control? Although self-control and emotion regulation are typically thought of as effortful processes (Baumeister, Vohs, & Tice, 2007; Heatherton, 2011; Moser, Krompinger, Dietz, & Simons, 2009; Ochsner & Gross, 2008), participants in the aforementioned studies consistently indicated during informal debriefings that it was quite easy for them to engage in non-first-person self-talk, which raised the question: Might this process constitute a relatively effortless form of self-regulation? We predicted that it would for two reasons. First, recent work suggests that people reason more wisely about other people’s negative emotional experiences than their own (Grossmann & Kross, 2014). Second, people

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virtually exclusively use names and other non-first-person pronouns to think about and refer to other people. Thus, there is a very strong association between using these parts of speech and thinking about others—an association that is so strong that we reasoned it might lead people to virtually automatically think about themselves similar to how they think about someone else. Therefore, to the extent non-first-person self-talk allows people to think about themselves similar to the way they think about others, we reasoned it might also allow them to reason about their emotions with relative ease. We tested this prediction by turning to the brain as our level of analysis. Over the past 15 years, an overwhelming amount of data has accumulated that pinpoints the different patterns of neural activity that underlie the experience of self-referential processing and negative emotional reactivity on the one hand, and cognitive control processes on the other (e.g., Araujo, Kaplan, & Damasio, 2013; Buhle et al., 2014). Thus, by using this information, we could ask the question: Does cueing a person to engage in non-first-person self-talk lead to reductions in brain signatures that capture self-referential processing and emotional reactivity with or without leading to increases in brain signatures that capture effortful cognitive control processes? Two studies addressed this issue. In the first study, we (Moser et al., under review) asked participants to introspect about how they felt in response to viewing a series of negatively arousing photographs (e.g., pictures of weapons, mutilated faces, bloody bodies; Lang, Bradley, & Cuthbert, 2008) using either their name or first-person singular pronouns (e.g., I, me, my) in the context of a within-subjects design. We monitored participants’ brain activity using electroencephalogram (EEG) throughout the task, and used the data that this method generated to extract event-related potentials— neurophysiological waveforms that reflect different psychological processes. Participants were trained prior to the study how to implement the manipulations and were simply asked to do so silently in their mind during the study. The results of the study indicated that non-first-person self-talk reduced a neurophysiological marker of self-referential emotional reactivity (i.e., the late positive potential; Hajcak, Weinberg, Macnamara, & Foti, 2012; Moser et al., 2009) within the first second of viewing aversive images without enhancing activation in a neurophysiological marker of effortful cognitive control (i.e., the stimulus proceeding negativity; Brunia, Boxtel, & Bocker, 2012; Moser et al., 2009).

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A follow-up functional magnetic resonance imaging (fMRI) study conceptually replicated these findings using a complementary neuroscience technique. Specifically, participants were asked to reflect on a series of highly arousing negative emotional events (i.e., experiences that led them to feel intensely distressed each time they thought about them) from their past using either their name or I in the context of a within-subjects design. The results indicated that participants reported feeling significantly less distressed on trials in which they reflected on their negative past experiences using their name vs I. At the neural level, participants displayed significantly less activation on name trials compared to I trials in a broad swath of the medial prefrontal cortex (MPFC), which prior research has reliably linked with self-referential processing (Araujo et al., 2013). Critically, we again found no differences between the two conditions (I vs name) in a network of a priori identified brain regions that support effortful cognitive control. This remained true even when the statistical threshold for detecting significant activations was dropped well below conventional standards for detecting significant effects, suggesting that low power was not driving our failure to observe significant results in these cognitive control areas. Although preliminary, these finding suggest that non-first-person selftalk may constitute a relatively effortless self-control process—a finding that, if true, has important basic science and clinical implications.

4.6 Clinical Implications Although no research that we are aware of has directly examined the clinical implications of linguistic self-distancing, two sets of findings support the idea that future research in this area may be a worthwhile endeavor. First, as described in the context of the Ebola study, the participants who benefited the most from non-first-person self-talk were those who scored the highest on a baseline measure of Ebola-related anxiety. Second, several of the previously mentioned studies included trait measures of social anxiety. To examine the potential clinical implications of research on distanced selftalk, we examined whether the aforementioned effects of non-first-person self-talk were moderated by participants’ social anxiety scores by conducting a metaanalysis across all studies in which self-reported social anxiety scores were available (Kross et al., 2014). Although we did not have clinical diagnoses of social anxiety, approximately 10% of the sample across these studies

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scored in the clinically social anxious range according to established guidelines (Carleton, Collimore, McCabe, & Antony, 2011; Connor et al., 2000). The results from this metaanalysis indicated that non-first-person self-talk was equally effective at fostering challenge appraisals, enhancing performance (e.g., better speech), and reducing negative affect among individuals both high and low in social anxiety. Taken together, these findings suggest that linguistic self-distancing may be useful for facilitating emotion regulation among vulnerable individuals, highlighting the need for future research to examine this issue further.

4.7 Converging Evidence Although the above findings are relatively recent, they are beginning to be extended by other groups. For example, Dolcos and Albarracin (2014) recently demonstrated over a series of studies that cueing people to address themselves with the word you led them to perform better on a demanding task (i.e., solving difficult anagrams) and enhanced their intentions to perform well compared to participants who were cued to address themselves using the word I. Interestingly, this group has also found that people are more likely to spontaneously address themselves using you when they encounter situations that require self-guidance (Zell, Warriner, & Albarracin, 2012), further underscoring the role that this process plays in self-regulation. Additional evidence supporting the self-regulatory benefits of non-firstperson self-talk comes from recent studies performed in the developmental domain. For example, White and Carlson (2016) found that 5-year-old children who used their names to reflect on the self outperformed children who used I on an executive functioning task (a seven-level card-sorting task designed for 2–7-year-olds). Interestingly, 3-year-olds did not benefit from this manipulation, a finding that the authors interpreted as suggesting that a certain level of theory of mind may be needed for these manipulations to be effective. A follow-up study by the same group extended these findings to the domain of perseverance (White et al., in press). Specifically, they examined whether cueing 4- and 6-year-old children to reflect on their performance on a boring repetitive task using their names or I influenced their performance on the task. Participants assigned to a third condition were asked to impersonate someone else who they thought was really good at working hard and reflect on their performance as though they were that person.

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The latter manipulation was conceptualized as an additional form of distancing. The findings revealed a linear effect of the manipulation across both age groups—children who impersonated someone else persevered the longest on the task, followed by children who reflected on their performance using their name, with children who reflected on their performance using I performing the worst.

4.8 Summary Collectively, these findings demonstrate how subtle shifts in the language people use to refer to themselves during introspection can influence their capacity to regulate how they think, feel, and behave under stress. It is important to emphasize, however, that all of the above work focuses on the role that non-first-person self-talk plays in enhancing self-regulation when people privately engage in this process (i.e., silently during introspection). There are, of course, times when people engage in non-first-person self-talk out loud. Whether engaging in that process is likewise helpful is unclear, and awaits future research.

5. MENTAL TIME TRAVEL Most of our research on self-distancing has focused on how people can reflect adaptively on negative experiences by self-distancing using visual imagery (i.e., adopting a fly on the wall perspective) or linguistic (i.e., engaging in non-first-person self-talk) techniques. Recently, we have begun to explore whether people can self-distance through an alternative mechanism: by focusing on their future selves. Our motivation to pursue this question stemmed from the recognition that both common wisdom and research suggest that the passage of time improves the way people feel about negative experiences (Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998). But can the healing power of time be harnessed through mental time travel to imagine a future self without having to wait for actual time to pass?

5.1 Experimental Evidence As with linguistic distancing, we first examined the connection between temporal and visual self-distancing. Based on our prior work and construal level theory, our expectation was that to the degree that temporal distancing is a form of self-distancing, cueing people to temporally distance and think

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about their future selves should also lead them to adopt a self-distanced visual perspective. This is indeed what we found. Two studies manipulated temporal distancing by asking people to think about how they might feel about a current stressor either 1 week from now (near-future perspective) or 10 years from now (far-future perspective). Findings showed that people in the far-future (vs near-future) condition rated themselves higher on items assessing spontaneous visual distancing (described in previous sections) (Bruehlman-Senecal & Ayduk, 2015). Next, we conducted a series of experimental studies to test the utility of temporal distancing for emotion regulation (Bruehlman-Senecal & Ayduk, 2015). Specifically, across several studies, we found that cueing people to think about how they might feel about a current stressor in the distant future (e.g., 10 years from now) vs near-future (e.g., 1 week from now) led them to experience less distress. These results held across a range of stressors, both minor (e.g., work deadlines) and major (e.g., loss of a spouse), and regardless of whether participants reflected on negative events that had already happened or were still ongoing. But what underlying mechanisms explain these effects? We considered several alternatives. First, focusing on how our future selves might feel about our current troubles might hasten emotional recovery by increasing participants’ awareness that their thoughts and feelings about the stressor might fade with the passage of time—a process we refer to as impermanence focus. Second, people tend to see their future through rose-colored glasses (e.g., Heller, Stephan, Kifer, & Sedikides, 2011) and expect it to be characterized less by ups and downs (Liberman, Sagristano, & Trope, 2002). Thus, another possibility is that adopting a temporally distanced perspective would downregulate distress by focusing people’s attention on an overly optimistic idealized future. Finally, when reflecting on how their lives may be in the near-future, people are also more likely to consider concrete situational forces that may shape their day-to-day experiences. Because concrete mental simulations evoke stronger emotional reactions than more abstract ones (Taylor & Schneider, 1989), temporal distancing may regulate distress by drawing people’s attention away from the potential concrete impact of the event. Across multiple studies, impermanence focus (e.g., I told myself that my feelings about the problem are temporary), but not idealized future (e.g., “I imagined the life I ideally want to lead in the future”) or concrete impact (e.g., “I thought about how this will affect my day-to-day life”)

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considerations, mediated the emotion-regulatory benefits of adopting a temporally far distanced perspective using the standard (Baron and Kenny, 1986) mediational framework. Moreover, when impermanence focus was experimentally manipulated using a causal-chain framework (Spencer, Zanna, & Fong, 2005), participants led to consider the ways in which their reactions might be transitory reported lower levels of distress than people led to consider the ways in which their reactions might be enduring and long lasting. Thus, converging evidence indicates that temporal distancing downregulates distress by making salient the transitory nature of the reactions one’s present self feels and thinks, shrinking the emotional significance of the experience in the here and now.

5.2 Individual Differences We have also examined whether individuals differ in their chronic tendencies to use temporal distancing in everyday life and, if so, whether the habitual use of temporal distancing has similarly beneficial effects on emotion regulation as experimentally induced temporal distancing. To address these issues, we developed a trait temporal distancing questionnaire (e.g., “I focus on how my feelings about the event may change with time,” “I generally do not consider that the consequences of the event may be temporary”) and examined how scores on this measure predicted a variety of theoretically relevant constructs (Bruehlman-Senecal, Ayduk, & John, 2016). Our results indicated that there were stable and reliable individual differences in people’s chronic tendencies to use temporal distancing to regulate their emotions. Furthermore, people high in temporal distancing scored higher on measures of decentering (i.e., taking a step back from one’s thoughts and feelings and observing them as passing events in the mind), the nonreactivity facet of mindfulness (i.e., noticing one’s thoughts and feelings without reacting to them), and emotion regulation efficacy (i.e., one’s perceived ability to successfully regulate their own emotions) and lower on neuroticism and impulsivity. Thus, the nomological network of temporal distancing was consistent with what one would expect theoretically. In terms of outcomes, trait temporal distancing was positively associated with indices of well-being (e.g., subjective well-being, positive affect) and negatively associated with indices of ill-being (e.g., depression, negative affect). Importantly, temporal distancing was a unique predictor of many of these outcomes even when controlling for the general tendency to use

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reappraisal as measured by the Emotion Regulation Questionnaire (Gross & John, 2003). In addition, a daily dairy study allowed us also to examine the more granular affective processes associated with temporal distancing at the daily level. As one would expect, people higher in temporal distancing displayed lower levels of negative affect and higher levels of positive affect across a 10-day daily diary period. More importantly, these differences in daily affect explained their well-being 3 and 6 months later. Interestingly, we also found that people high in temporal distancing were buffered against high negative affect particularly on days when they experienced high levels of negative experiences. On the flip side, they were protected against low positive affect particularly on days when positive, stimulating experiences were lacking in their lives. Again, these findings are part of a broader pattern that consistently emerges in our program of research where self-distancing facilitates effective emotion regulation during times of vulnerability, whether vulnerability is assessed as a stable trait (e.g., high anxiety or neuroticism) or as a situational risk factor (e.g., days with multiple stressful events or lack of positive events). Finally, drawing from our previous findings on the buffering effect of visual self-distancing against hostility (Ayduk & Kross, 2010b; Mischowski et al., 2012), we also explored how trait temporal distancing related to anger in response to a lab-based interpersonal provocation paradigm (Bushman et al., 2005). Specifically, participants were asked complete an anagram task and communicate their answers to the experimenter through an intercom. Three times during this interaction, the experimenter criticized participants for not speaking loudly enough and used a progressively rude manner in delivering the criticism. Participants rated their emotional reactions following the interaction. As expected, those higher in trait temporal distancing reported lower levels of anger (Bruehlman-Senecal et al., 2016, Study 5a).

5.3 Converging Evidence Consistent with this work, Huynh, Yang, and Grossman (2016) recently demonstrated that the benefits of temporal distancing extend to behavior in close relationships as well. For example, partners who were instructed to reflect on a relationship conflict from a temporally distanced, far-future perspective (e.g., “one year from now, when you think of this event, what thoughts would come to your mind?”) displayed more adaptive conflict

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reasoning (i.e., lower partner blame, greater forgiveness, and insight) following the reflection task than partners who were led to focus on the conflict from the perspective of their present selves (e.g., “right now, when you think of this event, what thoughts come to your mind?”). Furthermore, such adaptive reasoning predicted more positive relationship attitudes, such as greater positive affect toward the partner and higher expectations of relationship growth (vs decline).

5.4 Summary These finding suggest that temporal distancing is a form of self-distancing that involves shifting one’s perspective from the present self to a distant future self. As the above-mentioned study illustrates, this shift enhances people’s ability to control their feelings surrounding negative experiences. As research on this topic continues, a key challenge will be to examine the clinical implications of these findings, as well as the neural mechanisms that underlie the benefits of mental time travel for facilitating self-regulation.

6. SELF-DISTANCING TRAINING 6.1 Laboratory Training Intervention Given the benefits laboratory studies have revealed about self-distancing for self-regulation, we recently began to examine whether teaching people how to self-distance when they experience powerful emotions in their daily lives can enhance their coping ability. In one study, we randomly assigned participants to one of the three conditions at the start of the study: a selfdistancing training group, a relaxation-training active control group, and a no-instruction control group (Orvell, Bruehlman-Senecal, Kross, & Ayduk, in preparation). During the training session, participants in the selfdistancing group were taught how to self-distance when they experienced daily stress using both the visual and linguistic techniques described earlier. Participants in the relaxation group were simply told that they should try to relax when they experienced daily stress. Subsequently, participants in both groups formed implementation intentions to help ensure that they would use the strategies they were taught when they encountered stressors in daily life after the training period was over (Gollwitzer, 1999). Participants in the no-instruction control group were not given any instructions on how to cope with their daily stress and did not form any implementation intentions.

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Conceptually replicating prior research indicating that vulnerable individuals benefit the most from self-distancing (Kross & Ayduk, 2009; Kross et al., 2012; Park et al., 2014; Penner et al., 2016), participants in the self-distancing group who scored high on a baseline measure of vulnerability (e.g., trait anxiety and rumination) reported lower levels of daily negative affect and rumination during a 10-day daily diary assessment that followed the initial training session, compared to participants in the two control conditions who scored high on the same baseline measure of vulnerability. At low levels of vulnerability, we did not observe any differences between the groups. Encouragingly, we also observed long-term effects of selfdistancing training. Specifically, whereas vulnerable participants in the no-instruction control group displayed a significant increase in depressive symptoms assessed 3 and 6 months following strategy training, vulnerable participants in the self-distancing group were buffered against these increases. In fact, vulnerable participants in the self-distancing group were indistinguishable from their low vulnerability counterparts in terms of their depressive symptoms during the 3 and 6 month follow-ups. We did, however, observe one nonpredicted result in this study— vulnerable participants in the relaxation control group were also buffered against increases in depression over time. In this vein, it is important to recognize that relaxation training has been shown to lead to improvements in well-being (see Carlson & Hoyle, 1993, for review). Thus, our data suggest that whereas self-distancing training is particularly useful in buffering people against daily negative affect and rumination, both self-distancing and relaxation may provide people with useful tools that help buffer them against increases in depression over time.

6.2 Online Training Intervention In the previous study, participants were trained to self-distance in the laboratory one at a time. However, the recent advent and proliferation of online tools for performing studies provided us with an opportunity to examine the scalability of these initial results by investigating whether people can be taught how to self-distance online. We recently explored this possibility using Amazon’s Mechanical Turk (MTurk) online software (Ranney, Bruehlman-Senecal, & Ayduk, 2016). Specifically, we first had participants complete a baseline assessment of wellbeing (e.g., life satisfaction, positive affect) and ill-being (e.g., worry,

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negative affect). Next, we randomly assigned participants to one of the four strategy conditions: self-distancing training, temporal distancing training, positive reappraisal training, or no training control. In the self-distancing group, participants were taught the visual and linguistic self-distancing strategies. Participants assigned to the temporal distancing condition were trained to adopt the perspective of their future selves and reflect on how they might feel about a stressor in the future when they experienced distress (Bruehlman-Senecal & Ayduk, 2015; BruehlmanSenecal et al., 2016). Finally, participants in the positive reappraisal group were taught how to positively reinterpret their experience (i.e., focus on the bright side; e.g., Moser, Hartwig, Moran, Jendrusina, & Kross, 2014; Ochsner & Gross, 2008). All three groups then formed implementation intentions to use the strategy they were just trained on during the next 2 weeks (Gollwitzer, 1999). In the fourth, no training control condition, participants did not receive any strategy training, nor did they form any implementation intentions. Two weeks following the training, participants in the self-distancing and temporal distancing conditions displayed significantly higher levels of wellbeing and significantly lower levels of various markers of distress compared to participants in the no training control group. And although the positive reappraisal training group also outperformed the control group on these measures (as we predicted), we found no differences between either of the two distancing groups and the reappraisal condition. The latter finding was particularly noteworthy because the mental health benefits of positive reappraisal are well established (Folkman & Moskowitz, 2000; Shiota & Levenson, 2012; Tugade & Fredrickson, 2004). Thus, the present findings suggest that distancing strategies may be equally effective in their usefulness.

6.3 Converging Evidence Additional evidence supporting the benefits of self-distancing interventions comes from two additional studies that involved intervention conditions that were similar, but not identical to those described above. In one study, Finkel, Slotter, Luchies, Walton, and Gross (2013) tracked the marital satisfaction of 120 couples over a 2-year period. Half-way through the study, half of the couples were randomly assigned to an intervention group in which they were asked to write about the most significant conflict they experienced with their partner over the course of the past 4 months from the perspective of a well-intentioned neutral observer. The

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other half of the participants did not engage in any writing task (i.e., a no treatment control). This manipulation was administered three times during the second year of the study (during the 14-, 18-, and 22-month followups). Participants in the control group did not perform any writing. The results of the study were striking. Before the intervention, both groups displayed a decline in marital quality over time. After the intervention, however, participants in the control group continued to display this decline, whereas participants in the intervention group were buffered against any further decline in their marital quality levels. More recently, Denny and Ochsner (2014) performed a short-term training study in which they taught people how to adopt a distanced perspective (e.g., adopt the perspective of a neutral observer) when viewing negative emotional images taken from the International Affective Picture System (Lang et al., 2008) and then examined the effects of this training procedure on subsequent perceived stress (i.e., participants self-report ratings of how stressed and nervous they felt over the past few days using the Perceived Stress Scale; Cohen, Kamarck, & Mermelstein, 1983) in comparison to a nondistancing reappraisal training group and a no training control group. Their results indicated that the distancing group displayed a significant decline in perceived stress over the course of the study, whereas participants in the other conditions did not.

6.4 Summary Together, these studies provide converging evidence highlighting the value of teaching people how to self-distance to improve the way they cope with negative experiences and emotions in their daily lives. And although the way distancing was operationalized across many of these studies differed slightly, the fact that their results converge on a common set of findings speaks to the potential power of distancing as a scalable self-regulation strategy.

7. NEW EXTENSIONS One of the most exciting discoveries we have made in pursuing the aforementioned studies is that self-distancing has implications for a range of phenomena beyond meaning making and coping. In the following sections, we describe some of these phenomena to provide a glimpse into how current research is attempting to broaden and deepen our understanding of self-distancing.

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7.1 Wise Reasoning Although many people are eminently capable of offering wise counsel to others (Grossmann & Kross, 2014, Study 1), they often fail to do so effectively for themselves when they face their own personal dilemmas (Ybarra, Rees, Kross, & Sanchez-Burks, 2011). Might self-distancing allow people to reason more wisely under such circumstances? Several recent studies suggest that it does. For example, in one study, we randomly assigned college seniors and recent college graduates who were unsuccessful at securing a job after graduation to reason about how the economic recession characterizing the United States economy at the time would influence their career prospects from either a self-distanced or a self-immersed visual perspective (Kross & Grossmann, 2011, Study 1). Participants in the self-distanced group displayed higher levels of two common forms of wise reasoning—dialecticism (i.e., they were more likely to recognize that the world is in flux and the future is likely to change; Basseches, 1984; Kramer & Woodruff, 1986) and intellectual humility (i.e., they were more likely to recognize the limits of their own knowledge; Baltes & Smith, 2008; Ryan, 2012). A follow-up study (Kross & Grossmann, 2011, Study 2), conceptually replicated these findings in a different context. Specifically, we randomly assigned participants to think about how various foreign and domestic issues would play out if their chosen candidate lost the 2008 United States Presidential election from either a self-distanced or self-immersed perspective 3 weeks before the election. Consistent with the above findings, participants in the self-distanced group again displayed higher levels of dialecticism and intellectual humility following the experimental manipulation. They were also significantly more prosocial—they endorsed their own political views less strongly after the manipulation and signed up to join a bipartisan group at a marginally higher rate than participants in the immersed group. The latter findings were particularly noteworthy because prosocial tendencies are often conceptualized as an important consequence of wise reasoning (Sternberg, 1998). These studies provided preliminary evidence suggesting that selfdistancing facilitates wise reasoning. But just how effective is self-distancing for boosting wisdom? Does asking a person to reason about their own problems from a distance lead them to reason as wisely as they do when they offer other people counsel? Or does self-distancing provide people with a smaller nudge, leading them to reason about their problems more wisely than if they

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were immersed, but not quite as wisely as if they were thinking about someone else’s dilemma? To address these questions, we randomly assigned participants to one of the four conditions: (a) reason about one’s own problem from an immersed perspective, (b) reason about one’s friend’s problem from an immersed perspective, (c) reason about one’s own problem from a distanced perspective, or (d) reason about one’s friend’s problem from a distanced perspective) (Grossmann & Kross, 2014, Study 2). We found that participants who reasoned about their own problems from a distance reasoned as wisely as participants who reasoned about another person’s problem from either a distanced or immersed perspective. Thus, the findings from this study suggested that self-distancing completely eliminated the self-other asymmetry that normally characterizes wise reasoning (Grossmann & Kross, 2014, Study 1). These and several other recent papers have linked self-distancing and the ability to reason wisely (e.g., Grossmann, Gerlach, & Denissen, 2016; Staudinger & Gluck, 2010). Together, they suggest that researchers should consider investigating whether teaching people how to self-distance could increase their level of wise reasoning in daily life.

7.2 A Common Ingredient Underlying Successful Cognitive Interventions? Over the years, several researchers have suggested that psychological distancing is an essential ingredient that underlies self-control (Mischel & Rodriguez, 1993) and a key mechanism that allows people to benefit from cognitive interventions designed to improve the way people feel (Beck, 1970; Ingram & Hollon, 1986). But little research has directly examined whether self-distancing plays a role in mediating the outcomes of different cognitive interventions. As a first step toward addressing this question, we examined the role that self-distancing plays in mediating the emotional benefits associated with expressive writing (Park et al., 2016), a well-studied intervention that involves asking people to write expressively about their deepest thoughts and feelings surrounding a negative past experience over several consecutive days, which has been found to lead to a number of dramatic emotional and physical health improvements (Pennebaker & Chung, 2007; Pennebaker & Graybeal, 2001; Pennebaker et al., 1997; Smyth, 1998). We focused on expressive writing because several features of this paradigm suggest that it should enhance self-distancing. Specifically constructing narratives about

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one’s experience involves separating the self as author from the self as the target of writing (Apgar, 1997); writing also focuses one’s attention on the broader context in which one’s experience occurred (Meier, 2002), leads a person to adopt the perspective of multiple people (Labov & Fanshel, 1977), and often leads to writing in the past tense (Polanyi, 1982)—each of which represents a psychological process that involves transcending one’s egocentric view of the world. Thus, we reasoned that self-distancing might mediate the benefits of expressive writing. To test this idea, we first had all participants recall and reflect on their most distressing life experience (examples of such experiences included losing a loved one, experiencing painful romantic rejection, and failing to live up to one’s ideals). Then we randomly assigned participants to either an expressive writing group or a neutral writing group. Participants in the expressive writing group were asked to write about their deepest thoughts and feelings surrounding their most distressing life experience for 15 min over the course of three consecutive days; participants in the control group were asked to write about a neutral topic for the same amount of time. Both 1 day and 1 month following the writing intervention, we asked participants to recall and reflect on the same experience they thought about during the first day of the study, and then rate their tendency to adopt a self-distanced visual perspective as they reflected on their feelings surrounding the event. Both 1 day and 1 month following the intervention, participants in the expressive writing group self-distanced more than participants in the control group when they reflected on their negative experience. Moreover, participants’ tendency to self-distance when they reflected on their negative experience after the intervention predicted the emotional benefits of expressive writing over time. Specifically, participants in the expressive writing group displayed less negative emotion when thinking about their experience 1 day and 1 month following the intervention, and each of these effects was mediated by their tendency to adopt a self-distanced perspective. A follow-up study replicated and extended these findings by demonstrating that expressive writing promotes self-distancing not only compared to a neutral control condition, but also compared to a condition in which participants are asked to simply “think” about a negative experience. Including an additional “think” condition provided a relatively conservative control group because participants in this group were likewise asked to focus on the emotional content of their negative experience for the same amount of time as participants in the expressive writing group. However, unlike

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expressive writing, simply thinking about a negative emotional event was not expected to lead people to feel better (Lyubomirsky, Sousa, & Dickerhoof, 2006). The results of this study were consistent with this prediction. Expressive writing led participants to self-distance more following the intervention relative to both the thinking and neutral writing control groups. Moreover, as in the first study, self-distancing mediated the emotional benefits of expressive writing over time. These findings shed light on one factor that helps explain how expressive writing leads to some of its emotional benefits. But at a broader level, they raise an interesting possibility—that self-distancing may constitute a core process that explains how different cognitive interventions might achieve their benefits. Investigating this question in the future is important not only for advancing research on self-distancing, but also improving our understanding of the mechanisms that promote effective cognitive change more broadly.

7.3 Intergroup Relationships Recent research has also examined the role of self-distancing in a very different context: facilitating intergroup relations. Despite the recent increase in racial diversity in the United States (Colby & Ortman, 2014), Whites continue to hold most leadership positions in many academic and professional domains (Landivar, 2013), making it likely that racial minorities are often mentored or supervised by a White mentor. Why does this racially discordant mentorship structure matter? Interracial interactions tend to be anxiety provoking for both parties (Page-Gould, Mendoza-Denton, & Tropp, 2008). Racial minorities become anxious about confirming negative stereotypes about their group; Whites become anxious about coming across as racist (Butz & Plant, 2006; MendozaDenton, Goldman-Flythe, Pietzrak, Downey, & Aceves, 2010; Plant & Devine, 2003). Concerns about appearing prejudiced are in turn associated with negative attitudes toward minorities (Plant & Devine, 1998), lower quality intergroup interactions (Vorauer, 2006; Vorauer & Turpie, 2004), and the provision of less useful feedback by Whites in mentoring contexts (Crosby & Monin, 2007). Given the downstream negative consequences associated with becoming immersed in one’s concerns about not being perceived as prejudiced, we reasoned that cueing White mentors to self-distance prior to interacting with their minority mentee might improve the quality of the mentorship they provide.

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We tested this idea by cueing White mentors to prepare for an interaction with a Black mentee by thinking about how the interaction would proceed using either first-person pronouns (self-immersed condition) or non-first-person pronouns (self-distanced condition, Leitner et al., in press). Subsequently, we asked mentors to view a video of their mentee delivering a public speech and then provide feedback about the mentee’s performance. The Black mentee was, in fact, a confederate who was videotaped as he delivered a scripted speech about his qualifications for his dream job. We recorded mentor participants’ brain activity using EEG throughout this study so we could unobtrusively monitor their cognitive and emotional reactions to the task and manipulation. Current source density analysis, a technique that allows researchers to estimate the neural generators of scalp EEG activity (Grech et al., 2008; Pascual-Marqui, Michel, & Lehmann, 1994; Tenke & Kayser, 2012), revealed that the self-distancing manipulation led to reduced activity in brain regions linked to self-referential processing (MPFC) among mentors when they critiqued their mentees. This decreased MPFC activity, in turn, predicted more positive evaluations of the mentee, and the provision of more warm and helpful feedback as rated by judges who were blind to the study’s hypotheses. These results provide promising preliminary evidence suggesting that self-distancing may be useful for improving the quality of interracial mentorship by decreasing self-referential processing during the provision of criticism. They also provide a conceptual replication of our recent neuroimaging research on linguistic self-distancing, in that they link the activation of this process with MPFC modulation.

7.4 Social Support The bulk of self-distancing research to date has focused on how this process facilitates adaptive self-reflection. But in daily life, people do not simply try to work-through their experiences on their own. They also frequently rely on other people to help them deal with their problems. Might a common set of mechanisms explain how both of these routes to adaptive self-reflection— i.e., self-distancing and social support from other people—work? We addressed this question by randomly assigning participants to talk about an unresolved, painful negative interpersonal event with a confederate who was trained to either prompt the participant to recount (e.g., “Can you tell me what happened from start to finish?”) or reconstrue (e.g., “If you look at the big picture, does that help you make sense of this experience?”) their

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experience (Lee, Kross, Briskin, Shrapnell, & Ybarra, in preparation). We manipulated this construct because prior research has found that selfdistancing promotes adaptive self-reflection by leading people to focus less on recounting what happened to them and to focus more on reconstruing their experience. Thus, to the extent that these processes represent key levers that determine the whether the outcomes of self-reflection are helpful or harmful, we hypothesized that participants who were prompted to reconstrue their experience would feel better than participants who were prompted to recount it. Our results supported this prediction. Specifically, participants who were prompted to reconstrue their experience displayed significantly less negative affect at the end of the study. They also reported feeling a greater sense of closure surrounding their experience. Moreover, a follow-up study that directly replicated these results also demonstrated that the main effect of recounting vs reconstruing was evident regardless of participants’ preferred style of coping (Lee et al., in preparation). That is, even participants who indicated during a pretesting session that they preferred to cope with negative experiences by engaging in recounting benefited from the reconstrual manipulation. These findings begin to shed light on the mechanisms that underlie adaptive vs maladaptive forms of social support. They also highlight the need for future research to examine how these processes play out spontaneously among couples and friends as they live their lives.

7.5 Summary Although the foci of the different lines of research described in this section are distinct, they share a common thread—they demonstrate how the process of taking a psychological “step back” to reflect on one’s experiences and emotions can at times have far-reaching implications for influencing the way people think, feel, and behave.

8. CONCLUDING THOUGHTS Over the past 10 years, a substantial amount of evidence has accumulated demonstrating the benefits of distancing as a self-regulatory tool. Indeed, a recent metaanalysis (Webb, Miles, & Sheeran, 2012) conceptualized self-distancing as a member of a class of perspective-taking strategies that involve adopting a detached/observer perspective, which was one of the most effective for facilitating emotion regulation, speaking to the power

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of self-distancing for helping people control the way they think, feel, and behave. The results of this metaanalysis also provide important data regarding robustness, insofar as these studies collectively demonstrated that distancing strategies, despite being operationalized somewhat differently across studies, generally converge on favorable emotion regulation outcomes. Given these findings, it should come as no surprise that we are often asked, “Is self-distancing a magic pill?” Our answer to this question is an emphatic, “no.” We conceive of self-distancing as a psychological process. And psychological processes, in our view, are not singularly good or bad. Instead, whether they are helpful or harmful depends critically on the context in which they are engaged. In this vein, it is important to emphasize that we and others have studied self-distancing in contexts in which we hypothesized it would be helpful. Of course, it is possible that there are other contexts in which engaging in certain types of self-distancing strategies may be harmful or ineffective. For example, some research indicates that adopting a self-distanced visual perspective is harmful when people with social phobia imagine the stressful circumstances that drive their fear (e.g., Coles, Turk, & Heimberg, 2002; Coles, Turk, Heimberg, & Fresco, 2001; Schultz & Heimberg, 2008; Wells, Clark, & Ahmad, 1998). Other studies have linked visual self-distancing with no emotion regulation effects for people reflecting on past experiences that elicit negative self-conscious emotions such as guilt or embarrassment (Katzir & Eyal, 2013). Although it is tempting to conclude that self-distancing is ineffective in these kinds of situations, we suggest that instances such as these are precisely when a contextual analysis is most needed (Aldao, 2013; e.g., Aldao & Nolen-Hoeksema, 2012; Bonanno & Burton, 2013; Mendoza-Denton, Ayduk, Mischel, Shoda, & Testa, 2001; Mischel & Shoda, 1995, 1998). In this vein, consider the fact that we have found another type of self-distancing technique, linguistic self-distancing, to be helpful in the same contexts that the aforementioned work has found visual self-distancing techniques to be harmful or benign—when people who are intensely fearful of social anxiety reflect on situations that elicit negative self-conscious emotions (Kross et al., 2014, Study 6). Why might one self-distancing tactic be helpful in these situations and another harmful? Although we can only speculate at this point, a contextual analysis that seeks to identify the reasons why one strategy may be harmful or ineffective for certain people in certain types of situations, whereas another might be useful, offers many hypotheses that can be explored. Moving

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forward, a key challenge for future research is to address these types of questions not only in the context of self-distancing strategies, but also other psychological distancing and emotion-regulatory strategies more generally. Doing so has the potential to enrich our understanding of how selfregulation operates in ways that promise to both advance basic theory and also provide people with information they can use to improve their ability to cope with negative experiences in their lives.

ACKNOWLEDGMENTS We would like to thank Jordan Leitner, Rodolfo Mendoza-Denton, and Ariana Orvell for their helpful feedback on previous versions of this manuscript. Portions of the work described herein were supported by grants from the National Science Foundation (BCS-1509457, 1306709, 1514510), National Institutes of Mental Health (MH39349), John Templeton Foundation (59436, 21564, 24226), and the Character Lab (03072, 018011).

REFERENCES Abela, J. R. Z., Brozina, K., & Haigh, E. P. (2002). An examination of the response styles theory of depression in third- and seventh-grade children: A short-term longitudinal study. Journal of Abnormal Child Psychology, 30(5), 515–527. Aldao, A. (2013). The future of emotion regulation research: Capturing context. Perspectives on Psychological Science, 8(2), 155–172. http://dx.doi.org/10.1177/1745691612459518. Aldao, A., & Nolen-Hoeksema, S. (2012). The influence of context on the implementation of adaptive emotion regulation strategies. Behaviour Research and Therapy, 50(7), 493–501. http://dx.doi.org/10.1016/j.brat.2012.04.004. Angst, F., Stassen, H. H., Clayton, P. J., & Angst, J. (2002). Mortality of patients with mood disorders: Follow-up over 34–38 years. Journal of Affective Disorders, 68(2–3), 167–181. Apgar, S. C. (1997). Fighting back on paper and in real life: Sexual abuse narratives and the creation of safe space. In K. Tomoko (Ed.), Creating safe space: Violence and women’s writing. New York, NY: State University of New York. Araujo, H. F., Kaplan, J., & Damasio, A. (2013). Cortical midline structures and autobiographical-self processes: An activation-likelihood estimation meta-analysis. Frontiers in Human Neuroscience, 7, 548. http://dx.doi.org/10.3389/fnhum.2013.00548. Ariaga, C. E., & Rusbult, X. B. (1998). Standing in my partner’s shoes: Partner perspective taking and reactions to accomodative dilemmas. Personality and Social Psychology Bulletin, 24(9), 927–948. Ayduk, O., & Kross, E. (2008). Enhancing the pace of recovery: Self-distanced analysis of negative experiences reduces blood pressure reactivity. Psychological Science, 19(3), 229–231. Ayduk, O., & Kross, E. (2009). Asking ‘why’ from a distance facilitates emotional processing: A reanalysis of Wimalaweera and Moulds (2008). Behaviour Research and Therapy, 47(1), 88–92. Ayduk, O., & Kross, E. (2010a). Analyzing negative experiences without ruminating: The role of self-distancing in enabling adaptive self-reflection. Social and Personality Psychology Compass, 4(10), 841–854. Ayduk, O., & Kross, E. (2010b). From a distance: Implications of spontaneous self-distancing for adaptive self-reflection. Journal of Personality and Social Psychology, 98(5), 809–829.

Self-Distancing: Theory, Research, and Current Directions

127

Ayduk, O., & Kross, E. (in press). Self-distancing: Basic mechanisms and clinical implications. In D. de Ridder, M. Adriaanse, & K. Fujita (Eds.), Handbook of self-control in health and wellbeing. Vol. 1. Routledge, Taylor & Francis. Ayduk, O., & Mischel, W. (2011). Willpower in a cognitive affective processing system. In K. Vohs & R. Baumeister (Eds.), Handbook of self-regulation: Research, theory and applications, Vol 2. New York: Guilford. Baltes, P. B., & Smith, J. (2008). The fascination of wisdom: Its nature, ontogeny, and function. Perspectives on Psychological Science, 3(1), 56–64. Barnhofer, T., Crane, C., Brennan, K., Duggan, D. S., Crane, R. S., Eames, C., … Williams, J. M. (2015). Mindfulness-based cognitive therapy (MBCT) reduces the association between depressive symptoms and suicidal cognitions in patients with a history of suicidal depression. Journal of Consulting and Clinical Psychology, 83(6), 1013–1020. http:// dx.doi.org/10.1037/ccp0000027. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. Basseches, M. (1984). Dialectical thinking and adult development. New Jersey: Ablex. Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The strength model of self-control. Current Directions in Psychological Science, 16(6), 351–355. Beck, A. T. (1970). Cognitive therapy: Nature and relation to behavior therapy. Behavior Therapy, 1(2), 184–200. Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. (1996). Comparison of beck depression inventories-IA and -II in psychiatric outpatients. Journal of Personality Assessment, 67(3), 588–597. http://dx.doi.org/10.1207/s15327752jpa6703_13. Berman, M. G., Peltier, S., Nee, D. E., Kross, E., Deldin, P. J., & Jonides, J. (2010). Depression, rumination and the default network. Social Cognitive and Affective Neuroscience, 6(5), 548–555. http://dx.doi.org/10.1093/scan/nsq080. Bernstein, A., Hadash, Y., Lichtash, Y., Tanay, G., Shepherd, K., & Fresco, D. M. (2015). Decentering and related constructs: A critical review and metacognitive processes model. Perspectives on Psychological Science, 10(5), 599–617. http://dx.doi.org/10.1177/ 1745691615594577. Berntsen, D., Willert, M., & Rubin, D. C. (2003). Splintered memories or vivid landmarks? Qualities and organization of traumatic memories with and without PTSD. Applied Cognitive Psychology, 17(6), 675–693. Blascovich, J., & Tomaka, J. (1996). The biopsychosocial model of arousal regulation. Advances in Experimental Social Psychology, 28, 1–51. Bonanno, G. A., & Burton, C. L. (2013). Regulatory flexibility: An individual differences perspective on coping and emotion regulation. Perspectives on Psychological Science, 8(6), 591–612. http://dx.doi.org/10.1177/1745691613504116. Broderick, P. C., & Korteland, C. (2004). A prospective study of rumination and depression in early adolescence. Clinical Child Psychology and Psychiatry, 9(3), 383. Brosschot, J. F., Gerin, W., & Thayer, J. F. (2006). The perseverative cognition hypothesis: A review of worry, prolonged stress-related physiological activation, and health. Journal of Psychosomatic Research, 60(2), 113–124. Bruehlman-Senecal, E., & Ayduk, O. (2015). This too shall pass: Temporal distance and the regulation of emotional distress. Journal of Personality and Social Psychology, 108(2), 356–376. http://dx.doi.org/10.1037/a0038324. Bruehlman-Senecal, E., Ayduk, O., & John, O. P. (2016). Taking the long view: Implications of individual differences in temporal distancing for affect, stress reactivity, and wellbeing. Journal of Personality and Social Psychology, 111(4), 610–635. Brunia, C. H. M., Boxtel, G. J. M., & Bocker, K. B. E. (2012). Negative slow waves as indices of anticipation: The bereitschafts potential, the contingent negative variation, and the

128

E. Kross and O. Ayduk

stimulus-preceding negativity. In S. J. Luck & E. S. Kappenman (Eds.), The Oxford handbook of event-related potential components (pp. 189–207). New York: Oxford University Press. Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., … Ochsner, K. N. (2014). Cognitive reappraisal of emotion: A meta-analysis of human neuroimaging studies. Cerebral Cortex, 24(11), 2981–2990. http://dx.doi.org/10.1093/ cercor/bht154. Burwell, R. A., & Shirk, S. R. (2007). Subtypes of rumination in adolescence: Associations between brooding, reflection, depressive symptoms, and coping. Journal of Clinical Child & Adolescent Psychology, 36(1), 56–65. Bushman, B. J. (2002). Does venting anger feed or extinguish the flame? Catharsis, rumination, distraction, anger and aggressive responding. Personality and Social Psychology Bulletin, 28(6), 724–731. Bushman, B. J., Bonacci, A. M., Pedersen, W. C., Vasquez, E. A., & Miller, N. (2005). Chewing on it can chew you up: Effects of rumination on triggered displaced aggression. Journal of Personality and Social Psychology, 88(6), 969–983. Butz, D. A., & Plant, E. A. (2006). Perceiving outgroup members as unresponsive: Implications for approach-related emotions, intentions, and behavior. Journal of Personality and Social Psychology, 91, 1066–1079. Carleton, R. N., Collimore, K. C., McCabe, R. E., & Antony, M. M. (2011). Addressing revisions to the brief fear of negative evaluation scale: Measuring fear of negative evaluation across anxiety and mood disorders. Journal of Anxiety Disorders, 25, 822–828. Carlson, C. R., & Hoyle, R. H. (1993). Efficacy of abbreviated progressive muscle relaxation training: A quantitative review of behavioral medicine research. Journal of Consulting and Clinical Psychology, 61(6), 1059–1067. Christian, B. M., Parkinson, C., Macrae, C. N., Miles, L. K., & Wheatley, T. (2015). When imagining yourself in pain, visual perspective matters: The neural and behavioral correlates of simulated sensory experiences. Journal of Cognitive Neuroscience, 27(5), 866–875. http://dx.doi.org/10.1162/jocn_a_00754. Clark, J. V., & Arkowitz, H. (1975). Social anxiety and the self-evaluation of interpersonal performance. Psychological Reports, 36, 211–221. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396. Colby, S. L., & Ortman, J. M. (2014). Projects of the size and composition of the U.S. population: 2014 to 2060. Current Populations Reports, 1124–1143. Coles, M. E., Turk, C. L., & Heimberg, R. G. (2002). The role of memory perspective in social phobia: Immediate and delayed memories for role-played situations. Behavioural and Cognitive Psychotherapy, 30(4), 415–425. http://dx.doi.org/10.1017/ s1352465802004034. Coles, M. E., Turk, C. L., Heimberg, R. G., & Fresco, D. M. (2001). Effects of varying levels of anxiety within social situations: Relationship to memory perspective and attributions in social phobia. Behavior Research and Theory, 39(6), 651–665. Connor, K. M., Davidson, J. R. T., Churchill, L. E., Sherwood, A., Foa, E., & Weisler, R. H. (2000). Psychometric properties of the Social Phobia Inventory (SPIN): New self-rating scale. British Journal of Psychiatry, 176, 379–386. Coyne, J. C. (1994). Self-reported distress: Analog or ersatz depression? Psychological Bulletin, 116(1), 29–45. Crosby, J., & Monin, B. (2007). Failure to warn: How student race affects warnings of potential academic difficulty. Journal of Experimental Social Psychology, 43, 663–670. Dalgleish, T., & Werner-Seidler, A. (2014). Disruptions in autobiographical memory processing in depression and the emergence of memory therapeutics. Trends in Cognitive Sciences, 18(11), 596–604. http://dx.doi.org/10.1016/j.tics.2014.06.010.

Self-Distancing: Theory, Research, and Current Directions

129

Denny, B. T., & Ochsner, K. N. (2014). Behavioral effects of longitudinal training in cognitive reappraisal. Emotion, 14(2), 425–433. http://dx.doi.org/10.1037/a0035276. Denson, T. F., Spanovic, M., & Miller, N. (2009). Cognitive appraisals and emotions predict cortisol and immune responses: A meta-analysis of acute laboratory social stressors and emotion inductions. Psychological Bulletin, 135(6), 823–853. http://dx.doi.org/10.1037/ a0016909. Dolcos, S., & Albarracin, D. (2014). The inner speech of behavioral regulation: Intentions and task performance strengthen when you talk to yourself as a you. European Journal of Social Psychology, 44, 636–642. Dorfel, D., Lamke, J. P., Hummel, F., Wagner, U., Erk, S., & Walter, H. (2014). Common and differential neural networks of emotion regulation by detachment, reinterpretation, distraction, and expressive suppression: A comparative fMRI investigation. NeuroImage, 101, 298–309. http://dx.doi.org/10.1016/j.neuroimage.2014.06.051. Eich, E., Nelson, A. L., Leghari, M. A., & Handy, T. C. (2009). Neural systems mediating field and observer memories. Neuropsychologia, 47(11), 2239–2251. http://dx.doi.org/ 10.1016/j.neuropsychologia.2009.02.019. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An FMRI study of social exclusion. Science, 302(5643), 290–292. Finkel, E. J., Slotter, E. B., Luchies, L. B., Walton, G. M., & Gross, J. J. (2013). A brief intervention to promote conflict reappraisal preserves marital quality over time. Psychological Science, 24(8), 1595–1601. http://dx.doi.org/10.1177/0956797612474938. Foa, E. B., & Kozak, M. J. (1986). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99(1), 20–35. Folkman, S., & Moskowitz, J. T. (2000). Positive affect and the other side of coping. American Psychologist, 55(6), 647–654. Fujita, K., Trope, Y., Liberman, N., & Levin-Sagi, M. (2006). Construal levels and selfcontrol. Journal of Personality and Social Psychology, 90(3), 351–367. Gerin, W., Davidson, K. W., Christenfeld, N. J. S., Goyal, T., & Schwartz, J. E. (2006). The role of angry rumination and distraction in blood pressure recovery from emotional arousal. Psychosomatic Medicine, 68(1), 64–72. http://dx.doi.org/10.1097/01. psy.0000195747.12404.aa. Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998). Immune neglect: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 75(3), 617–638. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493. Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current status and future directions. Annual Review of Clinical Psychology, 6, 285–312. Gottman, J. M., Coan, J., Carrere, S., & Swanson, C. (1998). Predicting marital happiness and stability from newlywed interactions. Journal of Marriage and Family, 60(1), 5–22. Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G., Zervakis, M., … Vanrumste, B. (2008). Review on solving the inverse problem in EEG source analysis. Journal of Neuroengineering and Rehabilitation, 5, 25. http://dx.doi.org/10.1186/17430003-5-25. Gross, J. J. (1998). Antecedent-and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74(1), 224–237. Gross, J. J. (2013). Handbook of emotion regulation (2nd ed.). New York: Guilford Press. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362.

130

E. Kross and O. Ayduk

Grossmann, I., Gerlach, T. M., & Denissen, J. J. A. (2016). Wise reasoning in the face of everyday life challenges. Social Psychological and Personality Science, 7(7), 611–622. http://dx.doi.org/10.1177/1948550616652206. Grossmann, I., & Kross, E. (2010). The impact of culture on adaptive versus maladaptive self-reflection. Psychological Science, 21(8), 1150–1157. http://dx.doi.org/10.1177/ 0956797610376655. Grossmann, I., & Kross, E. (2014). Exploring solomon’s paradox: Self-distancing eliminates the self-other asymmetry in wise reasoning about close relationships in younger and older adults. Psychological Science, 25(8), 1571–1580. Gruber, J., Harvey, A. G., & Johnson, S. L. (2009). Reflective and ruminative processing of positive emotional memories in bipolar disorder and healthy controls. Behaviour Research and Therapy, 47(8), 697–704. http://dx.doi.org/10.1016/j.brat.2009.05.005. Hajcak, G., Weinberg, A., Macnamara, D., & Foti, D. (2012). ERPs and the study of emotion. In S. J. Luck & E. S. Kappenman (Eds.), Oxford handbook of ERP components (pp. 441–474). New York: Oxford University Press. Hankin, B. L. (2008). Rumination and depression in adolescence: Investigating symptom specificity in a multiwave prospective study. Journal of Clinical Child & Adolescent Psychology, 37(4), 701–713. Hankin, B. L., Stone, L., & Wright, P. A. (2010). Corumination, interpersonal stress generation, and internalizing symptoms: Accumulating effects and transactional influences in a multiwave study of adolescents. Development and Psychopathology, 22(1), 217–235. http://dx.doi.org/10.1017/S0954579409990368. Harvard School of Public Health Poll (October 8–12, 2014). Ebola Poll II. Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., & Lillis, J. (2006). Acceptance and commitment therapy: Model, processes and outcomes. Behaviour Research and Therapy, 44(1), 1–25. Heatherton, T. F. (2011). Neuroscience of self and self-regulation. Annual Review of Psychology, 62, 363–390. http://dx.doi.org/10.1146/annurev.psych.121208.131616. Heller, D., Stephan, E., Kifer, Y., & Sedikides, C. (2011). What will I be? The role of temporal perspective in prediction of affect, traits, and self-narratives. Journal of Experimental Social Psychology, 47, 610–615. Huynh, A., Yang, D. Y. Y., & Grossman, I. (2016). The value of prospective reasoning for close relationships. Social Psychological and Personality Science, 7(8), 893–902. Ingram, R. E., & Hollon, S. D. (1986). Cognitive therapy for depression from an information processing perspective. In R. E. Ingram (Ed.), Information processing approaches to clinical psychology (pp. 259–281). San Diego, CA: Academic Press. Kalisch, R., Wiech, K., Critchley, H. D., Seymour, B., O’Doherty, J. P., Oakley, D. A., … Dolan, R. J. (2005). Anxiety reduction through detachment: Subjective, physiological, and neural effects. Journal of Cognitive Neuroscience, 17(6), 874–883. http://dx.doi.org/ 10.1162/0898929054021184. Katzir, M., & Eyal, T. (2013). When stepping outside the self is not enough: A self-distanced perspectiv reduces the experience of basic but not of self-conscious emotions. Journal of Experimental Social Psychology, 49, 1089–1092. Kenny, L. M., & Bryant, R. A. (2007). Keeping memories at an arm’s length: Vantage point of trauma memories. Behaviour Research and Therapy, 45(8), 1915–1920. Kenny, L. M., Bryant, R. A., Silove, D., Creamer, M., O’Donnell, M., & McFarlane, A. C. (2009). Distant memories: A prospective study of vantage point of trauma memories. Psychological Science, 20(9), 1049–1052. Koenigsberg, H. W., Fan, J., Ochsner, K. N., Liu, X., Guise, K. G., Pizzarello, S., … Siever, L. J. (2009). Neural correlates of the use of psychological distancing to regulate responses to negative social cues: A study of patients with borderline personality disorder. Biological Psychiatry, 66(9), 854–863. http://dx.doi.org/10.1016/j.biopsych.2009.06.010.

Self-Distancing: Theory, Research, and Current Directions

131

Koenigsberg, H. W., Fan, J., Ochsner, K. N., Liu, X., Guise, K., Pizzarello, S., … Siever, L. J. (2010). Neural correlates of using distancing to regulate emotional responses to social situations. Neuropsychologia, 48(6), 1813–1822. http://dx.doi.org/10.1016/j. neuropsychologia.2010.03.002. Kramer, D., & Woodruff, D. S. (1986). Relativistic and dialectical thought in three adult age-groups. Human Development, 29(5), 280–290. Kross, E. (2009). When the self becomes other. Annals of the New York Academy of Sciences, 1167, 35–40. Kross, E., & Ayduk, O. (2008). Facilitating adaptive emotional analysis: Distinguishing distanced-analysis of depressive experiences from immersed-analysis and distraction. Personality and Social Psychology Bulletin, 34(7), 924–938. Kross, E., & Ayduk, O. (2009). Boundary conditions and buffering effects: Does depressive symptomology moderate the effectiveness of distanced-analysis for facilitating adaptive self-reflection? Journal of Research in Personality, 43(5), 923–927. Kross, E., Ayduk, O., & Mischel, W. (2005). When asking “why” does not hurt: Distinguishing rumination from reflective processing of negative emotions. Psychological Science, 16(9), 709–715. Kross, E., Berman, M. G., Mischel, W., Smith, E. E., & Wager, T. D. (2011). Social rejection shares somatosensory representations with physical pain. Proceedings of the National Academy of Sciences of the United States of America, 108(15), 6270–6275. Kross, E., Bruehlman-Senecal, E., Park, J., Burson, A., Dougherty, A., Shablack, H., … Ayduk, O. (2014). Self-talk as a regulatory mechanism: How you do it matters. Journal of Personality and Social Psychology, 106(2), 304–324. http://dx.doi.org/10.1037/ a0035173. Kross, E., Davidson, M., Weber, J., & Ochsner, K. (2009). Coping with emotions past: The neural bases of regulating affect associated with negative autobiographical memories. Biological Psychiatry, 65(5), 361–366. € Tsukayama, E., & Mischel, W. (2011). The effect of Kross, E., Duckworth, A., Ayduk, O., self-distancing on adaptive versus maladaptive self-reflection in children. Emotion, 11(5), 1032–1039. Kross, E., Gard, D., Deldin, P., Clifton, J., & Ayduk, O. (2012). “Asking why” from a distance: Its cognitive and emotional consequences for people with major depressive disorder. Journal of Abnormal Psychology, 121(3), 559–569. Kross, E., & Grossmann, I. (2011). Boosting wisdom: Distance from the self enhances wise reasoning, attitudes, and behavior. Journal of Experimental Psychology: General, 141(1), 43–48. Kross, E., Vickers, B., Orvell, A., Gainsburg, I., Moran, T., Boyer, M., … Ayduk, O. (under review). Nudging people toward rationality: Third person self-talk reduces Ebola anxiety and risk perception. Labov, W., & Fanshel, D. (1977). Therapeutic discourse: Psychotherapy as conversation. New York: Academic Press. Landivar, L. C. (2013). Disparities in STEM employment by sex, race, and Hispanic origin. Education Review, 29, 911–922. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Gainesville, FL: University of Florida, Technical Report A-8. Lazarus, R. S., & Alfert, E. (1964). Short-circuiting of threat by experimentally altering cognitive appraisal. Journal of Abnormal Psychology, 69, 195–205. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY: Springer. Ledgerwood, A., Trope, Y., & Liberman, N. (2010). Flexibility and consistency in evaluative responding: The function of construal level. Advances in Experimental Social Psychology, 43, 259–297.

132

E. Kross and O. Ayduk

Lee, D. S., Kross, E., Briskin, J., Shrapnell, T., & Ybarra, O. (in preparation). Reconstrual facilitates adaptive social support. Leitner, J. B., Ayduk, O., Mendoza-Denton, R., Magerman, A., Amey, R., Kross, E., & Forbes, C. E. (in press). Self-distancing improves interracial mentorship by decreasing medial prefrontal cortex activity during the provision of criticism. Social Cognitive Affective Neuroscience. Libby, L. K., & Eibach, R. P. (2002). Looking back in time: Self-concept change affects visual perspective in autobiographical memory. Journal of Personality and Social Psychology, 82(2), 167–179. Libby, L. K., & Eibach, R. P. (2011). Visual perspective in mental imagery: A representational tool that functions in judgment, emotion, and self-insight. Advances in Experimental Social Psychology, 44, 185–245. Liberman, N., Sagristano, M. D., & Trope, Y. (2002). The effect of temporal perspective on level of mental construal. Journal of Experimental Social Psychology, 110, 523–534. Liberman, N., & Trope, Y. (2008). The psychology of transcending the here and now. Science, 322(5905), 1201–1205. http://dx.doi.org/10.1126/science.1161958. Linehan, M. M., Comtois, K. A., Murray, A. M., Brown, M. Z., Gallop, R. J., Heard, H. L., … Lindenboim, N. (2006). Two-year randomized controlled trial and follow-up of dialectical behavior therapy vs therapy by experts for suicidal behaviors and borderline personality disorder. Archives of General Psychiatry, 63(7), 757–766. http://dx.doi.org/ 10.1001/archpsyc.63.7.757. Lyubomirsky, S., Sousa, L., & Dickerhoof, R. (2006). The costs and benefits of writing, talking, and thinking about life’s triumphs and defeats. Journal of Personality and Social Psychology, 90(4), 692–708. http://dx.doi.org/10.1037/0022-3514.90.4.692. McEwen, B. S. (1998). Protective and damaging effects of stress mediators. The New England Journal of Medicine, 338(3), 171–179. http://dx.doi.org/10.1056/ nejm199801153380307. McIsaac, H. K., & Eich, E. (2002). Vantage point in episodic memory. Psychonomic Bulletin & Review, 9(1), 146–150. McIsaac, H. K., & Eich, E. (2004). Vantage point in traumatic memory. Psychological Science, 15(4), 248–253. Meier, A. (2002). Narrative in psychotherapy theory, practice, and research: A critical review. Counseling and Pschotherapy Research, 2, 239–251. Mendoza-Denton, R., Ayduk, O., Mischel, W., Shoda, Y., & Testa, A. (2001). Person X situation interactionism in self-encoding (I am…when…): Implications for affect regulation and social information processing. Journal of Personality and Social Psychology, 80(4), 533–544. Mendoza-Denton, R., Goldman-Flythe, M., Pietzrak, J., Downey, G., & Aceves, M. J. (2010). Group-value ambiguity understanding the effects of academic feedback on minority students’ self-esteem. Social Psychological and Personality Science, 1, 127–135. Mischel, W., & Ayduk, O. (2004). Willpower in a cognitive-affective processing system: The dynamics of delay of gratification. In R. Baumeister & K. Vohs (Eds.), Handbook of selfregulation research: Research, theory, and applications (pp. 99–129). New York: Guilford. Mischel, W., & Rodriguez, M. L. (1993). Psychological distance in self-imposed delay of gratification. In R. R. Cocking & K. A. Renninger (Eds.), The development and meaning of psychological distance (pp. 109–121). Hillsdale, NJ: Erlbaum. Mischel, W., & Shoda, Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102(2), 246–268. Mischel, W., & Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Psychology, 49, 229–258. http://dx.doi.org/10.1146/annurev. psych.49.1.229.

Self-Distancing: Theory, Research, and Current Directions

133

Mischowski, D., Kross, E., & Bushman, B. (2012). Flies on the wall are less aggressive: The effect of self-distancing on aggressive affect, cognition, and behavior. Journal of Experimental Social Psychology, 48, 1187–1191. http://dx.doi.org/10.1016/j. jesp.2012.03.012. Monson, C. M., Schnurr, P. P., Resick, P. A., Friedman, M. J., Young-Xu, Y., & Stevens, S. P. (2006). Cognitive processing therapy for veterans with military-related posttraumatic stress disorder. Journal of Consulting and Clinical Psychology, 74(5), 898–907. http://dx.doi.org/10.1037/0022-006X.74.5.898. Mor, N., & Winquist, J. (2002). Self-focused attention and negative affect: A meta-analysis. Psychological Bulletin, 128(4), 638–662. Moser, J. S., Dougherty, A., Moran, T., Gueverra, D., Ayduk, O., Jonides, J., … Kross, E. (under review). Third-person self-talk facilitates emotional control without engaging cognitive control: Converging evidence from ERP and fMRI. Moser, J. S., Hartwig, R., Moran, T. P., Jendrusina, A. A., & Kross, E. (2014). Neural markers of positive reappraisal and their associations with trait reappraisal and worry. Journal of Abnormal Psychology, 123(1), 91–105. http://dx.doi.org/10.1037/ a0035817. Moser, J. S., Krompinger, J. W., Dietz, J., & Simons, R. F. (2009). Electrophysiological correlates of decreasing and increasing emotional responses to unpleasant pictures. Psychophysiology, 46(1), 17–27. http://dx.doi.org/10.1111/j.1469-8986.2008.00721.x. PSYP721 [pii]. Nigro, G., & Neisser, U. (1983). Point of view in personal memories. Cognitive Psychology, 15(4), 467–482. Nolen-Hoeksema, S., Morrow, J., & Fredrickson, B. L. (1993). Response styles and the duration of episodes of depressed mood. Journal of Abnormal Psychology, 102(1), 20–28. Nolen-Hoeksema, S., Stice, E., Wade, E., & Bohon, C. (2007). Reciprocal relations between rumination and bulimic, substance abuse, and depressive symptoms in female adolescents. Journal of Abnormal Psychology, 116(1), 198. Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424. Ochsner, K., & Gross, J. J. (2008). Cognitive emotion regulation: Insights from social cognitive and affective neuroscience. Current Directions in Psychological Science, 17(2), 153–158. Ochsner, K., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D. E., & Gross, J. J. (2004). For better or for worse: Neural systems supporting the cognitive down-and up-regulation of negative emotion. NeuroImage, 23(2), 483–499. Orvell, A., Bruehlman-Senecal, E., Kross, E., & Ayduk, O. (in preparation). From laboratory to daily life: Self-distancing training buffers against daily rumination and depression over time. Page-Gould, E., Mendoza-Denton, R., & Tropp, L. R. (2008). With a little help from my cross-group friend: Reducing anxiety in intergroup contexts through cross-group friendship. Journal of Personality and Social Psychology, 95(5), 1080–1094. http://dx.doi. org/10.1037/0022-3514.95.5.1080. Park, J., Ayduk, O., & Kross, E. (2016). Stepping back to move forward: Expressive writing promotes self-distancing. Emotion, 16(3), 349–364. http://dx.doi.org/10.1037/emo0000121. Park, J., Ayduk, O., O’Donnell, L., Chun, J., Gruber, J., Kamali, M., … Kross, E. (2014). Regulating the high: Cognitive and neural processes underlying positive emotion regulation in bipolar I disorder. Clinical Psychological Science, 2(6), 661–674. http://dx.doi. org/10.1177/2167702614527580. Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18(1), 49–65.

134

E. Kross and O. Ayduk

Pennebaker, J. W., & Chung, C. K. (2007). Expressive writing, emotional upheavals, and health. Handbook of Health Psychology (pp. 263–284). New York: Oxford University Press. Pennebaker, J. W., & Graybeal, A. (2001). Patterns of natural language use: Disclosure, personality, and social integration. Current Directions in Psychological Science, 10(3), 90–93. Pennebaker, J. W., Mayne, T. J., & Francis, M. E. (1997). Linguistic predictors of adaptive bereavement. Journal of Personality and Social Psychology, 72(4), 863–871. Penner, L. A., Guevarra, D. A., Harper, F. W., Taub, J., Phipps, S., Albrecht, T. L., & Kross, E. (2016). Self-distancing buffers high trait anxious pediatric cancer caregivers against short-and longer-term distress. Clinical Psychological Science, 4, 629–640. http:// dx.doi.org/10.1177/2167702615602864. Pfeiler, T. M., Wenzel, M., Weber, H., & Kubiak, T. (2015). Adaptive modes of rumination: The role of subjective anger. Cognition & Emotion, 9, 1–10. http://dx.doi.org/ 10.1080/02699931.2015.1117961. Plant, E. A., & Devine, P. G. (1998). Internal and external motivation to respond without prejudice. Journal of Personality and Social Psychology, 75, 811–832. Plant, E. A., & Devine, P. G. (2003). The antecedents and implicaitons of interracial anxiety. Personality and Social Psychology Bulletin, 29(6), 790–801. Polanyi, L. (1982). Linguistic and social constraints on storytelling. Journal of Pragmatics, 6, 509–524. Pronin, E., & Ross, L. (2006). Temporal differences in trait self-ascription: When the self is seen as an other. Journal of Personality and Social Psychology, 90(2), 197–209. http://dx.doi. org/10.1037/0022-3514.90.2.197. Ranney, R., Bruehlman-Senecal, E., & Ayduk, O. (2016). A brief online intervention comparing the efficacy of teaching specific reappraisal strategies. Journal of Happiness Studies. Ray, R. D., Wilhelm, F. H., & Gross, J. J. (2008). All in the mind’s eye? Anger rumination and reappraisal. Journal of Personality and Social Psychology, 94(1), 133–145. Resick, P. A., & Schnicke, M. K. (1992). Cognitive processing therapy for sexual assault victims. Journal of Consulting and Clinical Psychology, 60(5), 748–756. Ressler, K. J., & Mayberg, H. S. (2007). Targeting abnormal neural circuits in mood and anxiety disorders: From the laboratory to the clinic. Nature Neuroscience, 10(9), 1116–1124. http://dx.doi.org/10.1038/nn1944. Robinson, J. A., & Swanson, K. L. (1993). Field and observer modes of remembering. Memory, 1(3), 169–184. Rusting, C. L., & Nolen-Hoeksema, S. (1998). Regulating responses to anger. Journal of Personality and Social Psychology, 74(3), 790–803. Ryan, S. (2012). Wisdom, knowledge and rationality. Acta Analytica, 27(2), 99–112. Schartau, P. E., Dalgleish, T., & Dunn, B. D. (2009). Seeing the bigger picture: Training in perspective broadening reduces self-reported affect and psychophysiological response to distressing films and autobiographical memories. Journal of Abnormal Psychology, 118(1), 15–27. http://dx.doi.org/10.1037/a0012906. Schultz, L. T., & Heimberg, R. G. (2008). Attentional focus in social anxiety disorder: Potential for interactive processes. Clinical Psychology Review, 28(7), 1206–1221. http://dx.doi.org/10.1016/j.cpr.2008.04.003. Schwartz, J. A. J., & Koenig, L. J. (1996). Response styles and negative affect among adolescents. Cognitive Therapy and Research, 20(1), 13–36. Segal, Z. V., Williams, J. M. G., & Teasdale, J. D. (2002). Mindfulness-based cognitive therapy for depression: A new approach to preventing relapse. New York: Guilford Press. Shiota, M. N., & Levenson, R. W. (2012). Turn down the volume or change the channel? Emotional effects of detached versus positive reappraisal. Journal of Personality and Social Psychology, 103(3), 416–429. http://dx.doi.org/10.1037/a0029208.

Self-Distancing: Theory, Research, and Current Directions

135

Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: A review of the definition, assessment, and conceptualization of this multifaceted construct. Clinical Psychology Review, 29(2), 116–128. Smyth, J. M. (1998). Written emotional expression: Effect sizes, outcome types, and moderating variables. Journal of Consulting and Clinical Psychology, 66(1), 174–184. Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89(6), 845–851. http://dx.doi. org/10.1037/0022-3514.89.6.845. Stanton, A. L., Kirk, S. B., Cameron, C. L., & Danoff-Burg, S. (2000). Coping through emotional approach: Scale construction and validation. Journal of Personality and Social Psychology, 78(6), 1150–1169. Staudinger, U. M., & Gluck, J. (2010). Psychological wisdom research: Commonalities and differences in a growing field. Annual Review of Psychology, 62, 215–241. Sternberg, R. J. (1998). A balance theory of wisdom. Review of General Psychology, 2(4), 347–365. Taylor, S. E., & Schneider, S. K. (1989). Coping and the simulation of events. Social Cognition, 7, 174–194. Tenke, C. E., & Kayser, J. (2012). Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions. Clinical Neurophysiology, 123(12), 2328–2345. http://dx.doi.org/10.1016/j.clinph.2012. 06.005. Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110(3), 403–421. Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117(2), 440–463. Tugade, M. M., & Fredrickson, B. L. (2004). Resilient individuals use positive emotions to bounce back from negative emotional experiences. Journal of Personality and Social Psychology, 86(2), 320–333. http://dx.doi.org/10.1037/0022-3514.86.2.320. Turner, S. M., Beidel, D. C., & Larkin, K. T. (1986). Situational determinants of social anxiety in clinic and nonclinic samples: Physiological and cognitive correlates. Journal of Consulting and Clinical Psychology, 54, 523–527. Vasquez, N. A., & Buehler, R. (2007). Seeing future success: Does imagery perspective influence achievement motivation? Personality and Social Psychology Bulletin, 33(10), 1392–1405. http://dx.doi.org/10.1177/0146167207304541. Verduyn, P., Van Mechelen, I., Kross, E., Chezzi, C., & Van Bever, F. (2012). The relationship between self-distancing and the duration of negative and positive emotional experiences in daily life. Emotion, 12(6), 1248–1263. http://dx.doi.org/10.1037/a0028289. Vorauer, J. D. (2006). An information search model of evaluative concerns in intergroup interaction. Psychological Review, 113(4), 862–886. http://dx.doi.org/10.1037/0033295X.113.4.862. Vorauer, J. D., & Turpie, C. A. (2004). Disruptive effects of vigilance on dominant group members’ treatment of outgroup members: Choking versus shining under pressure. Journal of Personality and Social Psychology, 87(3), 384–399. http://dx.doi.org/10.1037/00223514.87.3.384. Webb, T. L., Miles, E., & Sheeran, P. (2012). Dealing with feeling: A meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychological Bulletin, 138(4), 775–808. http://dx.doi.org/10.1037/a0027600. Wells, A., Clark, D. M., & Ahmad, S. (1998). How do I look with my minds eye: Perspective taking in social phobic imagery. Behavior Research and Theory, 36(6), 631–634. White, R. E., & Carlson, S. M. (2016). What would Batman do?: Self-distancing improves executive function in young children. Developmental Science, 19(3), 419–426.

136

E. Kross and O. Ayduk

White, R. E., Kross, E., & Duckworth, A. L. (2015). Spontaneous self-distancing and adaptive self-reflection across adolescence. Child Development, 86(4), 1272–1281. http://dx. doi.org/10.1111/cdev.12370. White, R. E., Kuehn, M. M., Kross, E., & Ayduk, O. (under review). Focusing on the future from afar: Self-distancing from future stressors facilitates adaptive coping. White, R. E., Prager, E. O., Schaefer, C., Kross, E., Duckworth, A. L., & Carlson, C. R. (in press). The “Batman Effect”: Improving perseverance in young children. Child Development. Wilson, T. D., & Gilbert, D. T. (2008). Explaining away: A model of affective adaptation. Perspectives on Psychological Science, 3(5), 370–386. Wisco, B. E., Marx, B. P., Sloan, D. M., Gorman, K. R., Kulish, A. L., & Pineles, S. L. (2015). Self-distancing from trauma memories reduces physiological but not subjective emotional reactivity among Veterans with posttraumatic stress disorder. Clinical Psychological Science, 3(6), 956–963. http://dx.doi.org/10.1177/2167702614560745. Wisco, B. E., & Nolen-Hoeksema, S. (2011). Effect of visual perspective on memory and interpretation in dysphoria. Behaviour Research and Therapy, 49(6–7), 406–412. Ybarra, O., Rees, L., Kross, E., & Sanchez-Burks, J. (2011). Social-emotional intelligence for creating positive organization environments. In K. Cameron & G. Spreitzer (Eds.), Handbook of positive organizational scholarship (pp. 201–214). New York: Oxford University Press. Zell, E., Warriner, A. B., & Albarracin, D. (2012). Splitting of the mind: When the you I talk to is me and needs commands. Social Psychological and Personality Science, 3(5), 549–555. Ziegert, D. I., & Kistner, J. A. (2002). Response styles theory: Downward extension to children. Journal of Clinical Child & Adolescent Psychology, 31(3), 325–334.

CHAPTER THREE

Essentially Biased: Why People Are Fatalistic About Genes S.J. Heine*,1, I. Dar-Nimrod†, B.Y. Cheung*, T. Proulx{ *University of British Columbia, Vancouver, BC, Canada † University of Sydney, Sydney, NSW, Australia { University of Cardiff, Cardiff, Wales, United Kingdom 1 Corresponding author: e-mail address: [email protected]

Contents 1. Psychological Essentialism 1.1 Genetic Essentialism 1.2 Are These Biases Irrational? 1.3 Genetic Essentialism Is Widespread and Distorts People’s Understanding 2. The Impact of Genetic Attributions on People’s Perceptions 2.1 Sex and Gender 2.2 Sexual Orientation 2.3 Health 2.4 Race and Ancestry 2.5 Criminality 2.6 Political Orientation 2.7 Essences and Eugenics 2.8 Genetic Engineering 3. Perniciousness of Genetic Essentialism 3.1 Short-Term Efforts to Reduce Genetic Essentialism 3.2 Long-Term Efforts 4. Conclusion References

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Abstract We propose that people are genetic essentialists—that is, they tend to think of genetic attributions as being immutable, of a specific etiology, natural, and dividing people into homogenous and discrete groups. Although there are rare conditions where genes operate in these kinds of deterministic ways, people overgeneralize from these to the far more common conditions where genes are not at all deterministic. These essentialist biases are associated with some harmful outcomes such as racism, sexism, pessimism in the face of illnesses, political polarization, and support for eugenics, while at the same time they are linked with increased tolerance and sympathy for gay rights, mental illness, and less severe judgments of responsibility for crime. We will also discuss how these essentialist biases connect with the burgeoning direct-to-consumer genomics Advances in Experimental Social Psychology, Volume 55 ISSN 0065-2601 http://dx.doi.org/10.1016/bs.aesp.2016.10.003

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industry and various kinds of genetic engineering. Overall, these biases appear rather resistant to efforts to reduce them, although genetics literacy predicts weaker essentialist tendencies.

By all accounts, the genomics revolution has arrived. Since the first human genome was sequenced in 2003, the cost of sequencing genomes has fallen by more than a million fold, with the technical advancements of genotyping technologies far outpacing the rapid speed of improvements in computer transistors that was famously encapsulated in Moore’s law (National Human Genome Research Institute, 2016). As a result of these dramatic technical innovations, the new industry of direct-to-consumer (DTC) genomics companies has recently emerged, allowing individuals to have their genomes sequenced at a relatively affordable price. Customers can be provided with information that purportedly speaks to their likely genetic ancestry, their risks for developing various fatal diseases, and their children’s future career possibilities. Alongside this development, a vast scientific enterprise that links specific genetic variants to various human conditions continues to receive wide coverage in the popular media. How will people make sense of this brave new world of genomics? A growing field of psychological research has emerged to address this question. It may seem strange to ask how people will understand a new scientific revolution. Won’t they just come to learn this new information in the same way that they learn other information? That is, we might expect that people will respond more or less like scientists themselves do, whereby they slowly integrate their new insights with their existing ones, and ultimately developing a richer understanding that empowers them to more effectively interact with their world (cf. Kuhn, 1962/1996). On the other hand, much research has revealed that people do not process information about genetic attributions in a rational and even-handed way (for a review, see Dar-Nimrod & Heine, 2011; Heine, 2017). Rather, genetic causes are often understood fundamentally differently from other kinds of causes, and as we’ll elaborate later, this has significant implications for the ways that people make sense of their worlds. As a case in point, consider the following study (Dar-Nimrod, Cheung, Ruby, & Heine, 2014; Study 3): participants were randomly assigned to read one of three newspaper articles that discussed actual research related to obesity. Those who were assigned to a “Genetics” condition read about research describing how “obesity genes” relate to one’s weight. Another group was

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assigned to an “Experiences” condition and read about research describing how people’s social networks relate to their weight. A third group was assigned to a Control condition where they read an unrelated article about corn production. Following a series of food-related assignments to mask the purpose of the study, participants took part in a food tasting task; they were given some cookies to sample and evaluate their flavors. The key-dependent variable was the amount of cookies that people ate. The results revealed that those in the “Genetics” condition consumed approximately one-third more cookies than those assigned to either the “Experiences” or the “Control” condition, which did not differ significantly from each other. The results of this study, together with those from several correlational studies in the same paper, suggest that people became more fatalistic about their weight when learning about genetic causes for obesity, but not when learning about experiential causes for obesity. This relative overweighing of genetic causes occurred even though both genes and experiences contribute to risk for obesity. Why might genetic causes be perceived differently than experiential ones?

1. PSYCHOLOGICAL ESSENTIALISM We can gain insight into how people consider genetic causes when we consider the broader question of how people make sense of why things in general are the way they are. Aristotle provided an answer to this metaphysical question by proposing that all entities are as they are because of an underlying essence that they possess which makes them so (Moravcsik, 2001). Essences were described by Locke (1671/1959) as “the very being of anything, whereby it is what it is.” They are imagined to be deep, internal forces that form the basis of identity of entities, and cause the entities to function as they do. For example, essences are what give rise to a cat’s agility, aloofness, hunting prowess, soft furry coat, curiosity, and penchant for catnip—without such an essence, cats would be very different creatures. Of course, trying to specify what precisely is the underlying essence of a cat, and how this essence leads to all those cat-like characteristics, is metaphysically intractable. But the question embraced by psychologists is not whether there actually are essences that undergird the reality which we live in, but whether people believe that these kinds of essences indeed make things so (Medin & Ortony, 1989). This belief that essences give rise to entities is termed psychological essentialism.

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Evidence for psychological essentialism is widespread. Indeed, there are few psychological phenomena which have been identified in a broader array of samples than essentialism: studies have found support for essentialism in samples of Chaldean and Hmong immigrants in Detroit (Henrich & Henrich, 2007), Mongolian herders (Gil-White, 2001), Vezo children in Madagascar (Astuti, 2001), Menominee in Wisconsin (Waxman, Medin, & Ross, 2007), rural Ukrainians (Kanovsky, 2007), children from poor neighborhoods in Brazil (Sousa, Atran, & Medin, 2002), and several dozens of studies involving children and adults from Western countries (see Gelman, 2003, for a review). However, despite this universality in prevalence, there is evidence that some populations are more committed to essentialist thinking than others (e.g., Giles, Legare, & Samson, 2008). For example, higher SES Americans hold more essentialist views than those of lower SES (Kraus, Piff, Mendoza-Denton, Rheinschmidt, & Kelter, 2012). Likewise, some psychological phenomena related to essentialism, such as a tendency to make dispositional attributions (Choi, Nisbett, & Norenzayan, 1999) and to have more entity theories of self (Heine et al., 2001), are more prevalent among Western populations than among East Asians. This suggests that psychological essentialism could qualify as a functional universal, serving a similar function everywhere, although the degree of commitment varies across populations (Norenzayan & Heine, 2005). Some have argued that it was adaptive for an omnivorous foraging species like humans to categorize the natural world around them into different species on the basis of imagined essences, to facilitate their success at foraging (e.g., Atran, 1998). Research has identified a number of characteristics associated with psychological essentialism, and as we will explain later, these influence how people think about genetic causes. First, essences are perceived to be immutable. The properties that they carry are seen to persist and remain somewhat inviolate to one’s experiences (for a review, see Gelman, 2003). They are perceived to lie deep within an individual, beyond the penetration of outside influences. For an example of this quality of essences, consider a study by Gil-White (2001) who interviewed Mongolian herdsman from two tribes: the Uryankhais, who are believed to be able to cast curses, and the Torguuds, who are not thought to have any such curse-casting abilities. Gil-White posed a question to the herders: if a boy born to Uryankhai parents was raised by Torguuds, would he be able to cast curses? The interviewees responded that the boy would indeed have such capability, given his Uryankhai birth parents, although he would not know it, given his Torguud upbringing. The

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boy’s Uryankhai essence is perceived to remain intact and is not compromised despite his exclusively Torguud experiences. This sense that one’s upbringing does not impact one’s underlying essence is not likely the result of education, as it has been documented with young children across a variety of contexts (Giles, 2003; Hirschfeld & Gelman, 1997). For example, by the age of five, children show evidence of a belief in the immutability of essences even in the face of large-scale transformations, such as a caterpillar developing in to a butterfly (Rosengren, Gelman, Kalish, & McCormick, 1991). Entities may encounter a variety of experiences and transformations in their lives, but these are perceived to be inconsequential to the underlying essence (Gelman, 2004). Second, people think of essences as being deep down and internal, beyond the reach of external influences. We do not actually see essences; they lie beneath the surface far beyond our visual field. Yet people believe that essences give rise to all that they see around them. Experiments where people are presented with natural kinds that have encountered some kinds of modifications to their surface reveal that people still believe that the underlying essence remains intact (e.g., Rips, 1989). By the age of three, children view transformations of an object’s insides as impacting the object’s identity more than transformations to the object’s outsides, as the basis of identity lies with its deeply buried essence (Gelman & Welman, 1991). However, despite lying outside of our ability to see them, people believe that the essences are potentially accessible to experts (Gelman & Markman, 1987). Third, people think of essences as underlying the natural world; they play a much weaker role for our understanding of artifacts. So while people may perceive essences to differentiate between gold and fool’s gold, they are less likely to employ essences to define artifacts. The distinction between gold and fool’s gold is believed to be due to an underlying essence that is supposedly identifiable by experts; in contrast, the distinction between a station wagon and a sports utility vehicle is understood more to be a matter of convention (see also Gil-White, 2001; Malt, 1989). This distinction in the role of underlying essences between natural kinds (i.e., categories that exist in the natural world) and artifacts is also evident among children. For example, Keil (1989) presented kindergarten students with a thought experiment where a raccoon had been altered to look like a skunk, yet the children insisted that it remained a raccoon under its skunk dressing. On the other hand, the same children were quite willing to accept that a coffeepot had been transformed into a birdfeeder. Essences are defining elements for making sense of the perceived natural world and are less consequential for understanding artifacts.

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A fourth feature of essences is that they are perceived to draw the boundaries of categories. They represent what members of a category share in common, and what differentiate them from members of different categories (Medin & Ortony, 1989). This is especially so in the way people turn to essences to understand different species (Atran, 1998); for example, the basis of modern scientific taxonomy developed by Carl Linnaeus hinged on an assumption that different species derive from distinct essences (Ereshefsky, 2004). Likewise, this assumption also generalizes to people’s perceptions of ethnic groups (Gil-White, 2001; Rothbart & Taylor, 1992) and serves as one key foundation of prejudice (Allport, 1954). Essences seem to carve nature at its joints and as a result, people feel they can make inductive inferences about what a newly encountered individual may or may not be able to do based on the imagined essence of its group (Gelman, 2004). Finally, essences can be transferred from individual to individual while preserving their original identity. One way we can see this is that objects are perceived to be able to acquire the essences of their owners; a notion that is apparent from a young age (Gelman, Frazier, Noles, Manczak, & Stilwell, 2015). For example, Nemeroff and Rozin (1994) found that some American undergraduate students balked at the idea of wearing Hitler’s sweater, regardless of how much it has been dry cleaned, for fear of coming in touch with his contaminating essence. In another example, approximately one-third of recipients of heart-transplants feel that they have acquired traits from their donors (Inspector, Kutz, & David, 2004). Likewise, prior to any education about heritability, young children understand that a child comes to acquire some aspects of essences from their biological parents even if they were adopted (Heyman & Gelman, 2000). Essences thus are understood to be able to move from object to object and from parent to offspring. In sum, people’s intuitive understanding of essences comes wrapped up with some particular ideas about the ways that essences undergird the natural world.

1.1 Genetic Essentialism Despite having rather specific ideas about what essences are like, people have a harder time forming concrete mental representations of essences. Hence, they turn to an essence placeholder that serves as a scaffolding that affords explanations for how any observed characteristics have come to be (Medin & Ortony, 1989). People have turned to various essence placeholders throughout history for making sense of their worlds, such as the four humors of Hippocrates that were assumed to be critical for understanding health and

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personality, Chinese conceptions of chi, or the yogic belief in prana. But genes make a particularly apt placeholder for essences, and this leads people to imagine that genes share many of the features that they associate with essences (for more description, see Heine, 2017). Just as with essences, genes are perceived to be deep down and internal and thus share the same features as a nonmaterialistic placeholder, namely, not being constrained by any visible limitations in a manner that would bound future causal inferences (Medin & Ortony, 1989). Likewise, just as people believe that essences are potentially knowable to experts, people will often readily accept scientific claims of a novel genetic causal explanation for various phenomena (Dar-Nimrod & Godwin, 2016). Moreover, similar to how they perceive essences, people view genes as far-reaching causal factors—they offer reasons to explain a diverse array of phenomena and provide a succinct account for why people behave in the ways they do (Jayaratne et al., 2009). In addition, as with essences, genes are understood to be transferred from one generation to another (Gil-White, 2001). And, just as with essences, genes are perceived to be stable and unchanging throughout a person’s lifetime, facilitating a sense of self-unity despite the overwhelming physical and psychological transformations that occur across their development (Chandler & Proulx, 2008). Genes are therefore remarkably well suited to serve as essence placeholders, given that people’s understanding of genes aligns well with how people conceive of essences. Because of this overlap with people’s essentialist intuitions, we submit that when most people are thinking about genes they are not really thinking about genes—they are thinking about metaphysical essences. Conceiving of genes as essence placeholders suggests that people’s understanding of genetics may be somewhat distorted. In line with this, a body of research documents an enduring, limited public understanding of basic genetic science (Condit, 2010; Henderson & Maguire, 2000). For example, a survey of American adults (Lanie et al., 2004) found that fewer than half could correctly answer the question, “Where in your body are your genes located?” (the correct answer is in your cells). Likewise, based on a US nationally representative survey of American adults, Christensen, Jayaratne, Roberts, Kardia, and Petty (2010) found that 76% incorrectly believed that “single genes directly control specific human behaviors.” Moreover, with regard to evaluating US high school students’ knowledge of genetics, the National Assessment of Education Progress (NAEP) revealed substantial deficiencies in genetic proficiency (O’Sullivan et al., 2003), most commonly observed in students’ interpretation of genetic materials and

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understanding of genetic diseases. The NAEP included eight questions probing students’ understanding of genetics and genetic engineering (e.g., what is a gene? what is it made out of ?). It also provided an article discussing the use of viruses in genetic engineering and asked the examinees to utilize their own knowledge along with the materials in the article, in answering relevant questions. Only 1% of 12th graders provided accurate responses that reflected appropriate integration of their knowledge and the genetic essays they read, demonstrating broad misconceptions among soon-to-be high school graduates. Even among arguably the most informed students, those who voluntarily submitted an essay to the American Society of Human Genetics’ National DNA Day Essay Contest, more than half demonstrated a common misunderstanding of basic genetic concepts and essentialist biases (Shaw, Van Horne, Zhang, & Boughman, 2008). These various misconceptions about genes may be facilitated by the pervasive representations of characteristics and conditions as genetically derived which appear across various socialization agents (families, media, the arts, or schools). Frequently, simple OGOD (one gene, one disease; Conrad, 2002) accounts are offered in making sense of complex phenomena, facilitating the assumption that there is a single corresponding genetic cause underlying every human trait. Whether one reads a newspaper article entitled “‘Fat’ gene found by scientists” (Henderson, 2007), watches a Hollywood blockbuster in which genes are presented deterministically (e.g., Gattaca, The Hulk, X-Men), or is told that the “gene for alcoholism runs in my family,” these commonplace exposures contribute to the implicit endorsement of genes as the essence of personhood. These simplified representations are straightforward, simple to digest, and are commonly tainted by an erroneous fatalistic flair (Conrad, 1999; Dar-Nimrod & Heine, 2011). A genetics curriculum focusing on Mendelian models that highlight deterministic inheritance only exacerbates such reductionist notions (Dar-Nimrod, 2012; Dougherty, 2009; Radick, 2016). Regardless of how people come to acquire their understanding of genetics, it is common for them to perceive genes in a simplified and almost mystical, agentic fashion (e.g., Nelkin & Lindee, 1995; Sheldon, Pfeffer, Jayaratne, Feldbaum, & Petty, 2007), ignorant of the complex, interactive processes that invariably occur between genes and environmental factors (for thoughtful discussions, see Pinker, 2003; Ridley, 2003; Turkheimer, 2000). Despite harboring misconceptions about genes and their operations (e.g., Christensen et al., 2010; Lanie et al., 2004), people readily invoke genes to explicate a broad range of human afflictions, capabilities, and

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behaviors (e.g., Condit, Parrott, Harris, Lynch, & Dubriwy, 2004; Gelman & Welman, 1991; Jayaratne et al., 2006; Sheldon et al., 2007). As people’s understanding of genetics is extremely limited, it seems that genes are often invoked as the embodiment of a metaphysical essence rather than as components of a biological process for building proteins (Dar-Nimrod & Heine, 2011). This is not to say that essentialist explanations are the sole kind of causal explanations that people consider (e.g., people readily accept that smoking is causally related to lung cancer), nor that all essentialist explanations rest on a foundation of genes (for other exemplars, see Gil-White, 2001; Haslam, 2011; Rangel & Keller, 2011). Rather, given the substantial conceptual overlap between people’s lay understanding of both genes and natural kinds’ metaphysical essences, genetic attributions often activate or strengthen certain essentialism-derived cognitive biases. The Genetic Essentialism Framework (GEF; Dar-Nimrod & Heine, 2011) offers a theoretical foundation for describing cognitive processes set in motion once a person perceives genes to be a relevant causal factor. The GEF suggests that genetic attributions for various traits, conditions, or diseases activate four specific psychological processes termed genetic essentialist biases. The first bias, immutability/determinism, specifies that thinking about genetic attributions lead people to view relevant outcomes as less changeable and more predetermined (see Gould & Heine, 2012). To the extent that a phenomenon is perceived to be immutable, it will be perceived to be beyond someone’s control; and indeed, genetic attributions decrease perceptions of control over relevant outcomes (e.g., Parrott & Smith, 2014) and limit the perceived capability of other means, such as environmental manipulations or individuals’ volition, to modify the outcome (e.g., Jayaratne et al., 2009). For example, research indicates that endorsement of genetic etiological explanations for disease is negatively associated with perception of control over a disease (e.g., Dar-Nimrod, Zuckerman, & Duberstein, 2013; Jayaratne, Giordimaina, & Gaviglio, 2012; Shiloh, Rashuk-Rosenthal, & Benyamini, 2002). Discounting one’s personal control also leads to a reduction in perceived capability to execute a desirable behavior, which may lead to decreased domain-specific self-efficacy (Bandura, 1977). In line with that assertion, a study of inactive university students found that exercise-related self-efficacy is lower after exposure to a genetic attribution for inactivity compared with an experiential attribution (Beauchamp, Rhodes, Kreutzer, & Rupert, 2011).

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Perception of immutability is not the only relevant cognition that affects perceived behavioral control when genetic etiology is implicated. The second genetic essentialist bias, the tendency to discount additional causal explanations once genetic attributions are made (termed single or specific etiology), also increases the likelihood that people will disregard alternative casual attributions for complex phenomenon (Dar-Nimrod & Heine, 2011). In accordance with this bias, identifying a particular gene can become conflated with a diagnosis of the related condition. Genes seem to be especially suited for narrowing the search for causes, because when genes are implicated as potential etiological explanations, they are viewed as more specific than experiential explanations to the outcome (Dar-Nimrod, Cheung, et al., 2014, Study 2). For example, someone who learns that she does not have “the gene for breast cancer,” might then view herself as not needing to engage in any future screening efforts, which would be a grossly incorrect conclusion from the genetic testing results. In line with this bias, research indicates that whereas environmental causal attributions are positively correlated with a sense of personal choice, greater endorsement of genetic attributions are negatively associated with both environmental attributions and a sense of choice (Jayaratne et al., 2009). Whereas the first two genetic essentialist biases focus on individuals, the third one extends the attention to groups. The metaphysical essence is at its core a category-enabling construct; that is, it is the identifying facet that determines membership in a specific natural group (e.g., being a cat). It follows that essentialist thinking leads a person to focus on the central identifying features that are common to all the members, drawing attention away from ingroup differentiating features. A focus on the commonalities should lead to viewing individual members of a category as more homogeneous because they share the identifying features (e.g., “catness”); as such, essentialist thinking also brings to the fore the distinctiveness of members of a category from those who are not in it and do not share these defining features (e.g., a whale). Hence, genes, like essences, can be seen to carve nature at its joints. The same principle seems to operate with social categories, such as race or gender (Haslam, Rothschild, & Ernst, 2000; Rothbart & Taylor, 1992). And indeed, this third genetic essentialist bias, homogeneity/discreteness, asserts that genetic explanations for (social/natural) group differences increase the likelihood that each group will be viewed as homogeneous and more discrete from each other. Consistent with this bias, a large public opinion survey indicated that conservatives, who are more likely to see racial groups as homogenous and distinct from each other than liberals

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(e.g., Gillborn, 1997), are also more likely to view racial differences as caused by genetic dissimilarities (Suhay & Jayaratne, 2012). Utilizing an experimental approach, Brescoll and LaFrance (2004) found that highlighting biological/genetic differences between men and women increased adoption of stereotypical descriptors. The final genetic essentialist bias is naturalness, that is, genetic attributions increase the likelihood that a relevant outcome is perceived as a natural outcome. There is long-standing evidence in psychology that viewing an outcome as natural has an important evaluative component attached to it. Research on the naturalistic fallacy (e.g., Frankena, 1939; Moore, 1903; see also the related longevity bias: Eidelman & Crandall, 2014) has consistently demonstrated that elements which are perceived as natural are also perceived as inherently good and desirable. Whether it is the romantic notion behind Rousseau’s uncorrupted noble savage, the large “all natural ingredients” labels on goods at the supermarket, or political ideologies harnessing the term “natural order” to perpetuate their control, the evidence for such a tendency is all around us. By offering genes as causal explanation, the outcome is implicitly viewed to be natural and, by extension, appropriate. And indeed, men show increased moral acceptance of undesirable behaviors such as date rape when genes are even remotely implicated as opposed to societal forces (Dar-Nimrod, Heine, Cheung, & Schaller, 2011, Study 2). Furthermore, consistent with the naturalness bias, despite the consensual scientific position that genetically modified organisms (GMOs) are safe to eat, a majority of consumers wish to avoid them, often justifying their opposition by concluding that these products are “against nature” (Blancke, Van Breusegem, De Jaeger, Braeckman, & Van Montagu, 2015, p. 415). In that sense, genetic attributions may be used to increase acceptance of specific outcomes by appealing to our tendency to conflate what naturally “is” with what “ought to be,” although, as we shall see in later sections, attempts to strategically use this bias do not produce straightforward outcomes. Recently, a new measure, the Genetic Essentialist Tendencies Scale (GETS; Dar-Nimrod, 2014), was constructed to assess these four biases. With six items addressing each of the biases, the 24-item measure targets the different facets of genetic essentialism (e.g., Immutability: “People with a genetic predisposition to a certain personality are destined to behave in a certain way”; Specific Etiology: “The environment does not affect the chances of getting cancer for someone with a genetic susceptibility to cancer”; Homogeneity: “People with a gene associated with risk taking are

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probably quite similar”; Naturalness: “It is natural to behave aggressively if one has a genetic predisposition to aggression”). Data collected from community samples in several studies confirm the fit of a four factor underlying structure of the measure. They also uncover moderate to strong positive correlations (0.30 < r < 0.60) between the different biases and a factorial structure that supports the notion that genetic essentialism can be conceptualized as a unitary, second-order construct comprised from these separate biases (Dar-Nimrod et al., 2016). These studies revealed that the biases, as measured by the GETS, predicted varied outcomes such as fatalism, negative views of human nature (i.e., social cynicism), various forms of prejudice, health pessimism (both in general and when genes are implicated for a specific disease), and reduced intentions to engage in healthy behaviors, among others (Dar-Nimrod et al., 2016).

1.2 Are These Biases Irrational? We describe the aforementioned intuitions about genetic essentialism as biases. But what if instead these intuitions are a good-enough approximation of the ways that genes actually influence our phenotypes? On what grounds can we call them biases as opposed to accurate descriptions of the nature of genetic causes? Indeed, it is not difficult to come up with examples of genetic causes which map nicely on to the intuitions that we described earlier. For example, going right back to the origins of genetics research, we can consider the observations of Gregor Mendel’s original studies with pea plants that were conducted in the gardens of St. Thomas’s Abbey in the present day Czech Republic. Mendel found seven characters of peas that were passed faithfully from parent to offspring, and ultimately provided the scientific world with the key notions of genetic segregation and dominance (Henig, 2000). For example, a pea that inherited two copies of a particular allele from its parents would produce a pea with yellow pods—100% of the time. The causal forces that produce a yellow pod do indeed to appear to be immutable (there’s no indication that this causal series could be interrupted to produce a green pod instead); it has a specific etiology (the distinction between whether the pod is yellow or green is determined solely by the particular alleles that the plant inherits from its parents); all yellow-podded pea plants of this species are homogenous to the extent that they all share the same alleles and are discrete in that they are genetically different from their green-podded neighbors; finally, the yellowness of the pod would appear to be natural—emerging

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as it does from its default state and is not the product of any kind of artificial engineering. So are not the intuitions identified in the GEF just another means for describing the way that genes produce traits in pea plants? We agree that these intuitions are a good-enough description of the causal forces at work in Mendel’s pea plants. However, these pea plants are not at all representative of the ways that genes influence phenotypes more generally. Turkheimer (1998) highlights a continuum that lies between two broad classes of genotype–phenotype relations. On the one extreme of the continuum is what he calls strong genetic explanation. Strong genetic explanation means that a particular biological process has been identified and localized which can explain a large part of the phenotype under question. The link between the dominant allele and yellow pod color is a clear case of strong genetic explanation, as are the variations of the CFTR gene which have been found to deterministically cause cystic fibrosis (Pearson, 2009). For traits that can be characterized as having a strong genetic explanation, the aforementioned genetic essentialist intuitions are reasonably accurate descriptions. However, this direct one-to-one relation between a particular genetic variant and a particular phenotypic character is not at all common. For example, the DTC genomics company, 23andMe, provides information on 60 human traits, ranging from eye color to likelihood of going bald. Only one of these traits—whether or not you have wet or dry earwax—is a Mendelian trait that emerges in this direct one-to-one way (Heine, 2017). Indeed, when students learn about genetics in high school, they may well encounter examples that are described as Mendelian characteristics, such as eye color, or whether or not you can roll your tongue into a tube. Neither of these are Mendelian traits, and the fact that high school curricula rely so much on these inaccurate examples reflects just how rare easily observed monogenic traits actually are. Rather, human traits are more typically characterized by what Chabris, Lee, Cesarini, Benjamin, and Laibson (2015) refer to as “the fourth law of behavioral genetics,” which states that “a typical human behavioral trait is associated with very many genetic variants, each of which accounts for a very small percentage of the behavioral variability” (p. 305). For example, researchers have identified more than 100 common genetic variants that predict schizophrenia, which account for only a fraction of the genetic variability in the condition (Schizophrenic Working Group of the Psychiatric Genomics Consortium, 2014). Likewise, although height is highly heritable, researchers estimate that you would need to consider

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approximately 294,000 common genetic variants to account for half of the variability in human height (Yang et al., 2010). Similarly, despite the relatively strong heritability of IQ, there are no single gene that predict IQ to any appreciable degree (the strongest single predictor is associated with about 0.3 IQ points; Rietveld et al., 2014); instead, approximately 500,000 variants are estimated to be implicated in predicting one half of IQ variability (Davies et al., 2011). These examples all represent what Turkheimer (1998) calls weak genetic explanation. In these cases the phenotype (e.g., whether or not one develops schizophrenia) is associated with a genotype (e.g., risk of schizophrenia is heritable), but not in a direct or deterministic way. Many genes interact, perhaps thousands of them, and each contributes to the likelihood of the phenotype. Phenotypes are influenced by genes only when the genes express proteins, and this expression is dependent on experiences in the environment. Moreover, this expression varies across the organism’s developmental trajectory. Further complicating matters, various epigenetic markers can influence when and how often particular proteins are expressed (for a review, see Cole, 2009). Although genotypes correlate with phenotypes, in cases of weak genetic explanation the correlation remains largely unexplained, and the vast complexity of the interaction of the multiple forces at work may even be in principle unknowable, at least with the current limits of our technologies. Thus, explaining these complex phenomena by referring to the unspecified involvement of genes remains the wrong level of analysis to understand them (see Turkheimer, 1998, for discussion). The error that people commonly make, and what leads us to call people’s genetic essentialist intuitions “biases,” is that people overgeneralize from the straightforward, easy to understand, yet rare examples of strong genetic explanation to explaining the vast majority of other phenotypes which emerge from weak genetic explanation. It is incorrect to assume that traits that are a product of weak genetic influence can be understood better by thinking of them as immutable, natural, of a specific etiology, and forming homogenous and discrete groups.

1.3 Genetic Essentialism Is Widespread and Distorts People’s Understanding Our central thesis is as follows: when people consider that genes are involved in a trait, they come to think differently about that trait. They come to

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conceive of it as though it was caused by an essence, and they consider the trait in ways that are consistent with their essentialist biases. For example, consider how people’s genetic essentialist biases can manifest in the way they think of learning styles. Many people endorse the idea of learning styles, or the belief that different people rely on distinct styles for most efficiently learning new material (e.g., visual learners vs auditory learners; Dunn, Griggs, Olson, Beasley, & Gorman, 1995; Joy & Kolb, 2009). An extension of this idea is the meshing hypothesis, which suggests that optimal learning occurs when an educator’s teaching style matches with a student’s learning style. Despite the lack of robust empirical evidence for such concepts (see Pashler, McDaniel, Rohrer, & Bjork, 2008), many people still endorse them, which can be predicted based on the extent to which people essentialize genes. For example, American MTurk participants were asked to report the extent to which they thought learning styles were genetic vs environmental in origin, along with various perceptions of learning styles. The more the participants perceived learning styles to be genetically caused, the less control they expected one to have over what learning styles one has, the less malleable they thought learning styles were, and the more they thought that learning outcomes are best when learning styles and teaching styles match (i.e., endorsement of the meshing hypothesis). Crucially, mediation analyses revealed that both perceived control and perceived malleability of learning styles significantly mediated the relation between perceived etiology of learning styles and people’s endorsement of the meshing hypothesis (Cheung, White, Sumitani, Truong, & Heine, 2016). Overall, this study provides a simple illustration of the role that genetic essentialist biases may play in our lives. The notion that our essentialist biases influence our thinking when people encounter genetic explanations is not something that we should expect to be limited to a small set of rare or inconsequential traits, because evidence for heritability is extremely broad (e.g., Olson, Vernon, Harris, & Jang, 2001). Heritability refers to the proportion of a trait’s variability within a specific sample that is due to genetics, and it is typically summarized by a heritability coefficient which varies from zero to one. Notably, heritability does not indicate the extent to which a trait is caused by genes in some direct way. Evidence for heritability (i.e., a heritability coefficient that is significantly greater than zero) has been demonstrated for such diverse traits and behaviors as whether one disapproves of student pranks (Martin et al., 1986), watches a

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lot of television (Plomin, Corley, DeFries, & Fulker, 1990), donates blood (Pedersen et al., 2015), gets divorced (McGue & Lykken, 1992), or even gets mugged (Kendler & Karkowski-Shuman, 1997). In fact, evidence for heritability is so broad that Turkheimer (2000) proposed the “first law of behavioral genetics” which states that “all human behavioral traits are heritable” (p. 160). This law is meant to be the null hypothesis, rather than a fixed law, in the sense that heritability evidence is so extensive that we should assume human characteristics is heritable until proven otherwise. An example of an exception to this is that although one’s degree of religiosity is heritable (Bouchard, 2004), the particular religion that one belongs to (e.g., Buddhism, Islam, Catholicism) is not (D’Onofrio, Eaves, Murrelle, Maes, & Spilka, 1999). Of course, if almost everything is heritable, and to a fairly similar degree, then the utility of the very concept of heritability comes into question (Turkheimer, 2000). Nonetheless, given that so much of human behavior is heritable, and that—according to the fourth law of behavioral genetics—this heritability is usually the product of weak genetic influence (Chabris et al., 2015), our genetic essentialist biases can potentially distort how we understand much of the human condition. Our essentialist biases are also at risk for interacting with a new kind of genetic information that is becoming more common. In recent years, dozens of DTC companies have emerged that provide risk information for more than 100 common diseases that are products of weak genetic explanation, such as most cancers, coronary heart disease, or Type II diabetes (Kaufman, Bollinger, Dvoskin, & Scott, 2012). The essentialist bias that people tend to understand genetic causes as emerging from a single etiology is particularly worrisome in the face of this DTC disease-risk information. After reviewing their genotyping results, a customer might conclude, for example, that they have “the Type II diabetes gene,” or “the Parkinson’s gene.” With a tendency to see genetic causes as ultimate causes, people may well find this kind of genetic information to be far more worrisome than the risk estimates actually communicate. Indeed, such faulty interpretations of genotyping results are not rare (e.g., Gordon et al., 2012; Wang, Gonzalez, & Merajver, 2004), and genetic counselors struggle to communicate genetic risk in a way that people can understand it (Austin, 2010; Evans, Blair, Greenhalgh, Hopwood, & Howell, 1994; Smerecnik, Mesters, Verweij, de Vries, & de Vries, 2009). An individual’s genotyping results may be viewed through the same set of essentialist biases that distorts the way people understand genetic causes more generally.

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2. THE IMPACT OF GENETIC ATTRIBUTIONS ON PEOPLE’S PERCEPTIONS The notion that people have genetic essentialist biases presupposes that genetic arguments have undue influence on people’s perceptions. As we discussed earlier, participants responded quite differently to an article arguing for a genetic cause of obesity compared with an article arguing for an experiential cause—it was only the genetic article that impacted people’s eating behavior relative to a control group. This biased reaction to genetic information is not limited to perceptions of obesity—as we’ll show, genetic attributions affect people’s perceptions across a broad array of different domains. Curiously, the domains which have shown biased responses to genetic information cut across topics that tend to be quite politically contentious. Frequently, essentialist thinking is bound up with intolerance, such as racism and sexism. Yet on other topics, essentialist thinking is seemingly paired with increased tolerance, as in the case of gay rights and criminal responsibility. In the following section, we’ll consider the evidence for how genetic attributions affect people’s perspectives on a variety of topics.

2.1 Sex and Gender “It’s a girl!” These three words often signal a delightful end of the strenuous efforts to bring a new person into the world. The beaming, exhausted parents are now armed with an answer to the first question they are likely to encounter upon delivering the great news about the expanding family. Although the appropriate call should have been “it’s a female” denoting the sex of the newborn, given that sex, rather than gender, denotes the biological differences captured by the visible indicators and their underlying chromosomes, research may reveal that the reason for this ongoing error may be our tendency to see gender as the most essentialized social category (e.g., Haslam et al., 2000); that is, gender is viewed as denoting group differences that are the most natural, discrete, and immutable of all social categories (in contrast to categories such as race, ethnicity, sexual orientation, SES) and therefore may be easily swapped with the term sex. However, gender, the term that captures normative perspectives of femininity and masculinity, is highly culture specific. The host of roles, responsibilities, and behavior that capture appropriate ways to be a man or woman substantially differs from one place to another. For example, whereas the majority of people in gender-egalitarian countries view career importance

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and sexual freedom of men and women as equal, the majority of men and women in gender-stratified cultures do not (e.g., Williams & Best, 1990). Such malleability of gender perceptions has led some to completely reject biological explanations for gender differences beyond genitalia (e.g., Fausto-Sterling, 1985); however, a complete rejection of the role of nature in forming gender-typical preferences and behavior has proven to be inadequate when empirically applied to the real world. For example, instances of involuntary sex-reassignment procedures that have been conducted on infants in error (e.g., circumcision complications) have consistently demonstrated that while genitalia can be easily altered, gender development is most likely to follow natal sex rather than the reassigned one (e.g., Diamond & Sigmundson, 1997; Reiner & Gearhart, 2004). Natal sex, however, is not a deterministic predictor of a person’s gender either. Discarding both the chromosomal indicator and socialization pressures, transgender individuals, some not much older than toddlers, adopt alternative gender identities. The willingness of such individuals to incur substantial social costs (e.g., Norton & Herek, 2013; Stotzer, 2009) in the process indicates just how psychologically painful it can be to be assigned a gender identity at odds with how one identifies oneself. As Stotzer (2009) asserted, transgender people’s very existence is repelled by “a society that is unforgiving of any system of gender that is not binary” (p. 170). Such a reaction is not surprising when one considers that their presence pulls the rug from underneath this most essentialized category. Perceiving clear boundaries between men and women—boundaries that transgender people’s existence violates—allows for clearer assignments of gender roles, a priori suppositions about skills and limitations, and other forms of stereotypical thinking. As such, the GEF predicts that the tendency to view ingroup members as more homogeneous, on the one hand, and increasingly distinct from outgroup members, on the other hand, should be exacerbated when genetic attributions are involved; thus, one would expect that endorsing genetic attributions for gender differences are associated with indicators of stereotyping and prejudice. And indeed, the more a person believes that genes determine a host of behavioral and psychological phenomena, the higher they score on an inventory of sexism (Keller, 2005). Moreover, increased endorsement of biological over socialization explanations for gender is associated with greater self-identification with stereotypically gendered traits (Coleman & Hong, 2008), in line with increased perceptions of the homogeneity of one’s own group.

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The correlational nature of the above research limits causal inferences, because such findings may be interpreted as indicative of dispositional elements that give rise to both preferences for genetic explanations and endorsement of stereotypes (e.g., conservatism). Thus, experimental designs are needed to explore whether exposure to genetic attributions affects gender-related attitudes and behaviors. Utilizing this kind of experimental approach, Dar-Nimrod and Heine (2006) examined the effects of offering genetic causal explanations for alleged sex differences in math aptitude on women’s math performance. In two studies, women were provided with either genetic or experiential explanations for a purported superiority in math performance among men; other women were led to believe that there are no such gender differences. Results indicated that women who were exposed to a genetic explanation for the purported gender disparity performed worse compared to those in the experimental or no difference conditions. Women who were given an experiential explanation for the alleged sex difference, on the other hand, did not show diminished performance, performing on par with the women who learned that men and women do not differ in math performance (for similar findings on a different trait, see Moe` & Pazzaglia, 2010).

2.2 Sexual Orientation Gender may be the most essentialized social category, but it is far from being the only one. With a prominent (Western) societal shift from pathologizing and criminalizing same sex attraction toward its reception as an acceptable form of sexual desire, recent decades have seen monumental changes in judgment of nonheterosexual preferences and behavior. That said, painful reminders of rejection of nonheterosexual preferences are still around us. Whether such rejection comes in a form of ongoing criminalization of homosexual behavior as is the case in dozens of countries around the world, or through hateful rhetoric as in the case of the Westboro Baptist Church, or outright violence and carnage as in the murderous rampage at an Orlando gay nightclub in 2016, the prominence of sexual orientation as an essencebearing marker remains. As part of this prominence, much of the discussion (and some suggest the increased acceptance of same sex attraction) revolves around the perceived origin of one’s sexual orientation; whereas some endorse the idea that sexual orientation is the product of personal choice or upbringing, others favor genetic explanations (e.g., Jayaratne et al., 2006).

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Passionate scientific and popular debates rage around the causal question of sexual orientation. Whereas sex is chromosomally indicated, research has not discovered any genes that predict sexual orientation to a meaningful degree. The closest scientists have come to discovering such evidence came about in the early 1990s. Variations in a specific genetic marker (encompassing multiple genes) on the subtelomeric region of the long arm of the sex chromosome, Xq28, were implicated in influencing at least one subtype of male homosexuality (Hamer, Hu, Magnuson, Hu, & Pattatucci, 1993); however, independent replication efforts have provided conflicting results (Ramagopalan, Dyment, Handunnetthi, Rice, & Ebers, 2010; Rice, Anderson, Risch, & Ebers, 1999; Sanders et al., 2014). Moreover, when it comes to identifying specific genetic underpinning for female homosexuality, there is, as of yet, no reputable relevant evidence. Despite the inability of molecular biology to identify a specific gene as a conclusive indicator of sexual orientation, the term “gay gene” has been featured prominently in media coverage, as indicated, for example, by the hundreds of thousands of search results following a simple Google search. Molecular biology aside, a key controversy underlies debates on the causal determinants of sexual orientation: is sexual attraction inborn or does it arises from socialization experiences and/or personal choice? Although a full account of the scientific research and discourse about this question is beyond the scope of this chapter, there is much evidence for a significant heritable component for sexual orientation (e.g., Bailey & Bell, 1993; Bailey, Dunne, & Martin, 2000). Regardless of the evidence for the role of genes in sexual orientation, this chapter is concerned with people’s etiological perceptions and their outcomes. One desirable feature of believing that genes underlie sexual orientation is that this belief is associated with more accepting attitudes toward LGB individuals than is the belief that it is socially determined or chosen (e.g., Haslam & Levy, 2007; Jayaratne et al., 2006). This seems to suggest that adopting genetic explanations for sexual orientation decreases negative attitudes toward LGB individuals. Indeed, some experimental research also supports a causal interpretation of these correlations, indicating that manipulating perceived etiology by emphasizing genetic explanations results in increased support for LGB causes (e.g., Falomir Pichastor & Mugny, 2009, Study 5; Frias-Navarro, Monterde-i-Bort, Pascual-Soler, & Badenes-Ribera, 2015). However, some key moderators for the effect of genetic attributions on LGB support have also been identified, such as college major (Oldham & Kasser, 1999), premanipulation attitudes (Boysen &

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Vogel, 2007), or the nature of one’s opposition to equal rights (FriasNavarro et al., 2015), suggesting that manipulation of etiological attributions does not necessarily lead to increased tolerance. Taken together, growing data indicate that the association between positive attitudes toward LGB individuals/causes and endorsement of genetic attributions for sexual orientation may, at least for some, be an outcome of motivated cognition; that is, ideology may play a substantial role in explaining these data. This suggestion is supported by research which demonstrated that whereas ideological conservatives tend to endorse genetic rather than social explanations for racial and class-related differences (e.g., intelligence, drive, violence) more than liberals, the opposite is true for sexual orientation (Suhay & Jayaratne, 2012). Thus, people may also use etiological explanations for group differences strategically rather than blindly apply broad theories about the role of genes in explaining human behavior. The somewhat mixed pattern of causal effects of genetic attributions on attitudes toward LGB individuals and/or causes may also arise from the tension between different genetic essentialist biases. On the one hand, in line with attribution theory (Weiner, Perry, & Magnusson, 1988) and empirical evidence (e.g., Cheung & Heine, 2015; Dar-Nimrod et al., 2011), the immutability bias reduces perception of culpability; thus among those who view homosexuality as negative, endorsing relevant genetic explanations should lead them to assign less blame and harbor less negative attitudes. Similarly, a natural bias can further lead people to assume that homosexuality is more natural if it has a genetic basis, which would lead to more tolerant attitudes. On the other hand, the discreteness bias increases perceptions of distinctness between a heterosexual individual and a homosexual outgroup member, which is likely to lead to more negative attitudes. Such opposing pulls from the different biases are supported by research that indicated that whereas perceptions of increased immutability were predictive of reduced antigay attitudes, perceptions of discreteness of male homosexuality were a positive predictor of antigay attitudes (Haslam & Levy, 2007). Whereas much research was conducted on the relations between essentialist beliefs and attitudes toward LGB individuals among heterosexual individuals, only recently has research began to examine the nature of such relationships among LGB individuals themselves. Some scholars (e.g., LeVay, 1996) predicted that LGB individuals’ endorsement of genetic explanations for homosexuality will result in positive consequences; however, empirical research delivers a more nuanced picture. Recent studies do suggest that essentialist beliefs about sexual orientation have implications among

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LGB individuals as well. Just as heterosexual individuals show clashing effects among their essentialist biases and attitudes toward homosexuality, the different aspects of essentialism also conflict among LGB individuals (Morandini, Blaszczynski, Costa, & Dar-Nimrod, 2016; Morandini, Blaszczynski, Ross, Costa, & Dar-Nimrod, 2015). For example, those LGB individuals who view sexual orientation as biologically based or immutable tend to experience less sexual orientation uncertainty (which positively predicts well-being). On the other hand, gay men and bisexual women who perceive sexual orientation as existing in discrete typologies report more internalized antigay/bisexual attitudes (Morandini et al., 2016, 2015). Among gay men, this may be because discreteness beliefs sharpen the distinction between straight vs gay identified individuals, leading to increased self-stereotyping and increased feelings of marginalization or otherness. In contrast, bisexual women, who have flexible patterns of sexual attraction and therefore more amorphous sexual attraction boundaries, do not show this effect. There is also evidence that sexual orientation beliefs are connected to an individual’s experience of sexual orientation. Lesbian women who report being exclusively same sex attracted are more likely to view sexual orientation as biologically determined, immutable, and discrete, than lesbian women who are nonexclusively attracted to women (Morandini et al., 2016). As empirical research on this topic is just emerging, it is still unclear whether sexual orientation beliefs are formed by reflecting on the nature of one’s own sexuality or adopting perceived LGB community or broader societal zeitgeists. Regardless, these findings suggest that essentialist beliefs may be utilized to satisfy personalized epistemic needs, as suggested by Cheung, Dar-Nimrod, and Gonsalkorale (2014).

2.3 Health A key impetus underlying the scientific quest to identify genes has been to improve people’s health. An example of this quest can be seen in the sequence of three DNA bases, cytosine–adenine–guanine (CAG), which gets translated into the amino acid glutamine. Located along the short arm of chromosome 4, the wild-type (normal) form of the HTT gene contains between 6 and 35 such CAG repeats. However, among about 50–100 individuals of every million people, the number of these CAG repeats is greater than 40. For those individuals, the protein that the HTT gene produces leads to devastating outcomes; unless these individuals die at an early age, they will develop a ruinous disease, Huntington’s disease, that will

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ravage their cognitive capabilities, personality, and physicality, ultimately leading to their untimely death (e.g., Andrew et al., 1993). The number of CAG repeats does not only foretell whether one will or will not develop Huntington’s, it also predicts, with remarkable precision, the age of onset of visible symptoms and, by extension, longevity (Walker, 2007). The deterministic nature of the effects of the HTT gene shares many features with lay conceptualizations of destiny, but these kinds of fully penetrant monogenic disease-risk variants represent a small minority of humanity’s disease burden. That said, many of the more common causes of death such as heart disease, diabetes, or various forms of cancer are directly influenced by genetics and/or are indirectly affected by genetic influences on relevant health behaviors (such as smoking or caloric intake). The influence of one’s genes on health has been widely acknowledged for decades (e.g., Herzlich, 1973), and evidence of such common attributions also emerges in response to questions about specific conditions/illnesses such as alcoholism (e.g., Keller, 2005), obesity (e.g., Dar-Nimrod, Cheung, et al., 2014), cancer (e.g., de Vries, Mesters, Van de Steeg, & Honing, 2005), or mental illness (e.g., Schomerus et al., 2012), to name a few. Although such genetic associations for common diseases do indeed exist, they can best be understood as exemplars of weak genetic explanation, in that they are the product of many, many genes interacting with each other and with numerous environmental and psychosocial influences. Notwithstanding such complex networks of interacting etiologies, our essentialist biases make us prone to understanding disease risk in more deterministic ways. This raises the question of how people understand health outcomes if they are made aware of relevant genetic underpinnings. Specifically, how do genetic attributions affect the ways that people: (a) form attitudes toward those afflicted with particular conditions; (b) assess risk and personal control; and (c) ultimately behave? Consider the role of genetic attributions in how people make sense of mental illnesses and those afflicted by them. Much evidence supports a substantial heritable component for all common psychopathologies, but actual determinist links between specific genetic variants and mental health conditions are limited to rare syndromes (WHO, 2001). Despite the general lack of substantial genetic risk predictors for mental health outcomes, much evidence suggests that people are affected by considering the role of genes in mental illness. One source of evidence for the ways that people understand genetic causes of mental illnesses can be seen in efforts by various advocacy groups to reduce mental health stigma. Such advocacy groups often

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emphasize genetic and biological causes of common psychopathologies to reinforce their agenda, hoping that offering biological explanations will reduce stigma (e.g., Phelan, Cruz-Rojas, & Reiff, 2002). And indeed, research shows that people are less likely to blame individuals for their unwarranted symptomatic behaviors in the face of biological/genetic explanations (e.g., Phelan, 2005; Phelan et al., 2002). For schizophrenia, the most studied illness in this context, a metaanalysis indicates that biological/genetic explanations reliably predict less stigma and blame (Kvaale, Gottdiener, & Haslam, 2013). However, a closer look at the metaanalysis on the relations between these constructs suggests evidence for the double-edged sword of genetic essentialism. That is, although biological/genetic explanations may reduce blame, they are also associated with a modest increase in the perceived dangerousness of patients and a desire for greater social distance from the sufferers (Kvaale et al., 2013). On the one hand, the immutability bias implies reduced control by the actor and thus reduced blame, and the natural bias suggests that the illness is more acceptable and less stigmatizing. On the other hand, the discreteness bias highlights how the afflicted individuals are fundamentally different from others, making them a target for discrimination. Moreover, the sense of reduced control that comes with the immutability bias suggests that the individual may be unable to prevent themselves from engaging in behaviors that are dangerous to others. In addition, the specific etiology bias also raises the specter that the individual will never be free of their condition, thus leading to more pessimistic prognoses (Kvaale et al., 2013; Lebowitz, Ahn, & Nolen-Hoeksema, 2013). Thus, endorsing genetic explanations for mental illnesses yield decidedly mixed effects for how people view those with psychopathologies (Kvaale et al., 2013; Lebowitz & Ahn, 2014; Phelan et al., 2002). Although psychopathology is the most prominent topic in which perceptions of undesirable health-related symptoms as an outcome of biological/genetic explanations have been studied, similar essentialist tensions have been found in research on prenatal genetic testing (e.g., Blumberg, 1994; Kelly, 2009), smoking (e.g., Dar-Nimrod, Zuckerman, & Duberstein, 2014; Tercyak, Peshkin, Wine, & Walker, 2006), alcoholism (Dar-Nimrod et al., 2013), memory loss (Lineweaver, Bondi, Galasko, & Salmon, 2014), and overeating (Monterosso, Royzman, & Schwartz, 2005). Genetic essentialist biases are implicated in a wide array of health behaviors, but they are not limited to questions of whether a relevant behavior occurs (e.g., abstaining from unhealthy eating, performing well on a memory test); they may also affect which of several competing behavioral options

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is chosen as a goal. If an unhealthy behavior is seen as “genetic,” it is often presumed that biomedical interventions targeting genes would be appropriate to modify the behavior, while lifestyle interventions would not, in line with the specific etiology essentialist bias. Empirical evidence demonstrates that exposure to genetic attributions indeed increases preferences for physiological interventions to mitigate unhealthy behaviors or improve disease outcomes (e.g., Lebowitz & Ahn, 2014; Phelan, Yang, & Cruz-Rojas, 2006). For example, Wright, Weinman, and Marteau (2003) asked smokers to imagine being tested for genetic susceptibility to nicotine dependence. Half of the participants were told to imagine receiving a positive indication for genetic susceptibility and half were told to imagine receiving a negative indication. Respondents in the positive group were more likely to endorse medication as an effective means of smoking cessation than those in the negative group. In addition, smokers in the positive group were more likely to say they believed willpower to be less effective as a method of smoking cessation, demonstrating a potentially damaging effect of the specific etiology bias.

2.4 Race and Ancestry Research on population genetics has provided evidence for the intersection of two rather obvious and widely shared intuitions: (1) people inherit traits (and genes) from their biological parents and (2) people’s mating preferences are not determined randomly. Although there are several factors that guide the way that people choose their mates, one particularly powerful factor rarely gets discussed, perhaps because it seems to go without saying. The propinquity effect refers to the fact that people are far more likely to form relationships with people who are geographically near than with those who are geographically distant (Festinger, Schachter, & Back, 1950), and this also holds true for mating. A key genetic consequence of this effect is that genetic variants tend to cluster geographically. This clustering occurs for two reasons. First, selection pressures can leave adaptive alleles more common in certain regions, such as how an allele of gene SLC24A5, which is associated with lighter skin color, became more common in areas of higher latitude, as it was adaptive for people in those regions to absorb more ultraviolet radiation to catalyze the production of vitamin D (Lamason et al., 2005). Second, random genetic drift will lead some neutral alleles to proliferate in certain areas because those who had more offspring in a particular region will have their own genetic variants become relatively more common in

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subsequent generations. For example, the population of Norfolk Island has a number of genetic risk factors for cardiovascular disease that are more common than average because they descend from a small group of individuals who must have carried the same mutations (Bellis et al., 2005). Natural selection and genetic drift result in the frequencies of different alleles varying significantly around the world. The uneven distribution of alleles across the globe makes it possible to identify the geographic origins of one’s ancestors to a fair degree of precision by examining their genomes. For example, one study investigated a few thousand Europeans and was able to predict the location, within 310 km of precision, of the birthplace of approximately 50% of the participants by only examining the single-nucleotide polymorphisms of their autosomal DNA (Novembre et al., 2008). More pertinent to the theme of this chapter is the question of whether learning about the geographic distribution of genes affects the way that people understand themselves and others. There are numerous anecdotal accounts of the ways that people have experienced changes in their identity as a result of encountering unexpected genomic ancestry information, which has now been provided to over 3 million people through DTC genomics companies (Petrone, 2015). As an example of the effects of genes on identity, consider the case of Csana´d Szegedi; he was elected to the European Parliament as a leading member of the anti-Semitic Jobbik party in 2009. However, upon unexpectedly learning that his maternal grandmother was Jewish, he became an Orthodox Jew in 2013 (Applebaum, 2013). Although the kind of abrupt transformation exhibited by Szegedi is certainly unusual, many people do seem to experience a change in identity upon receiving unexpected information from their genomes. Roth and Lyon (in press) contacted more than 600 individuals who received genomic ancestry information from DTC companies. Among those who responded, only 26.1% reported that the test results had no impact on how they identify with their race or ethnicity, their identity more generally, or their activities or friendships. The vast majority claimed that the tests had an impact on some aspect of their lives. For example, upon receiving their genetic feedback some people began learning an ancestral language, chose new ethnic categories on the census, cheered for new teams in the World Cup, made new groups of friends, joined native tribes, and began to think of their identities differently. It should be noted that the ancestry information provided by DTC companies suffers from overpromising, is often full of errors, and is

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currently not subject to any oversight or regulation (Bolnick et al., 2007; Heine, 2017; Royal et al., 2010). Setting aside the question of how learning about the geographic ancestry of one’s own genes affects an individual, one may also examine how learning about the geographic distribution of genes in general affects people. Reflecting on how genetic variants are distributed unequally across the globe would seem to resonate with the essentialist bias of conceiving of essences as carving nature at its joints. It highlights that humanity does not share a uniform genome, but that people from different regions of the globe have some identifiable genetic differences. Considering the genetic boundaries that partition the world may therefore lead people to conclude that people of different ethnicities have distinctive essences. And because a key component of ethnic prejudice is that it’s founded on the sense that outgroups are of a different essence than ingroups (Allport, 1954; Yzerbyt, Judd, & Corneille, 2004), this raises the possibility that reflecting on the geographic distribution of genes may exacerbate feelings of prejudice. There is considerable evidence for this effect. In a study by Keller (2005), German students were asked to read an essay that described how geographic ancestry can be revealed by one’s genome or a control essay on an unrelated topic. Later, participants were asked questions about expanding the European Union and were asked to indicate their feelings toward people from various Western European countries and Eastern European countries. The results indicated that those who read about the geographic distribution of genes showed a larger ingroup bias in preferring Western Europeans over Eastern Europeans compared with those who read the control essay. Likewise, people who were exposed to arguments that the human population’s genome varied significantly evaluated ingroup and outgroup faces in a more dichotomous way compared with those who read that human genetic variation is minimal (Plaks, Malahy, Sedlins, & Shoda, 2012; see also Kang, Plaks, & Remedios, 2015). Moreover, Kimel, Huesmann, Kunst, and Halperin (2016) compared how American Jewish participants responded to an essay outlining how Jews and Arabs were highly genetically similar in contrast to those who read either an essay arguing that Jews and Arabs were genetically distinct or a control essay. Those who read about the genetic similarities between Jews and Arabs were more in support of peacemaking efforts in the Middle East than those who read either of the other two essays. Given that the human genome is remarkably homogenous in contrast to many other species (Templeton, 2013)—for example, whereas

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genetic variation associated with the continental races accounts for only about 4.3% of human genetic variability (Rosenberg et al., 2002), the different “races” of chimpanzees account for 30.1% of chimpanzee genetic variability (Gonder et al., 2011)—perhaps getting this message out will lead to less ethnic prejudice more generally. The genetic essentialist bias of seeing populations with different genes as being more discrete from each other can also interact with our other essentialist bias of seeing genetic causes as being of a specific etiology. That is, upon seeing phenotypic variation between human populations, people may conclude, erroneously, that genotypic differences between the populations must account for this. For example, this was a key argument of the controversial book, The Bell Curve, in noting that because AfricanAmericans and European-Americans performed differently on some kinds of intellectual tasks, the difference in performance could be explained by imagined genes that distinguished these two populations (Herrnstein & Murray, 1994). Given how this notion resonates so well with our essentialist biases, it’s worth noting that just because genes may account for individual variability in a trait, this says nothing about whether genes underlie between group variability in the same trait (for thoughtful discussion of this issue, see Nisbett, 2009). The degree to which people see population differences, such as those described in The Bell Curve, as having a genetic basis is something that we recently investigated (Schmalor, Cheung, & Heine, 2016). American MTurk participants were randomly assigned to read an essay either about a geographic distribution of genes, a description of human genetic homogeneity, or a control topic. They were presented with a list of ethnic stereotypes that covered a wide range of desirability (e.g., French have a more sophisticated palate; Africans have a better sense of rhythm; Asians are worse drivers). Participants were asked how accurate and how offensive they found the stereotypes, and then they were asked to indicate what percent of the population variability could be attributed to genes or experiences. On average, people viewed approximately 35% of the variability underlying these stereotypes as genetic. However, those who read about the geographic distribution of genes estimated that genes accounted for approximately 5% more of the population variability in comparison with the other two groups. That is, when people considered the argument that people vary in their genes around the world, they viewed genes to be a more significant component underlying ethnic stereotypes compared with those who did not encounter this argument.

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This tendency toward seeing population differences on complex traits as an outcome of underlying genetic variability speaks to the controversy over what entails race. Is race something that is largely socially constructed, or is it grounded in biology? Although it is tempting to see race as biologically grounded, given the visible differences across the continental races, there is a strong consensus among both biologists and social scientists that race is something that is socially constructed rather than biologically based (Bliss, 2012; Boas, 1940; Cavalli-Sforza & Feldman, 2003; Hunley, Healy, & Long, 2009; Lewontin, 1972; Yudell, Roberts, DeSalle, & Tishkoff, 2016). Biologists maintain a set of criteria for identifying whether a population can be considered a subspecies (the nonhuman equivalent of race), and human races do not come close to meeting any of these criteria (Templeton, 2013). Moreover, we can see evidence of the social basis of race when we consider the various ways that race is defined. For example, people of disadvantaged races are often subject to the notion of hypodescent, where those of the lower-ranked ethnic groups are seen to have a contaminating influence. An extreme example of this was the “one drop rule,” which made the case that people would be deemed to be Black if they had any African blood. Evidence for hypodescent can still be found today; if one morphs photos of White and Black faces, people tend to identify the faces as Black even if the percentage of the contribution from the morphed Black photo is considerably less than 50% (Ho, Sidanius, Levin, & Banaji, 2011). Hypodescent reflects that our judgments as to what counts as being a member of the Black race is not proportionately based on genes. Regardless of whether human races really are socially constructed, however, more relevant to this chapter is the question of how people respond when they encounter an argument that race is grounded in one’s biology vs social conventions. Williams and Eberhardt (2008) investigated this question by examining how White American participants would respond to a video of a Black target who was discussing how he had been laid off from his work. Prior to seeing the video, some participants had read an essay highlighting how race was a biological construct, whereas others read that race is a social construct. Those who read about the biological argument for race had more prejudiced attitudes toward a Black target than those who had read the social constructionist argument. Likewise, in another investigation, Asian-Americans who read an essay arguing for a biological account of race were found to identify less with American culture than those who read an essay arguing for a social basis of race (No et al., 2008; also see Chao, Chen, Roisman, & Hong, 2007).

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A perceived biological basis of race makes one’s ethnic identity appear fixed and responsible for characteristics of one’s ethnicity.

2.5 Criminality Academic inquiries into the biological bases of criminal behavior have long existed, most notably involving Lombroso’s (1876/2007) ideas about the physiognomy of criminality and his thesis of the “born criminal.” Although most of Lombroso’s ideas have not survived to the present, the underlying motivation of seeking a biological explanation for criminality persists. This is apparent in the appeal of the XYY chromosome defense that appeared in the 1960s, whereby people believed that having an extra Y-chromosome predisposed men to have lower intelligence and greater proneness toward violence, despite a striking lack of evidence (Slabbert, 2006). More recently, and more germane to the central theme of this chapter, results from the Human Genome Project and advances in behavioral genetics have tantalized the public and researchers with the possibility that criminal behavior may be distilled down to one’s genetic makeup (Friedland, 1998). Whereas criminality is most certainly a product of a complex interplay of both genetic and nongenetic factors (Alper, 1998), the scientific community has made great strides toward identifying various genetic markers that correlate with criminal or aggressive behavior (for a summary, see Beaver, Schwartz, & Gajos, 2015). The fact that researchers are discovering genetic components of criminal behavior is helpful for our understanding of human behavior and for the accumulation of scientific knowledge. However, an important issue that these findings pose for the legal community is how members of the criminal justice system, both laypeople and experts, interpret and make use of such findings. One important tenet of the criminal justice system is that it assumes that people act out of free will, and that criminal deeds are the product of one’s willful intent to act in contravention to the law. This is encapsulated in the core concept of mens rea, or “guilty mind,” referring to one’s mental state and volitional control during the commission of the crime (Aharoni, Funk, Sinnott-Armstrong, & Gazzaniga, 2008). The immutability bias, however, undermines this inference, which may engender the perception that “criminal genes” inevitably lead to criminal behavior, ultimately prompting mitigated judicial outcomes. The recognition of such biases has led to an ongoing debate among legal scholars regarding the place that genetic evidence has within the courtroom, particularly given that individual genes can

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generally only account for a very small proportion of the variability for a given phenotype (Baum, 2013; Berryessa & Cho, 2013; Chabris et al., 2015). Emerging evidence over the last decade has shown that genetic explanations of criminality lead members of the criminal justice system to think in ways consistent with genetic essentialist biases, in contrast to nongenetic, particularly environmental, explanations of criminality. In terms of impacting perceptions of a criminal, genetic explanations lead people to attribute less behavioral control to the perpetrator and less willful intention in terms of committing various crimes, relative to environmental explanations, reflecting the genetic essentialist bias of immutability (Cheung & Heine, 2015; Dar-Nimrod et al., 2011). Furthermore, people feel that such explanations of criminality are more persuasive and acceptable as excuses in criminal cases, compared to more experiential explanations such as exposure to violence or parental abuse (Heath, Stone, Darley, & Grannemann, 2003), leading them to perceive lower levels of overall criminal culpability (see also Monterosso et al., 2005). As a result, genetic explanations of criminal behavior lead people to be more accepting of insanity and diminished capacity defenses and to prescribe shorter prison sentences (Cheung & Heine, 2015), in accordance with the fact that such explanations appear to mitigate many concepts that are relevant for mens rea, including perceived control and intention (Dar-Nimrod et al., 2011). This finding generalizes even to state trial judges and Superior Court Judges (Aspinwall, Brown, & Tabery, 2012; Berryessa, 2016). As noted in previous sections, the same genetic essentialist biases that underlie the mitigating perceptions resulting from genetic explanations for criminal behavior may also lead to aggravating perceptions. For instance, expecting a deterministic relation between genetic causes and criminal behavior leads people to engage in more stable causal attributions and expect a greater likelihood of recidivism (Cheung & Heine, 2015). This creates an enhanced perception that the criminal is dangerous, engendering greater levels of fear toward the criminal (Appelbaum & Scurich, 2014). Importantly, both the mitigating and aggravating perceptions of the criminal factor into people’s prescribed prison sentences—in opposite directions. In other words, mitigating perceptions (e.g., less ascriptions of behavioral control) predict shorter prison sentences, whereas aggravating perceptions (e.g., greater expectations of recidivism) predict lengthier prison sentences (Cheung & Heine, 2015). These responses suggest that people’s objectives for punishment may be influenced by the kinds of explanations that they

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encounter. Genetic explanations trigger people’s concerns about incapacitation and protection of the public as an objective for punishment, rather than deterrence, rehabilitation, or just deserts (Carlsmith, Darley, & Robinson, 2002). Overall, the extant data suggest that people have a doubleedged perception of genetic causes for criminality, similar to their effects on evaluations of individuals with a psychopathology, leading to both mitigating and aggravating perceptions.

2.6 Political Orientation From its inception, political psychology has focused on individual differences associated with variations in political affiliation. In the wake of WWII, these efforts took on greater urgency as psychologists turned to the emerging field of personality research to determine what, if any, core psychological mechanisms underlie adherence to fascist ideology, and extreme right-wing beliefs more generally. The first of these “political personality” constructs was outlined in The Authoritarian Personality (Adorno, Frenkel-Brunswik, Levinson, & Nevitt Sanford, 1950), which featured proposed dimensions of right-wing ideology that would be reified over the course of the century: conventionalism and antiintellectualism, which represent resistance to social change, and submission to authority, with a corresponding preference for social hierarchy (also see Right-Wing Authoritarianism, Altemeyer, 1981; and Social Dominance Orientation, Pratto, Sidanius, Stallworth, & Malle, 1994). Many psychologists have intuited that fundamental biological differences undergird these personality constructs, which in turn are influenced by genetic factors (e.g., “Politics might not be in our souls, but it probably is in our DNA”; Hibbing, Smith, & Alford, 2014, p. 298). Initial evidence for this genetic influence came from twin studies suggesting a moderate degree of heritability (Martin et al., 1986; Olson et al., 2001). Using more powerful statistical techniques, subsequent research has supported the contention that social value adherence is partly heritable, along with the degree to which these values are maintained, even if the same cannot be said for specific political party affiliation (Alford, Funk, & Hibbing, 2005; Hatemi, Alford, Hibbing, Martin, & Eaves, 2008). In recent years, these apparent links have spurred efforts to specify shared genetic markers underlying a possible conservative phenotype (Deppe, Stoltenberg, Smith, & Hibbing, 2013; Fowler & Dawes, 2008). In particular, these efforts have focused on the potential genetic underpinnings of a purported “negativity bias” demonstrated by ideological conservatives

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(e.g., Hibbing et al., 2014, p. 22; but note a critical review of this argument, Charney & English, 2012). From this general perspective, political conservatism is itself seen as a genetically evolved response to threatening or uncertainty arousing stimuli (e.g., Jost, Glaser, Kruglanski, & Sulloway, 2003), which plays a role in both dimensions of conservatism as it is classically defined. First, a resistance to social change—and change, more generally— has been claimed to be the product of a relatively diminished neural response to error feedback that would motivate behavioral change (Jost & Amodio, 2012) caused by relatively decreased brain mass in areas associated with cognitive conflict detection (e.g., anterior cingulate cortex, Kanai, Feilden, Firth, & Rees, 2011). Second, a preference for social hierarchy has been argued to follow from the relative proclivity of conservatives to attend more (e.g., Dodd et al., 2012) and respond more strongly (Oxley et al., 2008) to aversive stimuli, and this bias has been implicated in the heightened derogation of perceived social outgroups (e.g., heightened disgust and attitudes toward gay men; Inbar, Pizarro, & Bloom, 2009). The reporting of this research has been widespread, with a particular emphasis on public dissemination of findings that suggest a genetically determined biological origin to political affiliation (e.g., Hibbing, Smith, & Alford, 2013). As we have discussed, attributing genetic origins to criminality (e.g., Cheung & Heine, 2015) and sexual orientation (e.g., HaiderMarkel & Joslyn, 2008) may indeed increase tolerance for behaviors that are otherwise stigmatized, as these behaviors are seen as immutable and natural. Similarly, Hibbing et al. (2014) emphasize that the genetic basis of political differences should lead to increased understanding and acceptance. However, recall the tension we have identified in previous sections between the immutability and naturalness biases on the one hand, and the discreteness bias on the other hand. This latter bias suggests that the manner in which genetic attributions are portrayed in the “political genetics” literature could facilitate intolerance of political outgroups by highlighting how those with conflicting political views are ultimately made up of different underlying essences. Political affiliations are often portrayed in a manner that emphasizes and exaggerates the extent to which they are bounded, immutable, genetically determined categories. “Conservatives” and “liberals” are described with discrete nouns, and as manifestations of homogenous natural kinds that ultimately originate “in our DNA” (Hibbing et al., 2014, p. 298). In reality, however, ideological commitments vary continuously across whatever dimensions they happen to be operationalized by, and are, at

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best, marginally related to contemporary measures of genetic influence (Charney & English, 2012; Hibbing et al., 2013). Regardless, the commonly misapplied genetic attributions may motivate individuals to perceive those who predominantly identify with an opposing ideology in a manner that would discourage efforts to seek common ground, as they are understood to possess a fundamentally divergent essence. Similarly, efforts toward meaningful engagement with perceived political outgroups may seem pointless to the extent that ideologies are understood as immutable natural categories that resist social influence as they spring from a specific etiology. Relative to liberals, genetic attributions may especially decrease tolerance toward conservatives, as the natural origin of conservatism is sometimes depicted as an atavistic response to disgust and fear (e.g., Jost et al., 2003), shaped by diminished brain structures associated with higher cognitive function, and exaggerated brain structures associated with threat detection (e.g., Kanai et al., 2011). In this depiction, the genetic pathways underlying conservatism were selected for during the Pleistocene epoch (Pinker, 2012), when primitive man faced constant mortal threat, in contrast to the present day. As such, conservative genetic markers may be regarded as an evolutionary dead end, insofar as “strong negativity biases were once selected for but now are not” (Hibbing et al., 2014, p. 32)—a depiction that resonates with eugenic beliefs, as we discuss in the next section. Despite the prevalence of this depiction in popular discourse (e.g., Mooney, 2012), there is relatively little research that assesses the extent to which genetic attributions for political affiliations decrease ideological tolerance or increase political discourse (Hibbing et al., 2014). In one recent study (Suhay, Brandt, & Proulx, in press), measures of genetic attributions for ideology (e.g., “A person’s political beliefs are determined by their genetics”) were inversely correlated to items that assessed tolerance of those with divergent political identities (e.g., “I often spend time with people who have political beliefs different from my own”) among both liberals and conservatives. Moreover, this heightened ideological discrimination was especially prevalent among political liberals, which may be due to the explicitly negative portrayal of conservative genetics. These perceptions may play a role in the generally negative (Duarte et al., 2015) and discriminatory (Inbar & Lammers, 2012) attitudes toward conservatives among ideologically liberal social scientists.

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2.7 Essences and Eugenics Considering a genetic foundation for human traits does not just affect how we think about those specific traits. It is also implicated in broader efforts to change those traits. Here we consider what is arguably the most pronounced cost of our genetic essentialist leanings—it can be associated with support for eugenic social policies. While eugenics may be associated primarily with the horrors of Nazi Germany and the holocaust, this social philosophy had much broader support prior to WWII. Indeed, discussions in favor of eugenics were commonplace throughout the industrialized world in the early 20th century, as the logic that governed the breeding choices made by livestock breeders began to be applied to humans (for a review, see Kevles, 1995; Paul, 1995). Some efforts were termed positive eugenics, in the sense that those who were seen as possessing good genes were encouraged to spread their bounty to the next generation. For example, “Fitter family” contests were held in state fairs in the United States, where medals were given to those designated as “Grade A Individuals,” alongside the other prize-winning livestock (Kevles, 1992). But these efforts were soon overtaken by much wider scale programs of negative eugenics, where the goal was to prevent the unfit from breeding. In the United States, the popular descriptor for the unfit was “feeble-minded,” a catch-all term that included any kind of perceived defect in intelligence or moral character. Negative eugenics was championed by a variety of progressive organizations, such as the Sierra Club and Planned Parenthood, as both sought the goal of reducing the world’s population by preventing births among the unfit (Paul, 1995; Stern, 2005). The eugenics movement cut across the political spectrum and was championed both by those on the right, who sought to increase the relative proportion of their own kind, and by the left, who viewed it as a necessary pillar for the establishment of a social welfare state (Spektorowski & Ireni-Saban, 2013). Mandatory sterilization was legalized in 1927 in the United States, which led to the forceful sterilization of more than 60,000 Americans, disproportionately minorities and women (Stern, 2005). Similar mandatory sterilization programs emerged across the industrialized world, in such diverse places as Canada, Sweden, Japan, and much of Latin America (Broberg & Roll-Hansen, 1996; Robertson, 2001; Stepan, 1991). These efforts rendered the decision of who would have children to rest ultimately with the state. Support for eugenics was embraced by leading Western politicians (e.g., Winston Churchill, Tommy Douglas, Teddy Roosevelt) and public

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intellectuals at the time (e.g., Alexander Graham Bell, W. E. Dubois, George Bernard Shaw, H. G. Wells; Kevles, 1995; Paul, 1995). But two disciplines stand out in their support for eugenics: first, psychology played an outsized role in the movement (which has received relatively little attention within the field), as the metric of genetic quality that was most widely targeted was IQ, and many psychologists were active in promoting eugenic policies to improve national IQ, including Carl Brigham, James McKeen Cattell, Robert Fisher, G. Stanley Hall, Karl Pearson, Charles Spearman, Lewis Terman, Edward Thorndike, and Robert Yerkes (Heine, 2017). Second, and most pertinent to the logic of genetic essentialism, was that eugenic support was widespread at the time among geneticists. Prior to the war, there was scant light that separated the fledgling field of human genetics from eugenics—indeed, the latter was often thought of as applied genetics (Paul, 1995). Evidence for this link can be seen from a variety of sources. For example, the founder of behavioral genetics, Francis Galton, was also the father of modern eugenics (Galton, 1875, 1883). Likewise, in 1916, every member of the founding editorial board of the journal, Genetics, endorsed the eugenics movement (Ludmerer, 1972). The link between the two fields is also evident in that half of academic biologists in Germany joined the Nazi party prior to the war, which was the largest representation of any professional group (Paul, 1995). One reason that there was such a link between the study of genetics and eugenics in the early 20th century was that many early geneticists favored simple Mendelian accounts of human traits. For example, Charles B. Davenport, the leading American eugenicist at this time, maintained that many human traits, included feeble mindedness, a love for the sea, nomadism, shiftlessness, and innate eroticism were the product of single genes (Comfort, 2012; Kevles, 1995). To the extent that human traits could be viewed as simple and direct consequences of single genes, it is far more straightforward to imagine efforts to change the frequency of desired traits through controlled breeding. We have investigated whether similarly deterministic perceptions of genetic causes predict support for eugenic ideas today. We created a scale to measure support for eugenic policies (Heine, Cheung, & Ream, 2015). It includes items such as “There should be laws discouraging people with low intelligence from having biological children” and “Anyone convicted of a violent crime should be sterilized as part of their punishment.” We correlated this scale with the Belief in Genetic Determinism Scale (Keller, 2005) and the GETS (Dar-Nimrod et al., 2016). Support for

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eugenic policies correlated moderately positively with these measures of genetic determinism/essentialism (Heine et al., 2015). Moreover, the link between eugenics and simple accounts of genetic understanding was further demonstrated in that we found negative correlations between eugenics support and knowledge of genetics (operationalized either in terms of the number of genetics classes taken, or in terms of performance on a genetics knowledge test; Ream, Cheung, & Heine, 2016). These findings reveal that those who are more likely to understand the irreducibly complex ways that genotypes get translated into phenotypes are less in support of efforts by the state to improve the collective genome through strategic breeding.

2.8 Genetic Engineering Studying the predictors of support for eugenics may seem like an outdated question, akin, perhaps, to investigating present day support for slavery. But with the rapid advent of several new genetic technologies, the idea of improving the genes of current and future generations has reemerged as a topic of public discourse (e.g., Hudson & Scott, 2002; Winkelman, Missmer, Myers, & Ginsburg, 2015). However, in contrast to the early 20th century when the state was petitioned to lead efforts to enhance a nation’s collective genome, this time eugenics is emerging through the backdoor (Duster, 2003). There are presently a variety of genetic technologies that are available for parents to make their own reproductive decisions which can potentially shape posterity (see Heine, 2017, for a review). Some of these technologies allow for the genes of fetuses to be genotyped (e.g., amniocentesis, chorionic callus sampling, cell-free fetal DNA screening), and thereby providing the parents with information about potential congenital disorders which may lead them to decide to terminate a pregnancy. Another alternative is to genotype a series of fertilized embryos using preimplantation genetic diagnosis, and then select to implant those in the womb that are not carriers of particular alleles associated with genetic diseases, or even to select the sex of one’s baby. Parents can also select from sperm and egg donors, while perusing remarkably detailed catalogs describing the donors’ phenotypes. If this was done on a large scale, it could shape the collective genome because people seek different traits when selecting gamete donors than they do when selecting romantic partners—when people select donors, they place relatively greater weight on their health, attractiveness, height, and various abilities, whereas when choosing romantic partners, people place relatively greater weight on character (Scheib, 1994;

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Scheib, Kristiansen, & Wara, 1997). Moreover, there is much fear over novel technologies that have not yet been used to create a human, such as cloning, or creating designer babies using genome editing by way of CRISPR/Cas9. In contrast to the negative relation between genetic knowledge and support for eugenic policies, there is a weak positive relation between genetic knowledge and support for new reproductive technologies to enhance the genomes of future children (Ream et al., 2016). But overall, these technologies strike many as deeply problematic, with a commonly expressed concern that these entail people playing God (Calnan, Montaner, & Horne, 2005; Condit, 2010; Winkelman et al., 2015). Moreover, aside from screening fetuses for congenital disorders, the other reproductive technologies are not yet commonly practiced. Another aspect of genetic engineering is far more common in our lives. GMOs now play a substantial role in our diets. Approximately 80% of American processed food contains at least some GMOs (Lemaux, 2008). Yet, despite the pervasiveness of this technology, it remains bothersome to many: approximately three quarters of Americans are concerned with having GMOs in the food supply (Harmon, 2014), and approximately 70% of GMO opponents view GMOs as absolute moral violations (Scott, Inbar, & Rozin, 2016)—that is, they are opposed to them regardless of any documented benefits or harms that they might entail. GMOs seem bothersome because they violate the essentialist bias of genes as natural. A frequent criticism is that GMOs are abominations, and that they represent people playing God (Condit, 2010). Likewise, GMOs run afoul of our essentialist biases as genes carving nature at its joints. For example, people are more bothered by GMOs that involve transgenic modifications (i.e., introducing genes from unrelated species, such as a tomato receiving a gene from a fish) than they are of those involving cisgenic modifications (i.e., introducing genes from a related species, such as an orange receiving a gene from a lemon; Gaskell et al., 2010). In general, people have a rather poor understanding of what GMOs entail; only 57% of Americans and 36% of Europeans correctly recognized that non-GMO food products also contain genes (Hallman, Hebden, Aquino, Cuite, & Lang, 2003), and only 42% of Americans were aware that the addition of a fish gene would not necessarily make a tomato taste fishy (Hallman, Hebden, Cuite, Aquino, & Lang, 2004). Opposition to GMOs is associated with a lack of genetic literacy; there is a weak positive correlation between performance on a test of genetic knowledge and support for GMOs (Ream et al., 2016). Such an association seems to be telling when one

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considers that, in stark contrast to the only 36% of Americans who support GMOs, 88% of American scientists are in favor of the technology (Pew Research Center, 2015). It appears that much of the opposition toward GMOs is not based on a weighing of costs and benefits, but is ultimately grounded in people being disturbed about the essences of their food being tampered with (see also Scott et al., 2016).

3. PERNICIOUSNESS OF GENETIC ESSENTIALISM The previous sections of this chapter have shown that genetic essentialist biases are based on an overly simplistic understanding of genetic information, combined with a human tendency to imagine underlying essences, leading people to arrive at unwarranted conclusions, and to ascribe undue inferential power to these genes. Given that many consequences of our genetic essentialist biases are decidedly negative, it is important that efforts go into reducing these biases. These efforts may be short-term or long-term, with most attempts falling into the latter category. As this section will demonstrate, existing attempts generally try to address people’s overly simplistic understanding of genes, yet these largely reveal the imperviousness of these biases.

3.1 Short-Term Efforts to Reduce Genetic Essentialism Immediate efforts to eliminate genetic essentialist biases generally involve trying to get people to understand the complex relation between genes and their associated outcomes, with varying levels of success. Two ways of accomplishing this are to emphasize the epistatic and polygenic nature of most genetic causes, or to manipulate people’s perception of the strength of a genetic effect. For example, Cheung (2016) sought to contrast how people would respond in their punitive judgments of someone convicted of murder depending on how the genetic risk information was presented. One study contrasted a monogenic cause vs a polygenic cause underlying violence. Another study varied the magnitude of the predictive strength of the putative violence-causing gene. Neither of these efforts significantly affected people’s judgments about the applicability of a defense of diminished responsibility. In all of these cases, people in the genetic risk conditions assigned less responsibility to the accused compared with those who instead read of an experiential cause of his crime. Another method of eliminating people’s specific etiological bias is to highlight the role of external and nongenetic factors in an effort to

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complicate the genetic causal story. The simplest method of accomplishing this is to discuss genetic causes in the context of interactions with the environment, downplaying the solitary role of genes. Current evidence is mixed on the effectiveness of this method, made more complicated by the fact that very little work has examined the impact of such causal interactions on people’s perceptions. For instance, one study found that those who read an account that schizophrenia was the product of both biology and environment had significantly reduced perceptions of danger compared with those who had learned only of a genetic account of schizophrenia (Walker & Read, 2002). Extending this research to address mental health professionals, Lebowitz and Ahn (2014) exposed clinicians to nuanced etiological accounts for depression (i.e., accounts containing both biogenic and psychosocial explanations) with varied emphases, yielding a condition in which the clinicians read a predominantly biological/genetic account and a condition in which they read a predominantly psychosocial account. They found that compared with clinicians in the predominantly psychosocial condition, clinicians in the predominantly biological/genetic condition assessed medication as more effective and psychotherapy as less effective. However, other research has found that people’s stigma toward mental illness did not differ significantly between thinking about mental illness as being due to biological causes vs a gene-by-environment interaction (Boysen & Gabreski, 2012; also see Deacon & Baird, 2009). Similarly, a study on perceptions of violence found that a genetic account of violence elicited an equivalent response from participants as a gene-by-environment account of violence; both conditions resulted in people judging a perpetrator to be less responsible for his crimes, compared with those who learned of an environmental account of violence (Cheung, 2016; also see Lippa & Sanderson, 2012 for similar findings). In sum, causal accounts that point to gene-by-environment interactions are not necessarily perceived much differently than purely genetic accounts. Although it remains possible that there may be ways of making the environmental component of the geneby-environment interactions more salient to participants (as did Lebowitz & Ahn, 2014), and that this might then weaken people’s essentialist biases, the evidence in support of that approach is limited at this time. Optimistically, there is some evidence that certain interventions can encourage behaviors that are reflective of weaker genetic essentialist biases. One such relatively successful attempt strongly emphasizes the important role that external procedures (e.g., applying sunscreen) can play in dampening or controlling the likelihood that underlying genetic risks will be

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expressed (e.g., genes for melanoma), which can empower people to engage in more preventative measures in reducing their risks of developing melanoma (Taber & Aspinwall, 2015). This success, though, still capitalizes on one’s genetic essentialist biases rather than eliminating those biases, because the external procedures mechanically prevent the genes from expressing themselves while likely leaving much of the underlying biases intact. Another effective method has utilized weight gain prevention intervention programs that focus solely on obesogenic behaviors rather than addressing the potential ways that genetic causes underlie obesity (McVay, Steinberg, Askew, Kaphingst, & Bennett, 2015). One particularly impressive part of the success of this intervention is in its ability to decrease the extent to which people make genetic attributions for weight loss. Despite these successes, a perusal of the nature of these programs underscores the difficult prospects of eliminating genetic essentialist biases: both programs rely on a disproportionately strong emphasis on the importance of nongenetic factors in order to overcome people’s genetic essentialist biases, speaking to the strength and pervasiveness of these biases.

3.2 Long-Term Efforts There are no experimental data to speak to the efficacy of long-term efforts on reducing genetic essentialist biases. The most promising evidence comes from work demonstrating that higher educational levels predict less prejudice, which is associated with having a less essentialized perception of racial categories (Jayaratne et al., 2006). These results are echoed by the finding that people who have taken more genetic courses (or who perform better on a test of genetic knowledge) tend to have weaker genetic essentialist biases (Dar-Nimrod & Godwin, 2016; Ream et al., 2016). Indeed, a largescale international comparison of primary and secondary school teachers found that greater levels of biological training are associated with a weaker tendency to appeal to genetic essentialism and innatism in understanding group differences (Castera & Clement, 2014). In particular, genetics training that emphasizes the interactive role of genes and experiences is associated with a less deterministic understanding of genetics, compared with a standard Mendelian curriculum (Radick, 2016). Collectively, these results suggest that an effective long-term strategy for stemming genetic essentialist biases is to improve and increase the public’s education in terms of factual genetic information, which many researchers have pushed for (e.g., Burley & Harris, 1999; Castera & Clement, 2014; Marks, 2009).

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In sum, the general pattern of results from existing data suggests that although there might be short-term avenues for reducing genetic essentialist biases, they generally require that genetic causes be trivialized as much as possible. The mere inclusion of genetic causes, even as only a part of a more sophisticated set of mechanisms that lead to a certain outcome, is often sufficient to trigger people’s genetic essentialist biases. One direction that is worth exploring is the more long-term option of ensuring that people are better educated on genetics so they have a more accurate understanding of the relation between genes and associated outcomes.

4. CONCLUSION The genomic revolution has arrived, and people are encountering information about their own genomes and about scientific research on genetics more than ever before. Our review suggests that people’s robust tendency for psychological essentialism makes them prone to conceive of imagined essences underlying the natural world. And because popular conceptions of genes make them such an effective placeholder for essences (Medin & Ortony, 1989), we propose that when people encounter these genetic attributions, they tend to understand them in ways similar to how they understand essences; that is, genetic causes appear immutable, of a specific etiology, and natural, and groups that possess them appear more homogeneous and discrete. These genetic essentialist biases are irrational responses for understanding complex human traits, and they have some potent costs, as they are associated with increased racism, sexism, fears about people with mental illnesses, deterministic and pessimistic thoughts about disease prognoses, fears of recidivism among criminals, unwarranted worries about GMO food products, and sympathy for eugenics, among others. On the other hand, these same biases are associated with more tolerance and sympathy for gay rights, people with mental illnesses, criminals, and the potential for international peace. Regardless of the valence of the outcomes, these biases represent incorrect ways of understanding how genes underlie complex human traits. There are several key questions about genetic essentialism that remain largely unexplored. First, the majority of the findings that we discuss come from WEIRD (Western, educated, industrialized, rich, and democratic) samples; do these effects generalize across different contexts? On the one hand, evidence for psychological essentialism has been found in every culture in which it has been explored thus far in published studies (see Gelman, 2003;

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Henrich, Heine, & Norenzayan, 2010), and this leads us to expect that the effects obtained here would be broadly found across other contexts. On the other hand, some constructs that would seem to relate to essentialism are more pronounced in some cultures than in others. For example, Westerners are more likely to make dispositional attributions over situational attributions (e.g., Choi et al., 1999) and are more likely to embrace entity theories of abilities (e.g., Heine et al., 2001), in comparison with East Asians. These differences would suggest that the kinds of effects described in this chapter would be weaker in East Asian societies. For example, one study found that Chinese were less likely than Canadians to incorporate biological information about a target in their behavioral predictions for that target (Lee, 2009). Another possibility is that more collectivistic cultures might be more prone to view essences underlying collectives (e.g., a Japanese genome), as opposed to essences underlying individual differences. Clearly, much research is in order to address the cultural boundaries of the effects reported here. A second key question that emerges is how broad and enduring are the essentialist responses that we have documented? In terms of the breadth of these responses, do people only essentialize specific genetic attributions, such as the notion about genes underlying criminality, or does encountering the general concept of genes make people essentialize all possible domains (e.g., personality, disease, intelligence)? Likewise, if people learn that genes underlie a specific domain, such as criminality, are they more likely to see genes underlying other specific unrelated domains, such as obesity? These questions have not yet been addressed in the published literature. Furthermore, it remains unclear how long people will show essentialist responses to a genetic prime. All of the experimental studies that we reviewed documented effects that were measured in a time span of a few minutes. Could encountering genetic arguments have more enduring consequences? Third, is it possible to reduce the magnitude of these kinds of genetic essentialist effects? Although our initial efforts to reduce these effects have not been especially encouraging (see Cheung, 2016), it seems reasonable to expect that there are likely ways of presenting genetic information such that it provokes a less essentialist response. For example, the evidence that genetics education is associated with weaker essentialist responses suggests that a richer understanding of how genes actually operate leads to less of a tendency to equate genes with essences. There may be other ways to reduce essentialist biases. For example, perhaps becoming aware that one possesses essentialist biases might itself reduce the impact of these effects.

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If recent history can be a guide for how the next few decades will unfold, it suggests that people will be increasingly encountering genetic attributions. Given that people’s essentialist biases seem to provide people with a fundamental misunderstanding of genetic causes, these encounters may well lead people to draw a variety of mistaken conclusions. In the face of this flow of new genetic discoveries and personalized genetic information, it is important to help people come to understand genetic causes better, so they can make more informed decisions about their own lives.

REFERENCES Adorno, T. W., Frenkel-Brunswik, E., Levinson, D. J., & Nevitt Sanford, R. (1950). The authoritarian personality. New York: Harpers. Aharoni, E., Funk, C., Sinnott-Armstrong, W., & Gazzaniga, M. (2008). Can neurological evidence help courts assess criminal responsibility? Lessons from law and neuroscience. Annals of the New York Academy of Sciences, 1124, 145–160. Alford, J. R., Funk, C. L., & Hibbing, J. R. (2005). Are political orientations genetically transmitted? American Political Science Review, 99, 153–167. Allport, G. (1954). The nature of prejudice. Reading, MA: Addison-Wesley. Alper, J. S. (1998). Genes, free will, and criminal responsibility. Social Science & Medicine, 46(12), 1599–1611. http://dx.doi.org/10.1016/S0277-9536(97)10136-8. Altemeyer, B. (1981). Right-wing authoritarianism. Winnipeg, MB: University of Manitoba Press. Andrew, S. E., Goldberg, Y. P., Kremer, B., Telenius, H., Theilmann, J., Adam, S., et al. (1993). The relationship between trinucleotide (CAG) repeat length and clinical features of Huntington’s disease. Nature Genetics, 4(4), 398–403. Appelbaum, P. S., & Scurich, N. (2014). Impact of behavioral genetic evidence on the adjudication of criminal behavior. Journal of the American Academy of Psychiatry and the Law, 42(1), 91–100. Applebaum, A. (2013). Anti-Semite and Jew. The New Yorker. Nov. 11. http://www. newyorker.com/magazine/2013/11/11/anti-semite-and-jew. Aspinwall, L. G., Brown, T. R., & Tabery, J. (2012). The double-edged sword: Does biomechanism increase or decrease judges’ sentencing of psychopaths? Science, 337(6096), 846–849. Astuti, R. (2001). Are we all natural dualists? A cognitive developmental approach. Journal of the Royal Anthropological Institute, 7(3), 429–447. Atran, S. (1998). Folk biology and the anthropology of science: Cognitive universals and cultural particulars. Behavioral and Brain Sciences, 21, 547–569. Austin, J. C. (2010). Re-conceptualizing risk in genetic counseling: Implications for clinical practice. Journal of Genetic Counseling, 19, 228–234. Bailey, J. M., & Bell, A. P. (1993). Familiarity of female and male homosexuality. Behavior Genetics, 23, 313–322. Bailey, J. M., Dunne, M. P., & Martin, N. G. (2000). Genetic and environmental influences on sexual orientation and its correlates in an Australian twin sample. Journal of Personality and Social Psychology, 78(3), 524. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. Baum, M. L. (2013). The monoamine oxidase A (MAOA) genetic predisposition to impulsive violence: Is it relevant to criminal trials? Neuroethics, 6(2), 287–306. http://dx.doi. org/10.1007/s12152-011-9108-6.

Genetic Essentialism

181

Beauchamp, M. R., Rhodes, R. E., Kreutzer, C., & Rupert, J. L. (2011). Experiential versus genetic accounts of inactivity: Implications for inactive individuals’ self-efficacy beliefs and intentions to exercise. Behavioral Medicine, 37(1), 8–14. Beaver, K. M., Schwartz, J. A., & Gajos, J. M. (2015). A review of the genetic and gene-environment interplay contributors to antisocial phenotypes. In J. Morizot & L. Kazemian (Eds.), The development of criminal and antisocial behavior: Theory, research, and practical applications (pp. 109–122). New York: Springer International Publishing. Bellis, C., Hughes, R. M., Begley, K. N., Quinlan, S., Lea, R. A., Heath, S. C., et al. (2005). Phenotypical characterisation of the isolated Norfolk Island population focusing on epidemiological indicators of cardiovascular disease. Human Heredity, 60, 211–219. Berryessa, C. M. (2016). Judges’ views on evidence of genetic contributions to mental disorders in court. The Journal of Forensic Psychiatry & Psychology, 27(4), 586–600. http://dx. doi.org/10.1080/14789949.2016.1173718. Berryessa, C. M., & Cho, M. K. (2013). Ethical, legal, social, and policy implications of behavioral genetics. Annual Review of Genomics and Human Genetics, 14, 515–534. http://dx.doi.org/10.1146/annurev-genom-090711-163743. Blancke, S., Van Breusegem, F., De Jaeger, G., Braeckman, J., & Van Montagu, M. (2015). Fatal attraction: The intuitive appeal of GMO opposition. Trends in Plant Science, 20(7), 414–418. Bliss, C. (2012). Race decoded: The genomic fight for social justice. Stanford, CA: Stanford University Press. Blumberg, L. (1994). The politics of prenatal testing and selective abortion. Sexuality and Disability, 12(2), 135–153. Boas, F. (1940). Race, language, and culture. New York: Free Press. Bolnick, D. A., Fullwiley, D., Duster, T., Cooper, R. S., Fujimura, J. H., Kahn, J., et al. (2007). The science and business of genetic ancestry testing. Science, 318, 399–400. Bouchard, T. J. (2004). Genetic influence on human psychological traits: A survey. Current Directions in Psychological Science, 13, 148–151. Boysen, G. A., & Gabreski, J. D. (2012). The effect of combined etiological information on attitudes about mental disorders associated with violent and nonviolent behaviors. Journal of Social and Clinical Psychology, 31(8), 852–877. http://dx.doi.org/10.1521/ jscp.2012.31.8.852. Boysen, G. A., & Vogel, D. L. (2007). Biased assimilation and attitude polarization in response to learning about biological explanations of homosexuality. Sex Roles, 57, 755–762. Brescoll, V., & LaFrance, M. (2004). The correlates and consequences of newspaper reports of research on sex differences. Psychological Science, 15, 515–520. Broberg, G., & Roll-Hansen, N. (1996). Eugenics and the welfare state: Sterilization policy in Denmark, Sweden, Norway, and Finland. East Lansing, MI: Michigan State University Press. Burley, J., & Harris, J. (1999). Human cloning and child welfare. Journal of Medical Ethics, 25, 108–113. http://dx.doi.org/10.1136/jme.25.2.108. Calnan, M., Montaner, D., & Horne, R. (2005). How acceptable are innovative health-care technologies? A survey of public beliefs and attitudes in England and Wales. Social Science & Medicine, 60, 1937–1948. Carlsmith, K. M., Darley, J. M., & Robinson, P. H. (2002). Why do we punish? Deterrence and just deserts as motives for punishment. Journal of Personality and Social Psychology, 83(2), 284–299. http://dx.doi.org/10.1037/0022-3514.83.2.284. Castera, J., & Clement, P. (2014). Teachers’ conceptions about the genetic determinism of human behaviour: A survey in 23 countries. Science & Education, 23(2), 417–443. http:// dx.doi.org/10.1007/s11191-012-9494-0.

182

S.J. Heine et al.

Cavalli-Sforza, L. L., & Feldman, M. W. (2003). The application of molecular genetic approaches to the study of human evolution. Nature Genetics, 33, 266–275. Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J., & Laibson, D. I. (2015). The fourth law of behavior genetics. Current Directions in Psychological Science, 24(4), 304–312. http:// dx.doi.org/10.1177/0963721415580430. Chandler, M. J., & Proulx, T. (2008). Personal persistence and persistent peoples: Continuities in the lives of individual and whole cultural communities. In F. Sani (Ed.), Selfcontinuity: Individual and collective perspectives (pp. 213–226). New York, NY: Psychology Press. Chao, M., Chen, J., Roisman, G., & Hong, Y. (2007). Essentializing race: Implications for bicultural individuals’ cognition and physiological reactivity. Psychological Science, 18, 341–348. Charney, E., & English, W. (2012). Candidate genes and political behavior. American Political Science Review, 106, 1–34. Cheung, B. Y. (2016). Out of my control: The effects of perceived genetic etiology. Doctoral dissertation University of British Columbia. Retrieved from https://open.library.ubc. ca/cIRcle/collections/24/items/1.0228787. [original work published 2016]. Cheung, B. Y., Dar-Nimrod, I., & Gonsalkorale, K. (2014). Am I my genes? Perceived genetic etiology, intrapersonal processes, and health. Social and Personality Psychology Compass, 8(11), 626–637. Cheung, B. Y., & Heine, S. J. (2015). The double-edged sword of genetic accounts of criminality: Causal attributions from genetic ascriptions affect legal decision making. Personality and Social Psychology Bulletin, 41(12), 1723–1738. http://dx.doi.org/ 10.1177/0146167215610520. Cheung, B. Y., White, E. T., Sumitani, J., Truong, G., & Heine, S. J. (2016). My genes tell me how I learn: The implications of perceiving learning styles as genetic. In Poster presented at the 2016 SPSP Annual Meeting—Society for the Teaching of Psychology Preconference, San Diego, CA, USA. Choi, I., Nisbett, R. E., & Norenzayan, A. (1999). Causal attribution across cultures: Variation and universality. Psychological Bulletin, 125, 47–63. Christensen, K. D., Jayaratne, T., Roberts, J., Kardia, S., & Petty, E. (2010). Understandings of basic genetics in the United States: Results from a national survey of black and white men and women. Public Health Genomics, 13(7–8), 467–476. Cole, S. W. (2009). Social regulation of human gene expression. Current Directions in Psychological Science, 18, 132–137. Coleman, J., & Hong, Y. (2008). Beyond nature and nurture: The influence of lay gender theories on self-stereotyping. Self and Identity, 7, 34–53. Comfort, N. (2012). The science of human perfection. New Haven, CT: Yale University Press. Condit, C. M. (2010). Public understandings of genetics and health. Clinical Genetics, 77, 1–9. Condit, C. M., Parrott, R. L., Harris, T. M., Lynch, J., & Dubriwy, T. (2004). The role of “genetics” in popular understandings of race in the United States. Public Understanding of Science, 13, 249–272. Conrad, P. (1999). A mirage of genes. Sociology of Health and Illness, 21, 228–241. Conrad, P. (2002). Genetics and behavior in the news: Dilemmas of a rising paradigm. In J. S. Alper, C. Ard, A. Asch, J. Beckwith, P. Conrad, & L. N. Geller (Eds.), The doubleedged helix: Social implications of genetics in a diverse society. Baltimore, MD: Johns Hopkins University Press. Dar-Nimrod, I. (2012). Postgenomics and genetic essentialism. Behavioral and Brain Sciences, 35(5), 362–363. Dar-Nimrod, I. (2014). The varied effects of genetic essentialist biases: A look at health, prejudice, and person perception realms. In Paper presented at the annual meeting of the Society of Australasian Social Psychologists, Canberra, ACT, Australia.

Genetic Essentialism

183

Dar-Nimrod, I., Cheung, B. Y., Ruby, M. B., & Heine, S. J. (2014). Can merely learning about obesity genes lead to weight gain? Appetite, 81, 269–276. Dar-Nimrod, I., & Godwin, A. (2016). The relationship between genetic knowledge and genetic essentialist biases in an Australian national probabilistic sample. University of Sydney. Dar-Nimrod, I., & Heine, S. J. (2006). Exposure to scientific theories affects women’s math performance. Science, 314(5798), 435. Dar-Nimrod, I., & Heine, S. J. (2011). Genetic essentialism: On the deceptive determinism of DNA. Psychological Bulletin, 137(5), 800–818. http://dx.doi.org/10.1037/ a0021860. Dar-Nimrod, I., Heine, S. J., Cheung, B. Y., & Schaller, M. (2011). Do scientific theories affect men’s evaluations of sex crimes? Aggressive Behavior, 37(5), 440–449. http://dx.doi. org/10.1002/ab.20401. Dar-Nimrod, I., Ruby, M., Godwin, A., Cheung, B., Murray, D., & Tam, K. (2016). The Genetic Essentialist Tendencies Scale (GETS): What can our genetic essentialist biases predict? University of Sydney. Dar-Nimrod, I., Zuckerman, M., & Duberstein, P. R. (2013). The effects of learning about one’s own genetic susceptibility to alcoholism: A randomized experiment. Genetics in Medicine, 15(2), 132–138. Dar-Nimrod, I., Zuckerman, M., & Duberstein, P. (2014). Smoking at the workplace: Effects of genetic and environmental causal accounts on attitudes toward smoking employees and restrictive policies. New Genetics and Society, 33(4), 400–412. Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., et al. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16, 996–1005. de Vries, H., Mesters, I., Van de Steeg, H., & Honing, C. (2005). The general public’s information needs and perceptions regarding hereditary cancer: An application of the integrated change model. Patient Education and Counseling, 56(2), 154–165. Deacon, B. J., & Baird, G. L. (2009). The chemical imbalance explanation of depression: Reducing blame at what cost? Journal of Social and Clinical Psychology, 28(4), 415–435. http://dx.doi.org/10.1521/jscp.2009.28.4.415. Deppe, K. D., Stoltenberg, S. F., Smith, K. B., & Hibbing, J. R. (2013). Candidate genes and voter turnout: Further evidence on the role of 5-HTTLPR. American Political Science Review, 107, 375–381. Diamond, M. N., & Sigmundson, H. K. (1997). Sex reassignment at birth: A long term review and clinical implications. Archives of Pediatrics and Adolescent Medicine, 151, 298–304. Dodd, M. D., Balzer, A., Jacobs, C. M., Gruszczynski, M. W., Smith, K. B., & Hibbing, J. R. (2012). The political left rolls with the good and the political right confronts the bad: Connecting physiology and cognition to preferences. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 367, 640–649. D’Onofrio, B. M., Eaves, L. J., Murrelle, L., Maes, H. H., & Spilka, B. (1999). Understanding biological and social influences on religious affiliation, attitudes and behaviors: A behavior-genetic perspective. Journal of Personality, 67, 953–984. Dougherty, M. J. (2009). Closing the gap: Inverting the genetics curriculum to ensure an informed public. The American Journal of Human Genetics, 85, 6–12. Duarte, J. L., Crawford, J. T., Stern, C., Haidt, J., Jussim, L., & Tetlock, P. E. (2015). Political diversity will improve social psychological science. Behavioral and Brain Sciences, 38, e130. Dunn, R., Griggs, S. A., Olson, J., Beasley, M., & Gorman, B. S. (1995). A meta-analytic validation of the Dunn and Dunn model of learning-style preferences. The Journal of Educational Research, 88(6), 353–362. Duster, T. (2003). Backdoor to eugenics. New York, NY: Routledge.

184

S.J. Heine et al.

Eidelman, S., & Crandall, C. S. (2014). The intuitive traditionalist: How biases for existence and longevity promote the status quo. Advances in Experimental Social Psychology, 50, 53–104. Ereshefsky, M. (2004). The poverty of the Linnaean hierarchy. Cambridge, UK: Cambridge University Press. Evans, D. G. R., Blair, V., Greenhalgh, R., Hopwood, P., & Howell, A. (1994). The impact of genetic counseling on risk perception in women with a family history of breast cancer. British Journal of Cancer, 70, 934–938. Falomir Pichastor, J. M., & Mugny, G. (2009). “I’m not gay… I’m a real man!”: Heterosexual men’s gender self-esteem and sexual prejudice. Personality and Social Psychology Bulletin, 35(9), 1233–1243. Fausto-Sterling, A. (1985). Myths of gender: Biological theories about women and men. New York, NY: Basic Books. Festinger, L., Schachter, S., & Back, K. (1950). Social pressures in informal groups: A study of human factors in housing. Stanford, CA: Stanford University Press. Fowler, J. H., & Dawes, C. T. (2008). Two genes predict voter turnout. The Journal of Politics, 70, 579–594. Frankena, W. K. (1939). The naturalistic fallacy. Mind, 48, 464–477. Frias-Navarro, D., Monterde-i-Bort, H., Pascual-Soler, M., & Badenes-Ribera, L. (2015). Etiology of homosexuality and attitudes toward same-sex parenting: A randomized study. Journal of Sex Research, 52(2), 151–161. Friedland, S. I. (1998). The criminal law implications of the Human Genome Project: Reimagining a genetically oriented criminal justice system. Kentucky Law Journal, 86, 303–341. Galton, F. (1875). The history of twins, as a criterion of the relative powers of nature and nurture. Fraser’s Magazine, 12, 566–576. Galton, F. (1883). Inquiries into human faculty and its development. London, UK: J. M. Dent and Sons. Gaskell, G., Stares, S., Allansdottir, A., Allum, N., Castro, P., Esmer, Y., et al. (2010). Europeans and biotechnology in 2010. Winds of change?: Eurobarometer Survey Series No. 7; Report EUR 24537. Publications Office of the European Union. Gelman, S. A. (2003). The essential child: Origins of essentialism in everyday thought. New York: Oxford University Press. Gelman, S. A. (2004). Psychological essentialism in children. Trends in Cognitive Sciences, 8, 404–409. Gelman, S. A., Frazier, B. N., Noles, N. S., Manczak, E. M., & Stilwell, S. M. (2015). How much are Harry Potter’s glasses worth? Children’s monetary evaluation of authentic objects. Journal of Cognition and Development, 16, 97–117. Gelman, S. A., & Markman, E. M. (1987). Young children’s inductions from natural kinds: The role of categories and appearances. Child Development, 58, 1532–1541. Gelman, S. A., & Welman, H. (1991). Insides and essences. Cognition, 38, 214–244. Giles, J. W. (2003). Children’s essentialist beliefs about aggression. Developmental Review, 23, 413–443. Giles, J. W., Legare, C., & Samson, J. E. (2008). Psychological essentialism and cultural variation: Children’s beliefs about aggression in the United States and South Africa. Infant and Child Development, 17, 137–150. Gillborn, D. (1997). Racism and reform: New ethnicities/old inequalities? British Educational Research Journal, 23(3), 345–360. Gil-White, F. J. (2001). Are ethnic groups biological “species” to the human brain? Essentialism in our cognition of some social categories. Current Anthropology, 42, 515–554. Gonder, M. K., Locatelli, S., Ghobrial, L., Mitchell, M. W., Kujawski, J. T., Lakester, F. J., et al. (2011). Evidence from Cameroon reveals differences in the genetic structure and histories of chimpanzee populations. Proceedings of the National Academy of Sciences of the United States of America, 108, 4766–4771.

Genetic Essentialism

185

Gordon, E. S., Griffin, G., Wawak, L., Pang, H., Gollust, S. E., & Bernhardt, B. A. (2012). “It’s not like judgment day”: Public understanding of and reactions to personalized genomic risk information. Journal of Genetic Counseling, 21, 423–432. Gould, W. A., & Heine, S. J. (2012). Implicit essentialism: Genetic concepts are implicitly associated with fate concepts. PLoS One, 7(6), e38176. Haider-Markel, D. P., & Joslyn, M. R. (2008). Beliefs about the origins of homosexuality and support for gay rights: An empirical test of attribution theory. Public Opinion Quarterly, 72(2), 291–310. Hallman, W., Hebden, C., Aquino, H., Cuite, C., & Lang, J. (2003). Public perceptions of genetically modified foods: A national study of American knowledge and opinion. New Brunswick, NJ: Food Policy Institute, Cook Collee, Rutgers. Publication number RR-1003-004. Hallman, W. K., Hebden, W. C., Cuite, C. L., Aquino, H. L., & Lang, J. T. (2004). Americans and GM food: Knowledge, opinion, and interest in 2004. New Brunswick, NJ: Food Policy Institute, The State University of New Jersey. Publication number RR-1104-007. Hamer, D. H., Hu, S., Magnuson, V. L., Hu, N., & Pattatucci, A. M. (1993). A linkage between DNA markers on the X chromosome and male sexual orientation. Science, 261(5119), 321–327. Harmon, A. (2014). A lonely quest for facts on genetically modified crops. New York Times. January 4. Retrieved October 6, 2015 from http://www.nytimes.com/2014/01/05/us/ on-hawaii-a-lonely-quest-for-facts-about-gmos.html?hp&_r¼1. Haslam, N. (2011). Genetic essentialism, neuroessentialism, and stigma: Commentary on Dar-Nimrod and Heine (2011). Psychological Bulletin, 137, 819–823. Haslam, N., & Levy, S. R. (2007). Essentialist beliefs about homosexuality: Structure and implications for prejudice. Personality and Social Psychology Bulletin, 32, 471–485. Haslam, N., Rothschild, L., & Ernst, D. (2000). Essentialist beliefs about social categories. British Journal of Social Psychology, 39, 113–127. Hatemi, P. K., Alford, J. R., Hibbing, J. R., Martin, N. G., & Eaves, L. J. (2008). Is there a “party” in your genes? Political Research Quarterly, 62, 584–600. Heath, W. P., Stone, J., Darley, J. M., & Grannemann, B. D. (2003). Yes, I did it, but don’t blame me: Perceptions of excuse defenses. The Journal of Psychiatry & Law, 31(2), 187–226. http://dx.doi.org/10.1177/009318530303100204. Heine, S. J. (2017). DNA is not destiny: The remarkable, completely misunderstood relationship between you and your genes. New York, NY: Norton. Heine, S. J., Cheung, B. Y., & Ream, C. (2015). Genetic attributions underlie people’s attitudes towards criminal responsibility and eugenics. In Symposium presentation at the Society for Personality and Social Psychology, Long Beach, CA. Heine, S. J., Kitayama, S., Lehman, D. R., Takata, T., Ide, E., Leung, C., et al. (2001). Divergent consequences of success and failure in Japan and North America: An investigation of self-improving motivations and malleable selves. Journal of Personality and Social Psychology, 81, 599–615. Henderson, M. (2007). ‘Fat’ gene found by scientists. The Times (UK). April 13. Retrieved on June 1, 2009 from http://www.timesonline.co.uk/tol/news/uk/health/article 1647517.ece. Henderson, B. J., & Maguire, B. T. (2000). Three lay mental models of disease inheritance. Social Science and Medicine, 50(2), 293–301. Henig, R. M. (2000). The monk in the garden. New York, NY: Mariner. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world. Behavioral and Brain Sciences, 33, 61–83. Henrich, N., & Henrich, J. (2007). Why humans cooperate: A cultural and evolutionary explanation. Oxford, UK: Oxford University Press. Herrnstein, R. J., & Murray, C. (1994). The bell curve. New York, NY: Free Press. Herzlich, C. (1973). Health and illness: A social psychological analysis. London: Academic Press.

186

S.J. Heine et al.

Heyman, G. D., & Gelman, S. A. (2000). Beliefs about the origins of human psychological traits. Developmental Psychology, 36, 663–678. Hibbing, J. R., Smith, K. B., & Alford, J. R. (2013). Predisposed: Liberals, conservatives, and the biology of political differences. London, UK: Routledge. Hibbing, J. R., Smith, K. B., & Alford, J. R. (2014). Differences in negativity bias underlie variations in political ideology. Behavioral and Brain Sciences, 37(3), 297–307. Hirschfeld, L. A., & Gelman, S. A. (1997). What young children think about the relation between language variation and social difference. Cognitive Development, 12, 213–238. Ho, A. K., Sidanius, J., Levin, D. T., & Banaji, M. R. (2011). Evidence for hypodescent and racial hierarchy in the categorization and perception of biracial individuals. Journal of Personality and Social Psychology, 100, 492–506. Hudson, K., & Scott, J. (2002). Public awareness and attitudes about genetic technology. Washington, DC: Genetics and Public Policy Center. Hunley, K. L., Healy, M. E., & Long, J. C. (2009). The global pattern of gene identity variation reveals a history of long-range migrations, bottlenecks, and local mate exchange: Implications for biological race. American Journal of Physical Anthropology, 139, 35–46. Inbar, Y., & Lammers, J. (2012). Political diversity in social and personality psychology. Perspectives on Psychological Science, 7(5), 496–503. Inbar, Y., Pizarro, D. A., & Bloom, P. (2009). Conservatives are more easily disgusted than liberals. Cognition and Emotion, 23(4), 714–725. Inspector, I., Kutz, Y., & David, D. (2004). Another person’s heart: Magical and rational thinking in the psychological adaptation to heart transplantation. The Israeli Journal of Psychiatry and Related Sciences, 41, 161–173. Jayaratne, T. E., Gelman, S., Feldbaum, M., Sheldon, J., Petty, E., & Kardia, S. (2009). The perennial debate: Nature, nurture, or choice? Black and White Americans’ explanations for individual differences. Review of General Psychology, 13, 24–33. Jayaratne, T. E., Giordimaina, A., & Gaviglio, A. (2012). Lay beliefs about genetic influences on the development of alcoholism: Implications for prevention. In A. Chapman (Ed.), Genetic research on addiction: Ethics, the law, and public health. Cambridge, UK: Cambridge University Press. Jayaratne, T. E., Ybarra, O., Sheldon, J. P., Brown, T. N., Feldbaum, M., Pfeffer, C. A., et al. (2006). White Americans’ genetic lay theories of race differences and sexual orientation: Their relationship with prejudice toward Blacks, and gay men and lesbians. Group Processes & Intergroup Relations, 9(1), 77–94. http://dx.doi.org/10.1177/1368430206059863. Jost, J. T., & Amodio, D. M. (2012). Political ideology as motivated social cognition: Behavioral and neuroscientific evidence. Motivation and Emotion, 36, 55–64. Jost, J. T., Glaser, J., Kruglanski, A. W., & Sulloway, F. (2003). Political conservatism as motivated social cognition. Psychological Bulletin, 129(3), 339–375. Joy, S., & Kolb, D. A. (2009). Are there cultural differences in learning style? International Journal of Intercultural Relations, 33(1), 69–85. Kanai, R., Feilden, T., Firth, C., & Rees, G. (2011). Political orientations are correlated with brain structure in young adults. Current Biology, 21(8), 677–680. Kang, S. K., Plaks, J. E., & Remedios, J. D. (2015). Folk beliefs about genetic variation predict avoidance of biracial individuals. Frontiers in Psychology, 6, 357. Kanovsky, M. (2007). Essentialism and Folksociology: Ethnicity again. Journal of Cognition and Culture, 7, 241–281. Kaufman, D. J., Bollinger, J. M., Dvoskin, R. L., & Scott, J. A. (2012). Risky business: Risk perception and the use of medical services among customers of DTC personal genetic testing. Journal of Genetic Counseling, 21, 413–422. Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press.

Genetic Essentialism

187

Keller, J. (2005). In genes we trust: The biological component of psychological essentialism and its relationship to mechanisms of motivated social cognition. Journal of Personality and Social Psychology, 88, 686–702. Kelly, S. E. (2009). Choosing not to choose: Reproductive responses of parents of children with genetic conditions or impairments. Sociology of Health & Illness, 31(1), 81–97. Kendler, K. S., & Karkowski-Shuman, L. (1997). Stressful life events and genetic liability to major depression: Genetic control of exposure to the environment? Psychological Medicine, 27, 539–547. Kevles, D. J. (1992). Out of eugenics: The historical politics of the human genome. In D. J. Kevles & L. Hood (Eds.), The code of codes (pp. 3–36). Cambridge, MA: Harvard University Press. Kevles, D. J. (1995). In the name of eugenics. Cambridge, MA: Harvard University Press. Kimel, S. Y., Huesmann, R., Kunst, J. R., & Halperin, E. (2016). Living in a genetic world how learning about interethnic genetic similarities and differences affects peace and conflict. Personality and Social Psychology Bulletin, 42(5), 688–700. Kraus, M. W., Piff, P. K., Mendoza-Denton, R., Rheinschmidt, M. L., & Kelter, D. (2012). Social class, solipsism, and contextualism: How the rich are different from the poor. Psychological Review, 119, 546–572. Kuhn, T. (1962/1996). The structure of scientific revolutions. Chicago, IL: The University of Chicago Press. Kvaale, E. P., Gottdiener, W. H., & Haslam, N. (2013). Biogenetic explanations and stigma: A meta-analytic review of associations among laypeople. Social Science & Medicine, 96, 95–103. Lamason, R. L., Mohideen, M.-A. P. K., Mest, J. R., Wong, A. C., Norton, H. L., Aros, M. C., et al. (2005). SLC24A5, a putative cation exchanger, affects pigmentation in zebrafish and humans. Science, 310, 1782–1786. Lanie, A. D., Jayaratne, T. E., Sheldon, J. P., Kardia, S. L. R., Anderson, E. S., Feldbaum, M., et al. (2004). Exploring the public understanding of basic genetic concepts. Journal of Genetic Counseling, 13, 305–320. Lebowitz, M. S., & Ahn, W.-K. (2014). Effects of biological explanations for mental disorders on clinicians’ empathy. Proceedings of the National Academy of Sciences of the United States of America, 111, 17786–17790. Lebowitz, M. S., Ahn, W. K., & Nolen-Hoeksema, S. (2013). Fixable or fate? Perceptions of the biology of depression. Journal of Consulting and Clinical Psychology, 81, 518–527. Lee, A. (2009). The book of life in the double helix: Cultural differences in biological beliefs. Master thesis Queens University. Lemaux, P. G. (2008). Genetically engineered plants and foods: A scientist’s analysis of the issues (Part I). Annual Review of Plant Biology, 59, 771–812. LeVay, S. (1996). Queer science: The use and abuse of research into homosexuality. Cambridge, MA: MIT Press. Lewontin, R. (1972). The apportionment of human diversity. Evolutionary Biology, 6, 396–397. Lineweaver, T. T., Bondi, M. W., Galasko, D., & Salmon, D. P. (2014). Effect of knowledge of APOE genotype on subjective and objective memory performance in healthy older adults. The American Journal of Psychiatry, 171(2), 201–208. Lippa, N. C., & Sanderson, S. C. (2012). Impact of information about obesity genomics on the stigmatization of overweight individuals: An experimental study. Obesity, 20(12), 2367–2376. http://dx.doi.org/10.1038/oby.2012.144. Locke, J. (1671/1959). In An essay concerning human understanding: Vol. 2. New York, NY: Dover. Lombroso, C. (1876/2007). Criminal man (M. Gibson, & N. H. Rafter, Trans.). Durham: Duke University Press.

188

S.J. Heine et al.

Ludmerer, K. M. (1972). Genetics and American society: A historical appraisal. Baltimore, MD: Johns Hopkins University Press. p. 34. Malt, B. C. (1989). An online investigation of prototype and exemplar strategies in classification. Journal of Experimental Psychology. Learning, Memory, and Cognition, 15, 539–555. Marks, N. J. (2009). Public understanding of genetics: The deficit model. eLS. http://dx.doi. org/10.1002/9780470015902.a0005862.pub2. Martin, N. G., Eaves, L. J., Heath, A. R., Jardine, R., Feingold, L. M., & Eysenck, H. J. (1986). Transmission of social attitudes. Proceedings of the National Academy of Sciences of the United States of America, 83, 4364–4368. McGue, M., & Lykken, D. T. (1992). Genetic influence on risk of divorce. Psychological Science, 3, 368–373. McVay, M. A., Steinberg, D. M., Askew, S., Kaphingst, K. A., & Bennett, G. G. (2015). Genetic causal attributions for weight status and weight loss during a behavioral weight gain prevention intervention. Genetics in Medicine, 18, 476–482. http://dx.doi.org/ 10.1038/gim.2015.109. Medin, D. L., & Ortony, A. (1989). Psychological essentialism. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 179–195). New York: Cambridge University Press. Moe`, A., & Pazzaglia, F. (2010). Beyond genetics in mental rotation test performance: The power of effort attribution. Learning and Individual Differences, 20(5), 464–468. Monterosso, J., Royzman, E. B., & Schwartz, B. (2005). Explaining away responsibility: Effects of scientific explanation on perceived culpability. Ethics & Behavior, 15(2), 139–158. http://dx.doi.org/10.1207/s15327019eb1502_4. Mooney, C. (2012). The Republican brain: The science of why they deny science—And reality. Hoboken, NJ: John Wiley & Sons. Moore, G. E. (1903). Principia Ethica. Cambridge, UK: Cambridge University Press. Morandini, J. S., Blaszczynski, A., Costa, D. S., & Dar-Nimrod, I. (2016). Sexual orientation beliefs and patterns of sexual attraction: Associations with sexual orientation uncertainty, internalized stigma and psychological wellbeing in lesbian and bisexual women. University of Sydney. Unpublished manuscript. Morandini, J. S., Blaszczynski, A., Ross, M. W., Costa, D. S., & Dar-Nimrod, I. (2015). Essentialist beliefs, sexual identity uncertainty, internalized homonegativity and psychological wellbeing in gay men. Journal of Counseling Psychology, 62(3), 413–424. Moravcsik, J. (2001). Essences, powers, and generic propositions. In T. Scaltsas, D. Charles, & M. Gill (Eds.), Unity, identity, and explanation (pp. 229–244). Oxford, UK: Oxford University Press. National Human Genome Research Institute. (2016). http://www.genome.gov/ sequencingcosts. Retrieved June 24, 2016. Nelkin, D., & Lindee, M. S. (1995). The DNA mystique: The gene as a cultural icon. New York, NY: Freeman. Nemeroff, C., & Rozin, P. (1994). The contagion concept in adult thinking in the United States: Transmission of germs of interpersonal influence. Ethos, 22, 158–186. Nisbett, R. E. (2009). Intelligence and how to get it. New York, NY: Norton. No, S., Hong, Y., Liao, H., Lee, K., Wood, D., & Chao, M. (2008). Lay theory of race affects and moderates Asian Americans’ responses toward American culture. Journal of Personality and Social Psychology, 95, 991–1004. Norenzayan, A., & Heine, S. J. (2005). Psychological universals: What are they and how can we know? Psychological Bulletin, 131, 763–784. Norton, A. T., & Herek, G. M. (2013). Heterosexuals’ attitudes toward transgender people: Findings from a national probability sample of US adults. Sex Roles, 68, 738–753.

Genetic Essentialism

189

Novembre, J., Johnson, T., Bryc, K., Kutalik, Z., Boyko, A. R., Auton, A., et al. (2008). Genes mirror geography within Europe. Nature, 456, 98–101. O’Sullivan, C. Y., Lauko, M. A., Grigg, W. S., Qian, J., Zhang, J., Lutkus, A. D., et al. (2003). NAEP science 2000. Editorial Note, 5(1), 43. Oldham, J., & Kasser, T. (1999). Attitude change in response to information that male homosexuality has a biological basis. Journal of Sex & Marital Therapy, 25(2), 121–124. Olson, J. M., Vernon, P. A., Harris, J. A., & Jang, K. L. (2001). The heritability of attitudes: A study of twins. Journal of Personality and Social Psychology, 80, 845–860. Oxley, D. R., Smith, K. B., Alford, J. R., Hibbing, M. V., Miller, J. L., Scalora, M., et al. (2008). Political attitudes vary with physiological traits. Science, 321(5896), 1667–1670. Parrott, R., & Smith, R. A. (2014). Defining genes using “Blueprint” versus “Instruction” metaphors: Effects for genetic determinism, response efficacy, and perceived control. Health Communication, 29(2), 137–146. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. Paul, D. B. (1995). Controlling human heredity: 1865 to the present. New York, NY: Humanity Books. Pearson, H. (2009). Human genetics: One gene, twenty years. Nature, 460, 164–169. Pedersen, O. B., Axel, S., Rostgaard, K., Erikstrup, C., Edgren, G., Nielsen, K. R., et al. (2015). The heritability of blood donation: A population-based nationwide twin study. Transfusion, 55, 2169–2174. Petrone, J. (January 13, 2015). https://www.genomeweb.com/microarrays-multiplexing/ consumer-genomics-market-should-pass-tipping-point-3-million-samples-tested. Retrieved on May 7, 2015. Pew Research Center (2015). Chapter 3: Attitudes and beliefs on science and technology topics. Retrieved on October 7, 2015, from http://www.pewinternet.org/2015/01/29/ chapter-3-attitudes-and-beliefs-on-science-and-technology-topics/. Phelan, J. C. (2005). Geneticization of deviant behavior and consequences for stigma: The case of mental illness. Journal of Health and Social Behavior, 46, 307–322. Phelan, J. C., Cruz-Rojas, R., & Reiff, M. (2002). Genes and stigma: The connection between perceived genetic etiology and attitudes and beliefs about mental illness. Psychiatric Rehabilitation Skills, 6(2), 159–185. Phelan, J. C., Yang, L. H., & Cruz-Rojas, Rr (2006). Effects of attributing serious mental illnesses to genetic causes on orientations to treatment. Psychiatric Services, 57, 382–387. Pinker, S. (2003). The blank slate: The modern denial of human nature. New York, NY: Penguin. Pinker, S. (2012). The better angels of our nature. New York, NY: Penguin. Plaks, J. E., Malahy, L. W., Sedlins, M., & Shoda, Y. (2012). Folk beliefs about human genetic variations predict discrete versus continuous racial categorization and evaluative bias. Social Psychological and Personality Science, 3, 31–39. Plomin, R., Corley, R., DeFries, J. C., & Fulker, D. W. (1990). Individual differences in television viewing in early childhood: Nature as well as nurture. Psychological Science, 1, 371–377. Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67(4), 741–763. Radick, G. (2016). Teach students the biology of their time. Nature, 533, 293. Ramagopalan, S. V., Dyment, D. A., Handunnetthi, L., Rice, G. P., & Ebers, G. C. (2010). A genome-wide scan of male sexual orientation. Journal of Human Genetics, 55, 131–132. Rangel, U., & Keller, J. (2011). Essentialism goes social: Belief in social determinism as a component of psychological essentialism. Journal of Personality and Social Psychology, 100(6), 1056.

190

S.J. Heine et al.

Ream, C., Cheung, B. Y., & Heine, S. J. (2016). Correlations between genetics knowledge, genetic essentialism, and eugenics. Unpublished raw data. Reiner, W. G., & Gearhart, J. P. (2004). Discordand sexual identity in some genetic males with cloacal exstrophy assigned to female sex at birth. The New England Journal of Medicine, 350, 333–341. Rice, G., Anderson, C., Risch, N., & Ebers, G. (1999). Male homosexuality: Absence of linkage to microsatellite markers at Xq28. Science, 284(5414), 665–667. Ridley, M. (2003). Nature via nurture: Genes, experience, and what makes us human. New York, NY: HarperCollins Publishers. Rietveld, C. A., Esko, T., Davies, G., Pers, T. H., Turley, P., Benyamin, B., et al. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences of the United States of America, 111, 13790–13794. Rips, L. I. (1989). Similarity, typicality, and categorization. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 21–59). New York: Cambridge University Press. Robertson, J. (2001). Japan’s first cyborg? Miss Nippon, eugenics and wartime technologies of beauty, body, and blood. Body & Society, 7, 1–34. Rosenberg, N. A., Prichard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., et al. (2002). Genetic structure of human populations. Science, 298, 2381–2385. Rosengren, K. S., Gelman, S. A., Kalish, C. W., & McCormick, M. (1991). As time goes by: Children’s early understanding of growth in animals. Child Development, 62, 1302–1320. Roth, W. D., & Lyon, K. (in press). Genetic ancestry tests and race: Who takes them, why, and how do they affect racial identities. In K. Suzuki & D. von Vacano (Eds.), Reconsidering race: Cross-disciplinary and interdisciplinary approaches. New York: Oxford University Press. Rothbart, M., & Taylor, M. (1992). Category labels and social reality: Do we view social categories as natural kinds? In G. R. S. Semin & K. Fiedler (Eds.), Language, interaction and social cognition (pp. 11–36). Newbury Park, CA: Sage. Royal, C. D., Novembre, J., Fullerton, S. M., Goldstein, D. B., Long, J. C., Bamshad, M. J., et al. (2010). Inferring genetic ancestry: Opportunities, challenges, and implications. The American Journal of Human Genetics, 86, 661–673. Sanders, A. R., Martin, E. R., Beecham, G. W., Guo, S., Dawood, K., Rieger, G., et al. (2014). Genome-wide scan demonstrates significant linkage for male sexual orientation. Psychological Medicine, 45, 1379–1388. Scheib, J. E. (1994). Sperm donor selection and the psychology of female mate choice. Ethology & Sociobiology, 15, 113–129. Scheib, J. E., Kristiansen, A., & Wara, A. (1997). A Norwegian note on sperm donor selection and the psychology of female mate choice. Evolution and Human Behavior, 18, 143–149. Schizophrenic Working Group of the Psychiatric Genomics Consortium. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421–427. Schmalor, A., Cheung, B. Y., & Heine, S. J. (2016). Unpublished data. The University of British Columbia. Schomerus, G., Schwahn, C., Holzinger, A., Corrigan, P. W., Grabe, H. J., & Carta, M. G. (2012). Evolution of public attitudes about mental illness: A systematic review and metaanalysis. Acta Psychiatrica Scandinavica, 125, 440–452. Scott, S. E., Inbar, Y., & Rozin, P. (2016). Evidence for absolute moral opposition to genetically modified food in the United States. Perspectives in Psychological Science, 11, 315–324. Shaw, K. R. M., Van Horne, K., Zhang, H., & Boughman, J. (2008). Essay contest reveals misconceptions of high school students in genetics content. Genetics, 178(3), 1157–1168.

Genetic Essentialism

191

Sheldon, J., Pfeffer, C., Jayaratne, T., Feldbaum, M., & Petty, E. (2007). Beliefs about the etiology of homosexuality and about the ramifications of discovering its possible genetic origin. Journal of Homosexuality, 52(3–4), 111–150. Shiloh, S., Rashuk-Rosenthal, D., & Benyamini, Y. (2002). Illness causal attributions: An exploratory study of their structure and associations with other illness cognitions and perceptions of control. Journal of Behavioral Medicine, 25(4), 373–394. Slabbert, M. N. (2006). “Genes ’R Us”: A critique of genetic essentialism. Stellenbosch Law Review, 17, 251–265. Smerecnik, C. M. R., Mesters, I., Verweij, E., de Vries, N. K., & de Vries, H. (2009). A systematic review of the impact of genetic counseling on risk perception accuracy. Journal of Genetic Counseling, 18, 217–228. Sousa, P., Atran, S., & Medin, D. L. (2002). Essentialism and folkbiology: Evidence from Brazil. Journal of Cognition and Culture, 2, 195–223. Spektorowski, A., & Ireni-Saban, L. (2013). Politics of eugenics: Productionism, population, and national welfare. New York, NY: Routledge. Stepan, N. L. (1991). The hour of eugenics: Race, gender, and nation in Latin America. Ithaca, NY: Cornell University Press. Stern, A. M. (2005). Eugenic nation: Faults and frontiers of better breeding in modern America. Berkeley, CA: University of California Press. Stotzer, R. L. (2009). Violence against transgender people: A review of United States data. Aggression and Violent Behavior, 14(3), 170–179. Suhay, E., Brandt, M. J., & Proulx, T. (in press). Lay belief in biopolitics and political prejudice. Personality and Social Psychological Science. Suhay, E., & Jayaratne, T. E. (2012). Does biology justify ideology? The politics of genetic attribution. Public Opinion Quarterly, 77, 497–521. Taber, J. M., & Aspinwall, L. G. (2015). Framing recommendations to promote prevention behaviors among people at high risk: A simulation study of responses to melanoma genetic test reporting. Journal of Genetic Counseling, 24(5), 771–782. Templeton, A. R. (2013). Biological races in humans. Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 262–271. Tercyak, K. P., Peshkin, B. N., Wine, L. A., & Walker, L. R. (2006). Interest of adolescents in genetic testing for nicotine addiction susceptibility. Preventive Medicine, 42(1), 60–65. Turkheimer, E. (1998). Heritability and biological explanation. Psychological Review, 105, 782–791. Turkheimer, E. (2000). Three laws of behavioral genetics and what they mean. Current Directions in Psychological Science, 9, 160–164. Walker, F. O. (2007). Huntington’s disease. The Lancet, 369(9557), 218–228. Walker, I., & Read, J. (2002). The differential effectiveness of psychosocial and biogenetic causal explanations in reducing negative attitudes toward ‘mental illness’. Psychiatry, 65, 313–325. Wang, C., Gonzalez, R., & Merajver, S. D. (2004). Assessment of genetic testing and related counseling services: Current research and future directions. Social Science & Medicine, 58, 1427–1442. Waxman, S., Medin, D. L., & Ross, N. (2007). Folk-biological reasoning-from a crosscultural developmental perspective: Early essentialist notions are shaped by cultural beliefs. Developmental Psychology, 43(2), 294–308. Weiner, B., Perry, R. P., & Magnusson, J. (1988). An attributional analysis of reactions to stigmas. Journal of Personality and Social Psychology, 55, 738–748. Williams, J. E., & Best, D. L. (1990). Sex and psyche: Gender and self viewed cross-culturally. In Cross-cultural research and methodology series: Vol. 13. Newbury Park, CA: Sage. Williams, M., & Eberhardt, J. (2008). Biological conceptions of race and the motivation to cross racial boundaries. Journal of Personality and Social Psychology, 94, 1033–1047.

192

S.J. Heine et al.

Winkelman, W. D., Missmer, S. A., Myers, D., & Ginsburg, E. S. (2015). Public perspectives on the use of preimplantation genetic diagnosis. Journal of Assisted Reproduction and Genetics, 32, 665–675. World Health Organization (WHO) (2001). The World Health Report 2001: Mental health: New understanding, new hope. World Health Organization. Wright, A. J., Weinman, J., & Marteau, T. M. (2003). The impact of learning of a genetic predisposition to nicotine dependence: An analogue study. Tobacco Control, 12, 227–230. Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42, 565–569. Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016). Taking race out of human genetics. Science, 351, 564–565. Yzerbyt, V., Judd, C. M., & Corneille, O. (2004). The psychology of group perception: Perceived variability, entitativity and essentialism. New York, NY: Psychology Press.

CHAPTER FOUR

The Intrapersonal and Interpersonal Dynamics of Self-Regulation in the Leadership Process K. Sassenberg*,†,1, M.R.W. Hamstra{ *Leibniz-Institut f€ ur Wissensmedien, T€ ubingen, Germany † University of T€ ubingen, T€ ubingen, Germany { Maastricht University, Maastricht, The Netherlands 1 Corresponding author: e-mail address: [email protected]

Contents 1. A Selective History of Leadership Research 2. Leaders’ Intrapersonal Dynamics: Leadership Behavior as Goal-Pursuit 2.1 Linking Leaders’ Regulatory Focus to Transactional and Transformational Leadership Behaviors 2.2 Linking Leaders’ Regulatory Mode and Need for Cognitive Closure to Leadership Behavior 3. The Interpersonal Dynamics: Leadership as Social Influence 3.1 Leadership Behavior and Followers’ Self-Regulation Strategies 3.2 Regulatory Fit Between Leader and Follower 3.3 The Case of Regulatory Focus, Transformational, and Transactional Leadership Behaviors 3.4 Leaders’ Influence on Followers Depends on Regulatory Mode and Need for Cognitive Closure 4. Discussion and Conclusion 4.1 Summary of SMLB and Its Application to Regulatory Focus 4.2 The Application of the SMLB Beyond Regulatory Focus 4.3 Avenues for Future Research 4.4 The Relation Between the SMLB and the Conclusions in the Historic Overview 4.5 Contributions to and Implications for Leadership Research 4.6 Contributions to Self-Regulation Research 4.7 Implications for Organizations Appendix Acknowledgments References

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Abstract This chapter presents a model and empirical research approaching the antecedents and consequences of leadership behavior from a self-regulation perspective. The presented self-regulation model of leadership behavior (SMLB) focuses on the role of selfregulation strategies (1) as antecedents of leadership behavior and (2) as guides of leaders’ social influence on followers. Research testing hypotheses derived from the model for regulatory focus, regulatory mode, and need for cognitive closure in the context of leadership is summarized. The presented research addresses two prominent gaps in research on leadership behavior: the impact of motivation on leadership behavior and the social influence processes underlying successful leadership (e.g., perceived leader effectiveness and follower effort).

Leadership is a key to organizational success and societal cohesion. Hence, understanding the conditions and mechanisms of its success is of highest economic and societal relevance. From a management perspective, the answer to the question “Which leader(-ship behavior) is effective?” is most pressing. Therefore, it is not surprising that leadership research has dedicated a substantial amount of attention to finding answers to this question. From a psychological perspective, however, the individual and social processes underlying leadership are much more interesting. Leadership aims to align individuals’ action toward the pursuit of collective goals (i.e., team or organizational goals). Thus, (1) goal-pursuit and (2) social influence are key elements of leadership. Surprisingly, goal-pursuit and social influence have not (yet) formed the core of leadership research and theory—nor in (social) psychological leadership research. Intra- and interpersonal processes of goal-pursuit and social influence, on the other hand, have been core research themes in social psychology. This places leadership solidly and centrally within social psychology. Leadership should, thus, receive more attention in our field than it currently does. This fact motivated us to specifically address these two key elements of leadership and to add a new social psychological perspective to leadership research. To be more precise, we have developed a self-regulation model of leadership behavior (SMLB). The SMLB covers the motivational antecedents of leadership behavior and the mechanisms underlying leaders’ social influence on followers. Specifically, we developed a self-regulation model, because selfregulation theories represent state-of-the-art theorizing about goal-pursuit (i.e., the first core component of leadership mentioned earlier). Furthermore, the model concentrates on leadership behavior, because there is solid

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evidence that certain leadership behaviors contribute to positive outcomes, such as team performance and follower satisfaction (for a metaanalysis, see Judge & Piccolo, 2004), and leadership behavior is the means by which leaders assert social influence (i.e., the second core component of leadership). In short, leadership behavior seems to be the key to understanding the interpersonal dynamics of successful leadership processes. The goal of this chapter and our model, more generally, is to demonstrate how social psychology can contribute to the understanding of leadership behavior, based on its insights about goal-pursuit and social influence—with a particular focus on self-regulation. To this end, the chapter will first provide a selective history of leadership research with a focus on the determinants of leadership success and on how social psychology inspired leadership research. Then, we will discuss the goal-pursuit component of leadership, in particular the intrapersonal (self-regulatory) antecedents of leadership behavior. This section will present the proposition that different types of leadership behavior are behavioral instantiations of self-regulation strategies in the leadership context and report evidence for the hypothesis derived from this proposition. The next section will present the social influence aspect of leadership and explain how theorizing about self-regulation can contribute to predicting its dynamics. We put forward the assumption that the social influence component of leadership relies on (a) followers’ perception of leadership behavior as the encouragement of certain self-regulation strategies and (b) the fit of these strategies with followers’ own self-regulatory preferences. Again, empirical research testing predictions based on these assumptions will be reported (see Fig. 5 for an overview of the propositions). In the concluding section, the SMLB will be summarized and discussed regarding its implications for leadership research, for self-regulation research, and for organizational practice.

1. A SELECTIVE HISTORY OF LEADERSHIP RESEARCH The sheer quantity of scientific publications about leadership precludes a comprehensive review in this chapter (for an overview, see Day, 2014). Nonetheless, we summarize key insights from different approaches to leadership research. The selection of the described research was guided by its relevance to the goal-pursuit and social influence aspects of leadership.

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Early leadership research focused on the characteristics that make individuals good leaders, because there has always been an interest in whom to select as a leader. Weber (1921/engl. 1978) voiced the idea that charisma is the key characteristic of a successful leader. Since then, many studies have investigated the relation between personality characteristics, such as the big five, and leader behaviors and success (see Stogdill, 1974, for a summary). Metaanalyses (Bono & Judge, 2004; Judge, Colbert, & Ilies, 2004) provided evidence for the long-standing assumption that personality traits correlate with leadership behavior and success, but typically with a small effect size (Stogdill, 1974; Yukl, 1998). Nonetheless, interindividual differences still interest leadership researchers and will most likely always do so, because such variables provide a potential basis for the selection of leaders. Individual differences will, therefore, be included in the SMLB as one of several relevant factors. As early as in the 1950s, another perspective suggested that almost anyone can be an effective leader—if the circumstances are right (e.g., Bales, 1950). At that time, two teams of researchers located at Ohio State University and the University of Michigan tried to identify which behaviors make a leader effective and how these behaviors can be classified (e.g., Fleishman, 1953; Katz & Kahn, 1952); the two teams came to similar conclusions, even though they used different terminology. According to these studies, there are two key dimensions of leadership behavior, namely consideration (i.e., positive interpersonal behavior expressing warmth, trust, respect, and so forth toward followers) and initiating structure (i.e., taskrelated behavior, such as giving structure, activating followers, checking results, and so forth). A metaanalysis by Judge, Piccolo, and Ilies (2004) found that consideration correlates with a medium to strong effect size with outcomes such as work satisfaction, whereas initiating structure is related to team and organizational performance with a medium effect size. Hence, leadership behavior seems to be a key to understanding leadership success and, thus, lies at the heart of our approach to leadership. Moreover, the two core components of leadership we identified earlier—goal-pursuit and social influence—find their equivalents in this analysis in initiating structure and consideration, respectively, although the link to these two fundamental components of leadership was not explicitly made by the Ohio State or the Michigan group. Addressing leaders’ personality characteristics or their behavior in isolation, however, might be short sighted. Fiedler (1965) presented in

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one of the early volumes of the Advances in Experimental Social Psychology an interactionist model—his contingency theory—indicating again that leadership research in its early days was a central topic of social psychology. He suggested that the impact of a certain leadership behavior actually depends on the “favorability” of the situation: whether task- or relationship-focused leadership behavior should be preferred depends on the leader–member relations, the task structure, and the leader’s power. Overall, the model has received support (for a metaanalysis, see Strube & Garcia, 1981). Nonetheless, the contingencies between leaders, followers, and tasks have rarely been picked up in later theories. We believe that these contingencies are very important, especially for the social influence component of leadership, and therefore we integrated them into the SMLB. In the 1970s and 1980s, a new approach to the task (or goal-pursuit) dimension of leadership behavior—going beyond the mere initiation of structure—was proposed, when the idea of transformational leadership came up (Bass, 1985; Burns, 1978; House, 1971, 1977). In essence, transformational leaders articulate long term, ideal visions and provide their followers with the freedom to organize their own goal-pursuits and to challenge conventions, because transformational leaders want to change the status quo. Transformational leadership aims to motivate followers to strive for organizational goals and moral standards. It is, thus, a leadership style that is considered most appropriate for a dynamic work environment in which followers need to make responsible decisions. At a more detailed level, transformational leadership, according to Bass (1985), is characterized by four components. First, idealized influence refers to the assumption that the leaders serve as charismatic role models. Second, inspirational motivation implies that leaders have and communicate a clear vision for the future in an enthusiastic way. Third, transformational leaders provide intellectual stimulation by challenging organizational norms, encouraging divergent thinking, and pushing followers to develop innovative strategies. Finally, individualized consideration is the component of transformational leadership that summarizes leader behaviors aiming at the followers’ needs and growth. Transformational leadership is distinguished from transactional leadership. Both are conceptualized as independent dimensions and can, thus, in theory be shown by one and the same leader. Transactional leadership relies on reinforcement, clear rules for exchange, and minimal performance standards. Transactional leadership shares some features with initiating structure in the

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early behavioral approaches. The leader aims to influence the “what” and the “how” of followers’ goal pursuit. Leaders monitor and correct followers’ actions, because transactional leaders want to maintain the status quo. Components of transactional leadership behavior are management by exception— implying that a leader monitors behavior and outcomes of followers and only takes corrective (i.e., reactive, as opposed to proactive) actions—and contingent rewards—by setting up and enforcing clear agreements about the rewards followers receive for their effort and performance in order to attain agreed-upon levels of performance (Bass, 1985). Transformational and transactional leadership have been demonstrated to predict performance, motivation, and beneficial outcomes among followers (for a metaanalysis, see Judge & Piccolo, 2004). Today, more than 30 years after they were introduced, they are key concepts for the description of leadership behavior in research as well as in applied contexts. The web of science listed, for instance, close to 8000 entries on transformational leadership (April 15, 2016), and there is a still growing annual number of papers (in 2004 for the first time >100, in 2008 >200, in 2010 >400, and from 2011 annually >500). Due to the crucial role transformational and transactional leadership behaviors play in leadership research, we considered it of utmost importance that our SMLB can be applied to these leadership styles. Furthermore, within the enormous body of research on transformational and transactional leadership, neither a goal-pursuit perspective on leadership (or leaders’ motivation more generally) nor the processes underlying leaders’ social influence resulting from these leadership styles have received much attention. Precisely, this deficit will be addressed by the application of SMLB to transformational and transactional leadership behaviors. Finally, more recently, leadership regained interest in social psychology, a field that had not devoted a lot of attention to this topic since the days of Fiedler (1965). The social identity theory of leadership took a social influence perspective and was very successful in explaining sources (e.g., the common social identity of leaders and followers) and social outcomes of leadership (e.g., perceived leadership effectiveness and leader endorsement; Hogg & van Knippenberg, 2003). This theory focuses on how leaders manage their social influence potential, relying on the joint social identity of leaders and followers. Its core predictions are that (a) more prototypical group members are more likely to emerge and to be endorsed as leaders and (b) this is particularly true if the social identity of the joint group to which the leader and the followers belong is salient; under these conditions, individual or

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interpersonal aspects become less relevant. Thus, according to this approach, leaders need to represent and realize the goals and identity of the group they share with their followers (see also Haslam, Reicher, & Platow, 2015). Yet, even though this approach clearly takes a social influence perspective on leadership, it does not specify—and it actually excludes the possibility of specifying—successful leadership behavior and the preconditions for it. As this approach indicates that what contributes to leadership success is contingent on social identities, it precludes the identification of specific styles, strategies, or behaviors that contribute to leadership success across the board (i.e., across different groups). Hence, the social identity theory of leadership does not provide insights about the social influence processes underlying the effects of specific leadership behavior (e.g., transactional and transformational), nor does it provide information about the motivational antecedents of specific leadership behaviors and leaders’ social influence. This might be partly due to the fact that transformational and transactional leadership behaviors focus on interpersonal effects of leadership—which the social identity theory of leadership explicitly excludes from its areas of application. Taken together, the social identity approach underlines the importance of considering social influence in the context of leadership, which is one of the two aspects of leadership we indeed aim to address. At the same time, it does not provide information about which leadership behavior might contribute to social influence and under what specific conditions, which is another aspect we seek to address. In sum, leadership research over the decades has developed a variety of structurally different approaches that have led to a number of take-home messages that new approaches should consider. First, the interindividual differences considered so far relate (at least to some extent) to leadership behavior and its outcomes, and they are relevant for the selection of leaders. This calls for a more fine-grained approach to interindividual differences. Therefore, it would be beneficial to consider interindividual differences in any new approach to leadership. Second, particular sets of behaviors are valid predictors of leadership success. Therefore, leadership behavior will form a key element in the SMLB. Transformational and transactional leadership behaviors predict leadership success particularly well. Hence, in order to constitute a comprehensive model, SMLB should be particularly valid for these leadership behaviors. Third, the effects of leader characteristics and leadership behavior are contingent on the situation, the followers, and so forth. Thus, contingencies or interaction effects might be more likely to describe the effects of leadership correctly than simple cause–effect relations

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(mirrored in bivariate correlations or main effects). Finally, a task dimension like goal-pursuit and a social dimension like social influence have been separately addressed in several approaches. Hence, it seems promising to address both dimensions in any new approach. Besides these conclusions, the earlier summary also reveals two deficits: First, there is a lack of knowledge regarding the motivational antecedents of leadership behavior—that is, what actually motivates a leader to show a specific behavior. Although there is research on the impact of the motivation to lead (e.g., the power motive) on leadership and its outcomes (e.g., Chan & Drasgow, 2001; McClelland & Boyatzis, 1982; Winter, 1991), it has unfortunately almost never been systematically connected to leadership behavior (for an exception, see De Hoogh et al., 2005). Second, the processes underlying the social influence of leadership behavior on followers have not been well understood (cf. Bono & Judge, 2004; Judge, Bono, Ilies, & Gerhardt, 2002)—that is, why a certain leadership behavior affects followers. Therefore, we developed the SMLB and tested it in the research described later. To clearly relate our theoretical assumptions to the two key deficits, we present the model in two separate sections. The first focuses on how goal-pursuit guides leadership behavior—or leaders’ intrapersonal dynamics—to explain the motivational antecedents. The second examines the resulting social influence processes—or the interpersonal dynamics between leaders and followers—to explain the processes underlying leaders’ social influence. Both of these sections start with a general model, making propositions that are valid across different types of self-regulation and leadership behavior. Afterwards, concrete hypotheses are derived from the general propositions for specific types of self-regulation and leadership behavior, with a focus on (but not restricted to) transformational and transactional leadership behaviors (Bass, 1985).

2. LEADERS’ INTRAPERSONAL DYNAMICS: LEADERSHIP BEHAVIOR AS GOAL-PURSUIT Leadership aims at aligning individual action toward the pursuit of joint (e.g., organizational or team) goals. In other words, leadership is goalpursuit at two levels: (a) the leaders’ behavior is their way of pursuing their goal to influence followers’ actions so that (b) the followers contribute to the achievement of the organization’s, the team’s, and the leader’s goals. As a consequence, different styles of leadership behavior are essentially different strategies applied in goal-pursuit. Self-regulation research, which has played a

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prominent role in social psychology over the last 2 decades, focuses exactly on identifying such self-regulation strategies, their antecedents, and their consequences. Therefore, studying leadership behavior from a self-regulation perspective conceptually makes sense. Self-regulation broadly summarizes “the volitional and cognitive processes individuals apply to reach a (subjectively) positive state” (Sassenberg & Woltin, 2008, p. 127), such as a goal. Brendl and Higgins (1996) locate selfregulation strategies at the lowest of three goal levels. They distinguish between high identity goals, low identity goals, and strategic goals. • High identity goals (e.g., team success) are very abstract. They do not specify whether a state is positive or negative. However, they allow deriving the more concrete low identity goals. • Low identity goals (e.g., convincing each team member to put substantial effort into team tasks) specify positive states to be strived for or negative states to be avoided. The achievement of low identity goals helps to pursue a high identity goal from which a low identity goal is derived. • Finally and most importantly in the current context, strategic goals specify the actions taken to achieve the states specified by a low identity goal. Across different low identity and high identity goals, strategic goals can follow a common principle, such as following rules to be “on the safe side.” The dominance of such a general (goal- and context-independent) principle is called self-regulation strategy. Applied to leadership, the high identity goal of a leader is to align subordinates’ actions toward the pursuit of a joint goal (e.g., team or organizational success across contexts). The low identity goals specify how this high identity goal is achieved in a particular context (e.g., completing a specific project so that the customer is very satisfied). The leadership behavior chosen as means to reach the specific low identity goals corresponds to the strategy goals (e.g., providing clear feedback on each part of the project). If a leader shows the same leadership behavior across different tasks and followers, s/he applies a certain self-regulation strategy. According to this analysis, leadership style—conceptualized in line with most leadership behavior research as a long-term tendency of a leader to consistently use a certain set of behaviors—is a self-regulation strategy applied in the leadership role or context. Therefore, it seems logical to search for the antecedents of leadership behavior among general strategies applied in goal-pursuit (i.e., selfregulation strategies). The SMLB proposes that self-regulation strategies should be considered the key antecedents of leadership behavior. What renders the application of self-regulation theories particularly attractive for the leadership domain is that self-regulation strategies are

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conceptualized as linked to features of the situation as well as to features of individuals (e.g., leaders or followers). Hence, they might belong to the interindividual differences that predict leadership behavior and that are important for leader selection (see first take home message earlier). At the same time, self-regulation styles vary in response to certain situational features, which allows for the flexibility for which situation-contingent leadership strategies call (see second take home message earlier). All in all, considering self-regulation in the context of different types of leadership behavior seems promising, because it does justice to the key requirements for a new approach to leadership derived in the historical overview: self-regulation strategies predict types of (leadership) behavior based on interindividual differences and contingent on contextual features. Moreover, leadership is about goal-pursuit, and leadership behavior is, in essence, consistent means-selection across different low identity goals in the leadership context. For example, a leader who, across situations, is consistently monitoring followers’ performance uses monitoring as a consistent way of making sure that low identity goals are being pursued effectively. Closely related, self-regulation strategies guide the selection of means across contexts. They should, thus, also guide the selection of means in the leadership context. Therefore, the first proposition of our SMLB is: Proposition 1 Different types of leadership behavior are behavioral instantiations of selfregulation strategies in the leadership context.

To render this abstract proposition testable, it is essential to find selfregulation strategies that match the types of leadership behavior to be explained. Based on the prominent role of transformational and transactional leadership behaviors in the literature, we first sought to find self-regulation strategies that are suitable to predict each of these types of leadership behavior. Therefore, we developed the predictions of the model in the most detailed fashion for transformational and transactional leadership behaviors, and the self-regulation approach corresponding to these types of leadership behaviors, which is—for a number of reasons outlined in the next section— regulatory focus theory (Higgins, 1997). Later in this chapter, we will also review research providing support for Proposition 1 based on other selfregulatory concepts and other types of leadership behavior.

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2.1 Linking Leaders’ Regulatory Focus to Transactional and Transformational Leadership Behaviors According to regulatory focus theory (Higgins, 1997), human goal-pursuit is guided by two motivational systems: the promotion system and the prevention system. A promotion focus (i.e., the predominant activation of the promotion system) is characterized by the eager pursuit of advancement and accomplishment needs. The dominant goals are ideals, and individuals focus on making hits or, in other words, approaching positive outcomes. For example, a person with a promotion focus will take every opportunity that presents itself, because each instance holds the potential for a hit. In contrast, a prevention focus implies the vigilant pursuit of safety and security needs. The dominant goals are obligations, and individuals focus on avoiding false alarms or, in other words, trying to avoid errors. For instance, a person with a prevention focus will be selective and more often say no to opportunities that present themselves. Regulatory focus varies between people and also depends on situational characteristics. The two foci are independent dimensions. Thus, they are assessed with two separate scales that correlate very little or not at all (e.g., Higgins et al., 2001; Lockwood, Jordan, & Kunda, 2002; Sassenberg, Ellemers, & Scheepers, 2012), but in most experimental studies, a promotion and a prevention condition are compared. Research on regulatory focus has, over the years, accumulated an enormous body of evidence concerning its impact on the resulting two strategies applied during goal-pursuit. In addition, regulatory focus has been demonstrated to affect a wide range of social behaviors, including decision making in economic games (Gu, Bohns, & Leonardelli, 2013), negotiation (Galinsky, Leonardelli, Okhuysen, & Mussweiler, 2005; Tr€ otschel, B€ undgens, H€ uffmeier, & Loschelder, 2013), the impact of close others on goal-pursuit (Righetti, Finkenauer, & Rusbult, 2011; Righetti, Rusbult, & Finkenauer, 2010), perspective taking and empathy (Woltin, Corneille, Yzerbyt, & F€ orster, 2011; Woltin & Yzerbyt, 2015), as well as social discrimination and victims’ responses to it (Sassenberg & Hansen, 2007; Sassenberg, Kessler, & Mummendey, 2003). More important than the impact of regulatory focus on this broad range of social phenomena is the fact that it also plays a role in social influence, which is essential for leadership. Regulatory focus determines which role models inspire us (Lockwood et al., 2002), influences how people deal with norms (Zhang, Higgins, & Chen, 2011), moderates the impact of different types of persuasive messages (Lee & Aaker, 2004; Tykocinski, Higgins, &

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Chaiken, 1994), and impacts on social influence during group decision making (Sassenberg, Landkammer, & Jacoby, 2014). In sum, the self-regulation strategies related to regulatory focus are relevant to a host of social phenomena and, most importantly, to social influence. But how and why should these strategies affect transformational and transactional leadership behaviors? As will become evident in the following summary, the strategies resulting from a promotion focus share substantial overlap with transformational leadership behavior, and the strategies that are predominantly applied in a prevention focus correspond to transactional leadership behavior.

2.1.1 Promotion Focus and Transformational Leadership Behavior There are at least five outcomes or associations of a promotion focus that are closely related to aspects of transformational leadership behavior. First, as mentioned earlier, in a promotion focus, ideals are the dominant goals (Higgins, 1997; Higgins, Roney, Crowe, & Hymes, 1994). Similarly, transformational leadership entails emphasizing ideals and presenting an idealistic vision (Bass & Steidlmeier, 1999; Shamir, House, & Arthur, 1993). Second, individuals in a promotion focus tend to see their future in an optimistic way (Hazlett, Molden, & Sackett, 2011) and focus on positive outcomes and gains (Higgins et al., 2001). This is mirrored in transformational leadership behavior, such as communicating with optimism (Berson, Shamir, Avolio, & Popper, 2001), holding high expectations, and having confidence in followers’ ability to reach goals (House & Aditya, 1997). Third, a promotion focus comes with a preference for change over stability (Liberman, Idson, Camacho, & Higgins, 1999) and a tendency to actually implement change when it is indicated (Molden & Hui, 2011; Sassenberg et al., 2014). Individuals with a dominant promotion focus are more inclined to creativity and idea generation (Friedman & F€ orster, 2001; Rietzschel, 2011), and prefer goal attainment over goal maintenance (Brodscholl, Kober, & Higgins, 2007). Similarly, transformational leaders facilitate and value change, innovation, and goal attainment (Howell & Avolio, 1993; Jung, Chow, & Wu, 2003). Fourth, a promotion focus heightens readiness to take risks (Crowe & Higgins, 1997; Hamstra, Bolderdijk, & Veldstra, 2011; Hamstra, Rietzschel, & Groeneveld, 2015) and concern for development and progress (Higgins, 1997), which is similar to transformational leaders’ tendency to take risks (Spangler & House, 1991) and their tendency to give

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followers the freedom and autonomy to develop themselves (which implies the willingness to take risks, Bass, 1985; Bass & Avolio, 1995). Finally, a promotion focus elicits a global information processing style (F€ orster & Dannenberg, 2010; Lee, Keller, & Sternthal, 2010; Zhu & Meyers-Levy, 2007). This means that promotion-focused people predominantly look at the big picture or the gestalt (rather than the details) and take a long-term perspective (cf. Trope & Liberman, 2010). A global information processing style corresponds to providing an abstract vision of the long-term future, which is part of transformational leadership (Bass, 1985; House, 1977; Sosik & Dionne, 1997; Yukl, 1998). All in all, there is a close correspondence between a promotion focus and transformational leadership behavior. Thus, there are strong arguments suggesting that leaders in a promotion focus should be likely to show transformational leadership behavior. 2.1.2 Prevention Focus and Transactional Leadership Behavior The strategic outcomes of a prevention focus and the behaviors subsumed as transactional leadership, likewise, show a number of similarities. First, prevention-focused individuals are concerned with doing what they ought to do (Higgins, 1997; Higgins et al., 1994), which overlaps with transactional leaders’ tendency to set up and enforce clear rules for exchange (Bass, 1985; House, 1971) and to induce follower compliance by referring to norms (Kuhnert & Lewis, 1987; Morhart, Herzog, & Tomczak, 2009). Second, a prevention focus comes with a concern for minimal standards (Keller & Bless, 2008), which is similar to transactional leadership behavior, such as a concern to live up to minimal performance standards (Bass, 1985). Third, a prevention focus leads to avoidance of mistakes or negative outcomes (Crowe & Higgins, 1997; Higgins et al., 2001) and to strong responses to negative events (Idson, Liberman, & Higgins, 2000; Sassenberg & Hansen, 2007). This overlaps with transactional leadership behaviors, such as closely monitoring and correcting followers’ performance and mistakes (Bass, 1985; Bass & Avolio, 1995; Masi & Cooke, 2000). Fourth, a prevention focus creates a tendency toward goal maintenance over goal attainment (Brodscholl et al., 2007) and a preference for stability over change (Liberman et al., 1999). Prevention-focused people stick to decisions once made (Molden & Hui, 2011; Sassenberg et al., 2014). Similarly, transactional leadership is characterized by a focus on preserving the status quo (Oke, Munshi, & Walumbwa, 2009; Yukl, 1998).

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Finally, a prevention focus leads individuals to construe information locally (F€ orster & Dannenberg, 2010; F€ orster & Higgins, 2005), suggesting that they have a short-term perspective, have a tendency to look at the details, and deal naturally with familiar events. The effects of local construal correspond to transactional leadership behavior, such as focusing on shortterm task-specific success, scrutinizing details of followers’ behavior, and aiming to maintain stability (Bass, 1985; House, 1971; Sosik & Dionne, 1997; Yukl, 1998). Taken together, there are strong arguments for the correspondence between a prevention focus and transactional leadership. 2.1.3 Hypotheses: Regulatory Focus and Leadership Behavior Taken together, there is a striking overlap between the self-regulation strategies associated with a promotion focus and the behaviors subsumed as transformational leadership behavior, as well as between the self-regulation strategies associated with a prevention focus and the set of behaviors labeled as transactional leadership. This suggests that transformational vs transactional leadership behavior may be rooted in eager promotion-focused selfregulation vs vigilant prevention-focused self-regulation, respectively. Given the strength of the correspondence, one might even talk about promotion and prevention leadership. Thus, we propose that regulatory focus theory provides a fundamental psychological explanation of the selfregulation strategies people apply in leadership contexts. This explanation has so far been missing from the transformational–transactional paradigm and can shed light on the question of what motivates leaders to exhibit different styles—that is, why they apply them or how they can be motivated to do so. In line with this notion, the hypotheses arising from the selfregulation model of leadership, applied to regulatory focus and transformational–transactional leadership, are (see Fig. 1): Hypothesis 1 Leaders in a (situationally induced or chronic) promotion focus show transformational leadership behavior.

Hypothesis 2 Leaders in a (situationally induced or chronic) prevention focus show transactional leadership behavior.

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Proposition 1

Leaders' selfregulation strategy

Leadership behavior

Hypothesis 1

Leaders' promotion focus

Transformational leadership behavior

Hypothesis 2

Leaders' prevention focus

Transactional leadership behavior

Fig. 1 Intrapersonal dynamics part of the self-regulation model of leadership behavior.

Kark and Van Dijk (2007) came to similar predictions—though based on a less parsimonious argument. They assumed that in a promotion focus, leaders are more open to change and should, therefore, enjoy leading others more. This should, in turn, facilitate transformational leadership behavior. Prevention-focused leaders should, in contrast, due to their conservative tendency (i.e., a focus on safety, conformity, and rules) be motivated to lead because they feel an obligation to do so. Leading out of an obligation should, according to their arguments, result in transactional leadership. In what follows, we will not further discuss this approach, because the argument for the link between promotion focus and transformational leadership behavior relies on a concept that is more reminiscent of a personality trait—openness (for change)—that is less strongly related to transformational leadership behavior (Judge, Colbert, & Ilies, 2004; Judge, Piccolo, & Ilies, 2004) than are self-regulation strategies. Nonetheless, it should be noted that Kark and Van Dijk (2007) came up with the same overall predictions around the time when we started to develop our model about the intrapersonal dynamics of leadership behavior: a stronger promotion focus leads to more transformational leadership behavior and a stronger prevention focus leads to more transactional leadership behavior. 2.1.4 Empirical Evidence There is indirect evidence for the impact of regulatory focus on leadership behavior. This evidence relies on studies showing that one and the same concept (a) is influenced by regulatory focus and (b) asserts an influence on transformational or transactional leadership behavior. This logic of a (causal) chain applies to two concepts linking a promotion focus to

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transformational leadership behavior, namely the striving for power and emotion recognition. The respective research will be discussed in what follows first for striving for power and then for emotion recognition. The link between striving for power in the form of the power motive and leaders’ behavior and performance has long been part of the discussion in leadership research (McClelland & Burnham, 1976/2003, 2003; McClelland & Burnham, 1976/2003, 1976/2003; Winter, 1991). According to Winter (1988), the power motive is defined as “a concern for having impact on others, arousing strong emotions in others, or maintaining reputation and prestige” (p. 510). House, Spangler, and Woycke (1991) argued that a stronger power motive would result in attempts to influence others to behave in line with one’s vision—which is part of transformational and charismatic leadership. In other words, striving for social power and the vision associated with charismatic (and transformational) leadership share a focus on a global, longterm perspective. House et al. (1991) analyzed inaugural speeches from US presidents and their actions during their presidential terms. They found that the stronger the presidents’ power motive (expressed in the inaugural speech), the more charismatic they were during their presidency (e.g., enjoying the ceremonial aspects of being in office or being able to visualize alternatives and weigh long-term consequences). These findings on the relation between the power motive and charismatic behavior support the idea that the power motive leads to charismatic leadership. However, the specific sample and methods limit the conclusions that can be drawn from this study and call for a conceptual replication. Indeed, De Hoogh et al. (2005) provided a replication with a sample of managers using, again, the coding of written information by leaders to assess the power motive, and the Multi-Culture Leader Behavior Questionnaire (Hanges & Dickson, 2004) among followers to assess leadership style. This questionnaire was developed based on the Multifactor Leadership Questionnaire (MLQ, Bass & Avolio, 1995, a very widely used measure of transformational and transactional leadership behaviors, see Table 1 for sample items), as well as other leadership measures, and contains aspects like high expectations of followers, articulation of a vision, and showing confidence in followers. The resulting charismatic leadership score, thus, overlaps to a substantial extent with transformational leadership behavior. Hence, De Hoogh and colleagues’ finding that the power motive predicted more charismatic leadership behavior implies that the power motive also predicts more transformational leadership behavior. Taken together, these studies suggest

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Table 1 Sample Items for Transformational and Transactional Leadership Behaviors from the CLIO and the MLQ and from Scales for Regulatory Focus and Regulatory Mode CLIO

Transformational leadershipa

I talk to subordinates about what is important to them

Transactional leadership

I see to it that agreements are followed through

I am able to make others enthusiastic about my plans

I attach a great deal of value to clear agreements and fair rewards

MLQ

Transformational leadership

I propagate a clear vision about future opportunities

Transactional leadership

I make agreements with subordinates about the rewards they will receive when they do what needs to be done

I speak optimistically about the future

I direct subordinates’ attention toward irregularities, exceptions, and deviations of what is expected of them Regulatory focus

Promotion focus

I want to achieve a great deal I am guided by my ideals I like trying out new things

Prevention focus

Success sets me at ease I am literally always following rules and regulations I take care to carry out my duties

Regulatory mode

Locomotion

I do not mind doing things even if they involve extra effort By the time I accomplish a task, I already have the next one in mind I am a doer

Assessment

I am a critical person I like evaluating other people’s plans I often compare myself to other people

a The transformational leadership scale in this questionnaire is called charismatic leadership, but most of the items are about transformational leadership, because the concepts are very closely related.

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that the power motive or, more generally, striving for power is linked to transformational leadership behavior. There is also a body of research providing strong evidence showing that a promotion focus facilitates the striving for power. Sassenberg, Jonas, Shah, and Brazy (2007) first voiced the prediction that individuals in a promotion focus might hold a particularly strong preference for high power. This is because high power positions (be it groups, roles, or social contexts) provide promotion-focused people with the opportunity to behave in line with their self-regulatory preferences for a global processing style (Magee & Smith, 2013), trying out new things, taking risks and so forth. Support for the relation between a promotion focus and striving for social power was provided by a number of studies in which (a) striving for social power was assessed both with questionnaires and with implicit (i.e., response time based) measures, (b) regulatory focus was either manipulated situationally (via framing the task in terms of gains and nongains in the promotion condition and in terms of losses and nonlosses in the prevention condition) or assessed as a chronic orientation, and (c) a number of different high power roles and high power groups served as targets (e.g., jobs differing in power, self-generated groups high or low in power or gender, Sassenberg et al., 2007; Sassenberg, Brazy, Jonas, & Shah, 2013; Scholl, Sassenrath, & Sassenberg, 2015). In addition, both experimental and longitudinal research demonstrated that employees with a chronic promotion focus preferred jobs with high power and valued power on the job more (Sassenberg & Scholl, 2013). Taken together, this body of research has indicated that (1) a promotion focus leads to stronger striving for social power and that (2) striving for social power relates to transformational (or to be more precise charismatic) leadership behavior. This provides indirect evidence for Hypothesis 1 (i.e., the effect of a promotion focus on transformational leadership behavior). Studies on regulatory focus, emotion recognition (i.e., the correct recognition of emotions displayed by faces), and leadership behavior point in the same direction and additionally provide explicit evidence that the global processing of information that is mentioned earlier in the last argument in favor of the hypothesis, is involved in this process. Indeed, emotion recognition is related to transformational leadership: Rubin, Munz, and Bommer (2005) conducted a correlational study among leaders to test the relation between their emotion recognition ability and their leadership behavior. They expected to find a relation between emotion recognition ability and transformational leadership behavior, because emotion recognition is crucial for understanding others and, thus, a precondition for individualized

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consideration—one of the components of transformational leadership behavior. Leadership behavior was assessed with a measure developed by Podsakoff, MacKenzie, Moorman, and Fetter (1990; e.g., “The leader is always seeking new opportunities,” “The leader has a clear understanding of where we are going,” and “Shows respect for my personal feelings”). Emotion recognition was assessed using the Diagnostic Analysis of Nonverbal Accuracy (DANVA; Nowicki & Duke, 2001), which requires participants to correctly indicate low and high intensity emotions expressed by individuals whose faces are shown in pictures. In other words, the DANVA consists of easy and difficult stimuli. Leadership behavior was assessed with follower ratings. In line with their hypothesis, the authors found that the higher leaders’ emotion recognition ability, the more likely were their followers to judge their leadership style as transformational. In short, emotion recognition was linked to transformational leadership behavior. But is emotion recognition also facilitated by a promotion focus? Sassenrath, Sassenberg, Ray, Scheiter, and Jarodzka (2014) assumed that a more global processing style in a promotion focus should lead to better emotion recognition. This prediction relies on the finding that face recognition in general, and more specifically emotion recognition, are facilitated by global or holistic processing (e.g., Bombari et al., 2013; Wang, Li, Fang, Tian, & Liu, 2012). Moreover, global processing also facilitates communicative understanding (Woltin, Corneille, & Yzerbyt, 2012). In an experiment, participants’ regulatory focus was manipulated via the recall of situations in which participants had applied promotion or prevention selfregulation (Higgins et al., 2001). Emotion recognition was, again, assessed using the DANVA (Nowicki & Duke, 2001). Individuals in a promotion focus indeed showed higher emotion recognition performance than individuals in a prevention focus. In a follow-up study, participants filled in a questionnaire assessing regulatory focus (Sassenberg et al., 2012; see Table 1 for sample items). The DANVA served, again, as a measure of emotion recognition. While performing this test, participants’ fixations were recorded via eye tracking. Low mean fixation duration on the stimulus faces (i.e., short face viewing time) served as indicator of global processing of the stimuli. The regression of emotion recognition on the two regulatory foci indicated that a stronger promotion focus (but not a prevention focus) predicted better emotion recognition. The relation between promotion focus and emotion recognition was mediated by the global processing of the facial stimuli (as indicated by a low mean fixation duration on the facial stimuli).

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All in all, the summarized research found that a promotion focus leads to better emotion recognition due to the global processing it elicits. Emotion recognition, in turn, is related to transformational leadership. In other words, these two papers provide indirect evidence that global processing might contribute to the impact of a promotion focus on transformational leadership behavior. Moreover, a promotion focus elicits stronger striving for power, and striving for power (or the power motive) predicts more transformational leadership behavior. Hence, this research supports the idea that regulatory focus and leadership behavior are related, but it does not directly test it. The research summarized in what follows closed this gap. As a first direct test of the relation between regulatory focus and the two leadership styles (Hamstra, Sassenberg, Van Yperen, & Wisse, 2009), we collected data from leaders from a Dutch company in the health-care sector and from a second sample of leaders from diverse companies. They filled in a questionnaire containing self-reports of their regulatory focus (the scale by Sassenberg et al., 2012). Their leadership style was assessed using two different instruments: the Dutch version of the MLQ (Bass & Avolio, 1995; Dutch version by Den Hartog, Van Muijen, & Koopman, 1997) or the Charismatic Leadership in Organizations Questionnaire (De Hoogh, Den Hartog, & Koopman, 2004; see Table 1 for sample items). We regressed the leadership styles on the two regulatory foci. In line with Hypotheses 1 and 2, promotion focus positively predicted transformational leadership behavior (mean r ¼ 0.51) and prevention focus positively predicted transactional leadership behavior (mean r ¼ 0.50). In contrast, the relation of promotion focus with transactional leadership (mean r ¼  0.01) and of prevention focus with transformational leadership (mean r ¼ 0.07) was not statistically significant. Although these studies provided the first direct support for the prediction, they have two key limitations. First, data about regulatory focus and leadership behavior originated from the same source, which usually leads to an overestimation of effect sizes. Second, the correlational nature of the study does not allow conclusions about causality. Both deficits were addressed in separate follow-up studies: a field study with separate data sources for regulatory focus and leadership behavior and a lab experiment in which regulatory focus was manipulated and leadership behavior was rated by a third party. In the field study (Hamstra et al., 2009), we assessed the self-reported promotion and prevention strategies of communication trainers (promotion: “In my role as a trainer, I regularly try out new things”;

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prevention: “As a trainer I think things through before I act”) in a course with seven lessons. In the fifth of these training sessions, students filled in the Dutch version of the MLQ (Den Hartog et al., 1997) to rate their leaders’ (here, trainers’) leadership behavior. Again, in line with the prediction, a multilevel analysis indicated that leaders’ promotion focus predicted transformational (effect size: r ¼ 0.36), but not transactional leadership, and their prevention focus predicted transactional (r ¼ 0.32), but not transformational leadership. Finally, in a lab experiment (Hamstra, Sassenberg, Van Yperen, & Wisse, 2014), regulatory focus was manipulated via the recall of situations in which participants applied either promotion or prevention strategies (e.g., for the promotion focus condition, participants were asked: “think about a situation in which you felt you made progress toward being successful in your life”; for the prevention focus condition, participants were asked: “think about a situation in which being careful enough avoided you getting into trouble”; Hamstra, Van Yperen, Wisse, & Sassenberg, 2013; Higgins et al., 2001) to test the impact of a situationally induced regulatory focus on leadership behavior. Participants came to the lab in groups of three. They completed a bogusleadership questionnaire that ostensibly served to assign one of them to the role of the leader and the other two to the role of a regular group member. Before the group interaction started, the regulatory focus of the leader was manipulated. Only the leader received the task instructions, and s/he was given the power to distribute incentives between the three members of the group. Each group had to build a house from Lego according to certain criteria. Afterwards, participants responded to a questionnaire. The video tapes of the group interaction were rated concerning the leadership style applied by the group leader. To this end, external raters who were blind to experimental conditions judged the leadership behavior on items adapted from the MLQ (transformational leadership eight items, e.g., “This group’s leader spoke with excitement about what was to be achieved”; transactional leadership eight items, e.g., “This group’s leader brought mistakes to the attention of group members, in order to fulfill the task demands”). Both according to participants (i.e., group members) and according to the external ratings of the video tapes, again in line with our Hypotheses 1 and 2, leaders in a promotion focus (compared to leaders in a prevention focus) showed more transformational leadership behavior, and leaders in a prevention focus (compared to those in a promotion focus) showed more transactional leadership behavior (see Fig. 2).

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Reeks2 Promotion focused leaders Reeks3 Prevention focused leaders

3.0

2.5

2.0

1.5

1.0 Transformational leadership

Transactional leadership

Fig. 2 Transformational and transactional leadership behaviors as a function of leaders’ regulatory focus. Copyright © 2013 Elsevier Inc. Reproduced with permission from Hamstra, M. R. W., Sassenberg, K., Van Yperen, N. W., & Wisse, B. (2014). Followers feel valued—When leaders’ regulatory focus makes leaders exhibit behavior that fits followers’ regulatory focus. Journal of Experimental Social Psychology, 51, 34–40. http://dx.doi.org/10.1016/ j.jesp.2013.11.003.

2.1.5 Summary and Discussion: Regulatory Focus as Predictor of Leadership Behavior In sum, these studies demonstrate that a promotion focus elicits transformational leadership behavior and a prevention focus elicits transactional leadership behavior. The studies provide evidence for this effect in the lab and in different field settings. Hence, the effect of regulatory focus on leadership behavior has been demonstrated with high internal and high external validity (across studies). This research clearly supports Hypotheses 1 and 2 derived from Proposition 1 of the SMLB. Importantly, however, due to the close connection between regulatory focus and the two leadership styles, it is difficult to test for underlying processes. The numerous detailed overlaps mentioned earlier to argue in favor of that relation should be understood as arguments and not as cognitive or motivational processes. Each single aspect of transformational and transactional leadership can be seen as an operationalization of the respective selfregulatory strategy it matches. One element of a promotion strategy, for instance, the preference for change over stability (Liberman et al., 1999), could be operationalized in terms of leaders’ evaluation of change, which is a component of transformational leadership style (Howell & Avolio,

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1993; Jung et al., 2003) and thus cannot be distinguished from it and treated as a separable mediator. Therefore, the assumption underlying the intrapersonal dynamics of our model cannot be tested using a traditional process test in the form of a statistical mediation. However, the indirect evidence summarized in the beginning of this section points to potential concepts underlying the formation of a transformational leadership style among individuals in a promotion focus, namely striving for power, emotion recognition, and global processing. Future research might use a similar approach to test the impact of a promotion focus on transformational leadership via the other elements mentioned when deriving the hypothesis earlier, such as (the attitudes toward) risk taking or openness for change. Such studies would provide a valuable contribution to further validate our account. For the impact of a prevention focus on transactional leadership, separate evidence for one or more of the five arguments presented earlier in favor of that relation is much less straightforward. There is some evidence that obligations, in the sense of guidance by others, (a) influence interpersonal behavior more in a prevention focus and (b) underlie transactional leadership. To be more precise, (a) prevention-focused individuals’ interpersonal behavior is inspired by what others do (i.e., external sources; Zhang et al., 2011) and (b) extrinsic (rather than intrinsic) motivation is an antecedent of transactional leadership (Barbuto, 2005). Even though this provides some indication that obligations might play a role in the impact of a prevention focus on transactional leadership, more evidence concerning the underlying mechanism would definitely be useful. For instance, studies testing the links between a prevention focus, transactional leadership, error management strategies (a concept that should be related to attention to mistakes), and commitment to organizational norms or external standards (concepts that are related to the monitoring of oughts) would be informative. The prediction would be that a specific concept (e.g., commitment to organizational norms) might serve both as an outcome of a prevention focus and as an input for transformational leadership. Overall, the research summarized here provides clear evidence for the impact of a promotion focus on transformational leadership and a prevention focus on transactional leadership. It also presents the first evidence in favor of the reasoning from which we derived these hypotheses. The intrapersonal dynamics underlying leadership behavior presented here provide, for the first time, a model that identifies some of the motivational underpinnings of transformational and transactional leadership behaviors.

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Linking the two leadership styles that strongly predict organizational performance to regulatory focus theory is an additional benefit, because the rich knowledge about the situational antecedents of regulatory focus allows us to derive predictions about specific situational antecedents of transformational and transactional leadership behaviors. A prevention focus is, for instance, elicited by threatening situations (Oyserman, Uskul, Yoder, Nesse, & Williams, 2007; Seibt & F€ orster, 2004). Hence, a transactional leadership style should become more likely in this type of situation. Even though the case for the link between regulatory focus theory and transformational vs transactional leadership behavior is particularly noteworthy—because both approaches have received so much attention in their respective fields—Proposition 1 of the SMLB is more generic. As stated in the proposition, other self-regulation strategies should also predict certain types of leadership behavior. Obvious candidates are self-regulatory dualities that are known to have a trait component, such as action vs state orientation (Kuhl, 1981), approach vs avoidance motivation (Carver & Scheier, 1990; Lewin, 1935), and locomotion vs assessment regulatory mode (Higgins, Kruglanski, & Pierro, 2003; Kruglanski et al., 2000), as well as the need for cognitive closure (Kruglanski & Webster, 1996). There is, indeed, evidence indicating that regulatory mode and need for cognitive closure influence leadership behavior. As this work was not conducted with the intention to test the SMLB, but rather to test a variety of different specific hypotheses, we will first summarize the studies and then draw conclusions about more general hypotheses.

2.2 Linking Leaders’ Regulatory Mode and Need for Cognitive Closure to Leadership Behavior According to regulatory mode theory (Higgins, Kruglanski, & Pierro, 2003), two modes of goal-pursuit can be distinguished: locomotion and assessment modes. Individuals in a locomotion mode continuously initiate and sustain movement, whereas individuals in an assessment mode often evaluate their current state regarding goal progress. As in case of regulatory focus, both modes are considered to be independent dimensions. Pierro, Giacomantonio, Mannetti, Higgins, and Kruglanski (2012) applied regulatory mode theory to leadership and predicted that leaders who are high in both locomotion and assessment should provoke higher performance among their followers. This prediction was derived from regulatory mode theory’s assumption that locomotion (or action) and assessment (or monitoring) are both required to achieve goals. Three studies assessing leaders’

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regulatory mode and followers’ performance supported this prediction: followers performed best when leaders were both high in locomotion and high in assessment orientation. Although these studies did not assess leadership behavior, it seems likely that the effect of leaders’ regulatory mode on followers’ behavior must have been a result of (perceived) leadership behavior. One might speculate that a stronger locomotion mode results in leadership behavior encouraging speed, and a stronger assessment mode results in leadership behavior motivating accuracy, the combination of which may ultimately result in high follower performance. Pierro, Presaghi, Higgins, and Kruglanski (2009) provided more direct evidence for the impact of regulatory mode on concrete leadership behavior: They studied the relation between regulatory mode and teachers’ autonomy-supporting vs controlling instructional styles. The authors expected that an assessment mode should lead to a more controlling instructional style (i.e., applying implicit or explicit pressure and rewards/punishments), because controlling is a form of assessment. In contrast, a stronger locomotion mode should lead to an autonomy-supporting instructional style (i.e., taking the students’ perspective and providing them with information and choice), because locomotors value autonomy and self-guidance and locomotor teachers might, thus, also provide their students with the room to act in that way. A first study assessed teachers’ self-reported locomotion and assessment mode (see Table 1 for sample items), as well as their teaching styles. These styles were assessed using the Problem at School Questionnaire by Deci, Schwartz, Sheinman, and Ryan (1981), which consists of eight scenarios, for each of which participants can chose between four response behaviors: highly autonomy supportive, moderately autonomy supportive, moderately controlling, or highly controlling (for example, see Appendix). The results supported the hypothesis that locomotion predicted a more and assessment predicted a less autonomy-supporting (vs controlling) teaching style. A follow-up experiment induced locomotion and assessment modes by asking participants to think back to three situations in which they had applied one of the two modes and subsequently measured participants’ teaching styles using the same measure as explained earlier. The results replicated the previous finding: a more autonomy-supporting teaching style in the locomotion (rather than in the assessment) condition. In sum, the studies provide evidence for an impact of regulatory mode on autonomysupporting vs controlling teaching styles. Although a replication among organizational leaders would be desirable, the work by Pierro et al. (2009) and Pierro, Kruglanski, and Raven (2012) suggests that regulatory

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mode affects leadership behavior. A strong locomotion mode seems likely to lead to autonomy-supporting leadership behavior and a focus on keeping moving. Assessment mode, in contrast, could be related to controlling, planning, and goal monitoring, which are also leadership behaviors contributing to team success (Peterson & Behfar, 2005). Research has also studied the impact of leaders’ need for cognitive closure on leadership behavior. Need for cognitive closure reflects the desire for an answer, as opposed to ambiguity and confusion (for a recent review, see Roets, Kruglanski, Kossowska, Pierro, & Hong, 2015). In the context of leadership behavior, need for cognitive closure seems to affect the power/ influence tactics that leaders use. The interpersonal power interaction model (Raven, 2001) differentiates between a “hard” basis of power—such as reciprocity, coercion, or reward—and a “soft” basis of power—such as mutual dependence, information, or expertise. Corresponding to these bases of power, leaders can apply hard and soft power tactics (which may be closely related to leadership behavior). As a stronger need for cognitive closure comes with a preference for quick and lasting outcomes, high need for cognitive closure individuals should be more likely to prefer hard power tactics. Pierro, Kruglanski, and Raven (2012) tested this prediction in one study using leaders as the source of information about both need for cognitive closure and power tactics, and a second study assessing need for cognitive closure among leaders and leaders’ power tactics as perceived by their followers. Consistently, across both studies, results supported the prediction that a stronger need for cognitive closure relates to leaders having a more positive evaluation of hard tactics and a less positive evaluation of soft tactics. Even though the correlational design of the studies does not allow for drawing conclusions about causality (but see Pierro, Mannetti, De Grada, Livi, & Kruglanski, 2003), the findings suggest that leaders with a high (vs low) need for closure might behave differently, namely more forcefully, toward their followers. Hence, need for cognitive closure is yet another motivational aspect that seems to assert influence on leadership behavior. In sum, different motivational concepts, namely regulatory focus, regulatory mode, and need for cognitive closure, can affect leadership behavior. These data provide evidence for Proposition 1 of the SMLB: leaders’ styles of goal-pursuit, or to be more precise their self-regulation strategies, seem to be a powerful predictor of leadership behavior. These findings also identify what motivates leaders to adopt a certain leadership behavior. At the same time, the summarized research clearly indicates a need for additional highquality research and the use of more sophisticated designs (e.g., experiments

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and longitudinal studies) in this domain. Taking a self-regulation approach highlights the intrapersonal, dynamic antecedents to leadership behavior and, thus, shows that leadership behavior can be regarded as leaders’ strategies in their goal-pursuit. The next section describes why the same approach is also useful for understanding the interpersonal dynamics and social influence resulting from the respective types of leadership behavior.

3. THE INTERPERSONAL DYNAMICS: LEADERSHIP AS SOCIAL INFLUENCE Conceptualizing leadership as a social process, directed at influencing the goal-pursuit behavior of followers, gives rise to the question how leaders assert influence on their followers in order to align their action toward goals. In other words: how does leaders’ behavior influence followers, and how is self-regulation relevant to this process? Our SMLB proposes that the effects of the social influence process of leadership will depend on the interpersonal dynamics between leader and follower. This perspective implies that the impact of leadership is contingent on the characteristics of the followers (and potentially also the environment; Fiedler, 1965). Specifically, the outcomes of leadership behavior should depend on followers’ self-regulatory preferences. Two closely interrelated mechanisms are vital to understanding this influence process: (1) the impact of leadership on followers depends on the (mis)match of the leadership behavior with followers’ self-regulatory preferences—that is, regulatory (non)fit between leaders and followers—and (2) this regulatory fit is driven by the self-regulation strategies that leadership behavior encourages followers to apply. The latter point means that the social influence of leadership behavior represents a signal for followers as to which self-regulation strategies they are expected to use in the respective social context.

3.1 Leadership Behavior and Followers’ Self-Regulation Strategies Leadership behavior functions as a signal for applying self-regulation strategies that are considered appropriate within that specific social context, such as the team or the organization. In this way, leaders exert direct social influence on followers’ self-regulation strategies. For example, a leader who consistently shows monitoring behavior not only signals that s/he finds accurate performance important, but also this leads to followers’ own monitoring

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behavior becoming more important as well. There are several reasons why such a direct social influence mechanism occurs. To begin, due to their position, leaders have authority over those whom they supervise. This implies the formal authority to allocate rewards or reprimands and to determine the way things are to be done in order to reach predetermined (team or organizational) objectives. Even when leaders allow followers great latitude or complete autonomy in determining how they go about pursuing their individual goals and carrying out their individual tasks (such as in transformational leadership), followers will take their cue from their leader’s example behavior (e.g., Bass, Waldman, Avolio, & Bebb, 1987; Choi & Mai-Dalton, 1999; Mayer, Kuenzi, Greenbaum, Bardes, & Salvador, 2009; Miller & Dollard, 1941). This means that followers look at their leaders to determine the desired or appropriate behavior in a team or organizational context. In addition, leaders’ position typically makes them attractive role models. After all, leaders have had some success within their careers or within the organization. Role models are objects of learning for those who observe them (Bandura, 1977, 1986). On average, leaders will then be seen as (at least to some extent) competent individuals who have been able to rise to their position, meaning they have been, in some manner, effective. Thus, leaders hold prestige or status in the eyes of their followers, even if it is merely by virtue of their position. This status renders leaders naturally attractive role models for followers’ own future behavior (Brown, Trevin˜o, & Harrison, 2005). Due to these mechanisms, followers by and large tend to emulate the behaviors they see their leader exhibiting (Henrich & Gil-White, 2001). They learn that the leader considers this behavior to be appropriate or desirable, and they assume that this behavior will lead to their own success. Many models and theories in the field of organizational behavior and psychology support such a general mechanism, and a wide range of studies indicate that leadership behavior “trickles down” the hierarchical ladder. For example, this applies to more positive behaviors, such as a service-oriented behavior: when leaders consider followers’ needs as important, followers start to consider each other’s, and customers’ needs as important (e.g., Liden, Wayne, Liao, & Meuser, 2014). Likewise, this effect applies to ethical behaviors (Mayer et al., 2009; Ruiz, Ruiz, & Martı´nez, 2011), self-sacrifice (Choi & Mai-Dalton, 1999), and transformational leadership at different hierarchical levels (Bass et al., 1987), but this effect also applies to negative behaviors, such as interpersonal abusiveness, unethical behaviors, and so forth (Aryee, Chen, Sun, & Debrah, 2007; Bardes Mawritz, Mayer,

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Hoobler, Wayne, & Marinova, 2012; Liu, Liao, & Loi, 2012). When followers experience verbal abuse or bullying from their leader, they themselves start to show such behavior. All these examples have in common the assumption that followers learn from their leaders what is rewarded or considered appropriate in the social context and, accordingly, show behavior similar to the behavior that they see their leader exhibiting. What a SMLB adds pertains to the content of what is emulated. That is, leaders function as examples for the self-regulatory strategies of followers, showing how followers are expected to behave and why it is useful for followers to emulate the self-regulatory strategies they observe their leaders applying. This leads us to the next proposition of the SMLB (see Fig. 3): Leader

Follower Followers' selfregulation strategy

Leadership behavior

Encouraged selfregulation strategy

Regulatory fit

Proposition 2 Leader

Transfer to leader and beyond

Proposition 3

Follower Followers' promotion focus

Transformational leadership behavior

Encouraged promotion strategy

Regulatory fit

Hypothesis 3 Leader

Transfer to leader and beyond

Hypothesis 4

Follower Followers' prevention focus

Transactional leadership behavior

Encouraged prevention strategy

Hypothesis 5

Regulatory fit

Transfer to leader and beyond

Hypothesis 6

Fig. 3 Interpersonal dynamics part of the self-regulation model of leadership behavior.

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Proposition 2 The type of leadership behavior signals to followers how they should pursue goals, and, thereby, leaders exert social influence on followers’ selfregulation.

3.2 Regulatory Fit Between Leader and Follower Followers’ own self-regulatory preferences, their dominant strategies, guide their evaluation of specific types of self-regulation strategies they are encouraged or incentivized to apply in a specific situation. For example, some situations call for a promotion strategy: an example of such a situation is one in which a goal is attained by scoring as many hits as possible, and where losses (mistakes) are not punished, as in case of sales performance in an outbound call center (Hamstra, Rietzschel, & Groeneveld, 2015). A person’s promotion vs prevention focus will guide his/her evaluation of this situation due to the strategy implied by the situation. In the leadership context, as discussed earlier, leadership behavior is what encourages certain strategies, and the preferred strategies of followers might or might not correspond to the strategies suggested by the leader (by means of their leadership style). This correspondence between followers’ self-regulatory preferences and the self-regulation strategies suggested by leaders guides the interpersonal dynamics between leaders and followers (i.e., the processes underlying leaders’ social influence on followers). Before going into detail about the implications of this correspondence, we first delineate why they arise and why they are important. When people use the self-regulatory strategies that correspond to their own preference, they experience “regulatory fit” (Higgins, 2000, 2005). The regulatory fit principle entails that when individuals use those selfregulation strategies that are in line with their current self-regulatory preference, this feels right to them. This feeling of “rightness” occurs because fitting strategies “sustain” an individual’s self-regulatory preference, rather than disrupting it. As a general motivational principle, regulatory fit has been supported in scores of studies (e.g., Avnet, Laufer, & Higgins, 2013; Freitas & Higgins, 2002; Keller & Bless, 2006; Lee & Aaker, 2004; Sassenberg et al., 2007). The feeling of “rightness” resulting from regulatory fit affects people’s motivation (i.e., effort and persistence) and attitudes more generally, such that they tend to start feeling right also about

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other things they are encountering (Cesario, Grant, & Higgins, 2004; Hamstra et al., 2013; Higgins, Idson, Freitas, Spiegel, & Molden, 2003). In a sales performance task in a call center as in the example of the earlier mentioned situation, a promotion-focused person required to contact as many people as possible will experience fit, while a preventionfocused person required to do the same will experience misfit. If we would then ask the two people to evaluate something else, let’s say a book that they had read and liked, the promotion-focused person would evaluate the book more positively than the prevention-focused person. That is, because the task experience (the call center) felt right, the promotionfocused person also feels right about his/her opinion about the book, which makes him/her feel more positive, while the prevention-focused person feels wrong about his/her opinion and, hence, less positive. In addition, under conditions of regulatory fit, individuals tend to experience a sense of engagement in the goal-pursuit process, which entails consequences such as exhibiting greater persistence (e.g., Markman, McMullen, Elizaga, & Mizoguchi, 2006; Spiegel, Grant-Pillow, & Higgins, 2004). In a classic regulatory fit study (e.g., Cesario et al., 2004), participants’ dominant self-regulation strategy is assessed or manipulated, after which they are given the opportunity to use a strategy that either fits or does not fit with that concern. For instance, when individuals are given the opportunity to pursue a goal in an eager way (they are given the opportunity to earn a gain or avoid a nongain), this feels right to individuals in a promotion focus, whereas it feels wrong to individuals in a prevention focus. In contrast, when individuals are given the instruction to pursue a goal in a vigilant way (to avoid a loss or approach a nonloss), this feels right to individuals in a prevention focus, but wrong to individuals in a promotion focus. After this procedure, individuals are usually asked to evaluate some unrelated object. Studies show that, under fitting (vs unfitting) conditions, the object is perceived as more valuable. In other words, the experience of fit in a certain situation is transferred to the (unrelated) object. Our example in the preceding paragraph illustrated the regulatory fit principle between leaders and followers using promotion and prevention regulatory focus. However, just as different leadership behaviors can encourage a range of different strategies (beyond those related to regulatory focus), the regulatory fit principle applies more broadly than just to

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regulatory focus. It has, for instance, also received support with regard to regulatory mode (i.e., locomotion vs assessment mode, Avnet & Higgins, 2003). Combining the regulatory fit principle with the first mechanism of leaders’ social influence on self-regulation strategies (i.e., Proposition 2) implies an interaction between, on the one hand, the strategies encouraged by leader behavior and, on the other hand, the followers’ self-regulatory preferences, resulting in fit or nonfit. That is, when a leader encourages selfregulation strategies that fit (vs do not fit) followers’ own self-regulatory preferences, followers feel right about what they are doing and, thus, are more willing to engage in the goal-pursuit. In general, following research on regulatory fit, a host of outcomes should depend on the fit vs nonfit between leader behavior and follower motivation. First and foremost, “feeling right” experiences will transfer from the goal-pursuit itself to other things followers are doing. This means that regulatory fit will make followers experience their leader’s behavior as more “right,” lead them to evaluate their leader’s behavior more positively, to see their leader as more effective, to make followers feel more valued, and to motivate them to engage more in the tasks at hand. Because of this, the leader will more easily be able to influence the follower, will be liked and trusted more, and the relationship between the leader and the follower will be of higher quality—thus, the leader will ultimately be more effective in his or her role as a leader. Moreover, just as this feeling of right about followers’ goal-pursuit transfers to how the followers evaluate their leader, it is equally likely to transfer to other “objects” of the work environment, such as the organization. If the organization starts to “be felt as more right,” employees should in turn be more likely to be committed to the organization and less likely to desire leaving the organization for another job. Hence, the next proposition is based on this notion (see Fig. 3): Proposition 3 A fit between the self-regulatory preferences of followers and the encouraged regulatory strategies as communicated by their leader’s behavior will result in the feeling of rightness among the followers. This feeling will transfer to followers’ evaluations of the leader and the organization, thereby enhancing the leader’s ability to lead effectively and enhancing followers’ relationship to the organization.

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3.3 The Case of Regulatory Focus, Transformational, and Transactional Leadership Behaviors An application of Propositions 2 and 3 can, again, be found in the relations between transformational–transactional leadership behavior and followers’ promotion–prevention self-regulation strategies. The arguments in favor of the links between transformational leadership and the encouragement of a promotion focus as well as between transactional leadership and the encouragement of a prevention focus mirror the earlier provided arguments for the impact of leaders’ regulatory focus on leadership behavior. First, transformational leadership behavior implies the communication of a vision, a vision that is global, abstract, and focused on long-term future prosperity (House, 1977; Rafferty & Griffin, 2004; Sosik & Dionne, 1997). When followers embrace this vision, it means that they, likewise, tend toward adopting a more global, abstract, and long-term perspective for their own goal-pursuit. Second, another example of transformational leadership behavior is risk taking and actively looking for new opportunities and perspectives (Bass, 1985; Yukl, 1998). These behaviors, once exhibited, mean that followers will likewise start to take more risks and to experiment with new ways of working. Third, transformational leaders focus on high levels of performance (Bass, 1985; Bass & Avolio, 1995) and ideal future possibilities (Bass & Steidlmeier, 1999; Shamir et al., 1993). When encouraged to do so by their leaders’ behavior, followers will also start to pursue ideal standards (as opposed to other standards) and start to pursue maximal goals (as opposed to minimal goals). Fourth, transformational leaders’ communication about the to-be-attained objectives is optimistic, and transformational leaders communicate high and positive expectations of their followers (Berson et al., 2001; House & Aditya, 1997). This, in turn, should boost followers’ confidence, instill a sense of optimism in their own goal-pursuit, and promote a tendency to aim high, to be ambitious. Fifth, transformational leaders tend to present the status quo as undesirable (Ford & Ford, 1994); they seek to change away from this status quo (Conger & Kanungo, 1987). As a consequence, followers may, likewise, start to pursue goals leading away from the status quo and look eagerly for opportunities to advance. These consequences of transformational leadership for followers’ selfregulation strategies—the abstract, global, long-term focus, eagerness, and risk taking, seeking to move away from the status quo, optimism, ideal standards, maximal performance goals, taking on new challenges, and trying out

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new ways of working—are all examples of a promotion-focused strategy. This leads to the following prediction: Hypothesis 3 Transformational leadership behavior makes followers perceive that they should pursue goals by using promotion strategies.

In contrast, transactional leadership behavior, first, entails scrutinizing the details of followers’ work (Bass, 1985). When leaders pay so much attention to details, it naturally makes followers consider details to be important, too. Second, transactional leaders focus on short-term and very task-specific successes (Rafferty & Griffin, 2004; Sosik & Dionne, 1997). This and their focus on details likely instill a local construal level and an orientation toward the short-term future among followers. Third, transactional leaders spend effort setting up, clarifying, and enforcing rules and agreements for exchanging rewards for performance (Bass, 1985; House, 1971; Oke et al., 2009). This means followers will tend to look at their tasks referring to minimal standards that they need to reach in order to fulfill their obligations—they aim to do what is expected of them by the leader, what they ought to do. Fourth, transactional leaders monitor mistakes (Bass & Avolio, 1995), which make followers more strategically concerned about accuracy. These consequences of transactional leadership for followers’ self-regulation strategies—details, being specific, oughts, being short-term oriented, error-avoidant, and rulefollowing—are all examples of prevention-focused strategies, which suggest the next hypothesis. Hypothesis 4 Transactional leadership behavior makes followers perceive that they should pursue goals by using prevention strategies.

At this point, one might be tempted to conclude that leadership behavior, and in particular transformational and transactional leadership behaviors, tends to change followers’ regulatory focus. One general argument for such a claim is provided by research on another very basic social influence process, namely the extent to which parenting styles shape the development of regulatory focus in children. Keller (2008) studied this question, linking, in line with our general arguments, transformational and transactional leadership

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behaviors with promotion and prevention, and found significant effects of parenting styles on chronic regulatory focus. More precisely, a bolstering style of parenting (e.g., “My parents told me that they were proud of me when I was trying to be good at something”) increased participants’ promotion focus, whereas a critical and punitive parenting style (e.g., “My parents spanked me when I was disobedient”) increased participants’ prevention focus (see also Higgins & Silberman, 1998). However, the argumentation underlying Hypothesis 3 suggests that leadership behavior does not necessarily change followers’ chronic selfregulation tendencies. We propose that leadership behavior rather encourages certain strategies of goal-pursuit within the social context—to which followers respond differently, depending on their own regulatory focus. What is crucial to the ultimate outcome of the social influence process of leadership is the extent to which the encouraged strategies (that emerge from the leadership behavior) fit followers’ own self-regulatory preferences. This means that the effects of leadership would not necessarily be mediated by followers’ (situational) regulatory focus, but are rather moderated by followers’ (chronic or situational) regulatory focus, and mediated by the encouraged regulatory strategies followers (situationally) apply. To be more concrete, the outcome of social influence resulting from transformational leadership behavior should depend on followers’ promotion focus, because transformational leadership behavior suggests to followers that they are expected to apply promotion strategies. Hence, transformational leadership behavior results in the experience of regulatory fit among followers in a promotion focus (but not among those in a prevention focus). In contrast, social influence resulting from transactional leadership behavior should depend on followers’ prevention focus, because transactional leadership behavior is perceived as encouragement of the application of prevention strategies. Accordingly, transactional leadership should result in a stronger experience of regulatory fit among followers in a prevention focus (but not among those in a promotion focus). This implies two predictions (see Fig. 3): Hypothesis 5 Transformational leadership behavior (because it encourages promotion strategies) will lead to a stronger experience of regulatory fit and more positive evaluations of leaders and organizations in followers who have a promotion focus compared with followers who have a prevention focus.

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Hypothesis 6 Transactional leadership behavior (because it encourages prevention strategies) will lead to a stronger experience of regulatory fit and more positive evaluations of leaders and organizations in followers who have a prevention focus compared with followers who have a promotion focus.

3.3.1 Empirical Evidence We will first address the impact of transformational and transactional leadership on the encouragement of followers to apply, during goal-pursuit, promotion and prevention strategies, respectively (Hypotheses 3 and 4). Indirectly, several studies support the notion that transformational–transactional leadership behaviors encourage promotion–prevention strategies. In a study of training groups in higher education (Hamstra, Van Yperen, Rietzschel, Wisse, & Sassenberg, unpublished data), we measured the leadership behaviors of psychological skills trainers (using the MLQ, see earlier) and assessed the self-regulation strategies that students reported to have been used within their training groups. For example, we asked participants to rate their agreement with statements such as “In this group, people tend to behave eagerly” or “… vigilantly.” The results indicated that the trainers’ transformational leadership behavior was positively related to the group members’ eagerness (i.e., promotion strategies), r ¼ 0.44, whereas transactional leadership was positively related to group members’ vigilance (i.e., prevention strategies), r ¼ 0.24. In this study, the strategies presumably encouraged by the respective leadership behavior were reported to be applied within the group. Similar findings have been reported in a number of studies that focused on followers’ situational or context-dependent regulatory focus as an outcome of leadership behavior. Henker, Sonnentag, and Unger (2015) tested the impact of transformational leadership via work-specific promotion focus on followers’ creativity in a three-wave longitudinal study with a 4-week time lag between the waves. Measures of transformational leadership and promotion focus were taken from followers in wave 1 only. Leadership style was measured with a scale by Podsakoff et al. (1990) that is similar to the MLQ. Items from the Work Regulatory Focus Scale served as measure of promotion focus (e.g., “I tend to take risks at work in order to achieve success”). Henker et al. (2015) found that their participants’ work-specific promotion focus mediated the relation between transformational leadership behaviors and followers’ self-rated creative performance. As another

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example, Kark, Katz-Navon, and Delegach (2015) conducted a series of studies to test the notion that leadership behaviors affected employees’ work related, situational regulatory focus. They reported an experimental simulation and a set of field studies, where they measured situational, work-related regulatory focus with items reflecting eagerness and vigilance, respectively (e.g., “In my work with this manager, I worry about living up to my obligations”). Across their three studies, Kark et al. (2015) consistently found a link between transformational leadership behavior and followers’ eager state (promotion focus), and between transactional leadership and followers’ vigilant state (prevention focus). Similarly, in a related study among team leaders, Shin and Kim (2013) found a positive relation between transformational leadership and team promotion focus and between transactional leadership and team prevention focus. Overall, these studies seem to suggest that leadership behavior asserts an impact on followers’ regulatory focus. This impact is, however, restricted to the self-regulation strategies applied in the work context— sometimes even in one specific situation. Whether this results, over the course of time, in a change in followers’ chronic regulatory focus or whether this is just the expression of conformity to the encouragement triggered by a certain leadership behavior (as predicted in Hypotheses 3 and 4) remains unclear at this stage. Longitudinal research would be required to address this question. However, the proposition that transformational leadership behavior encourages followers to apply promotion-focused strategies, whereas transactional leadership encourages followers to apply prevention-focused strategies, was directly tested and supported in a series of studies we conducted (Hamstra, Van Yperen, Wisse, & Sassenberg, 2014). In one study, for example, employees completed measures of their supervisor’s transformational and transactional leadership behaviors. Transformational leadership was measured with items such as “My supervisor speaks optimistically about the future,” whereas transactional leadership was assessed with items such as “My supervisor makes sure that agreements are followed through on” (Bass & Avolio, 1995; Den Hartog et al., 1997; De Hoogh et al., 2004). In turn, participants completed a measure to assess the strategies they felt encouraged to apply. Specifically, we asked them: “The supervisory behavior of my leader makes me feel primarily encouraged to …” and then provided them with a list of strategies to rate, such as—for promotion—“take risks,” “attain the maximum possible,” and “try out new things” vs—for prevention—“comply with rules and regulations,” “work accurately and in a precise way,” and “behave as others expect of me.”

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In another study (in the same article), participants read scenarios in which leaders presented themselves to the organization. We informed participants that we were interested in testing the impression leaders made when giving information about themselves, in order to be able to better tailor information about new leaders to employees in actual organizations. We created two scenarios, one transformational and one transactional, based on prior research involving scenarios about these styles (Ehrhart & Klein, 2001). For example, the transformational leader scenario emphasized future prospects, doing one’s very best, improving and changing the organization, and reaching something great and important. In contrast, the transactional scenario emphasized the communication of clear expectations and living up to them, scrutinizing that nothing goes wrong, and maintaining the status quo in the organization. In this second study, participants were asked to imagine that they were working with this organizational leader and to rate the strategies they felt this leader encouraged them to employ. In both these studies, we found that followers experienced the transformational leader as encouraging promotion-focused strategies, whereas they experienced the transactional leader as encouraging prevention-focused strategies. Hence, these studies provided direct evidence that transformational leadership encourages followers to apply promotion-focused selfregulation strategies, whereas transactional leadership encourages followers to apply prevention-focused goal-pursuits strategies. In sum, these studies show that transformational leadership encourages promotion strategies, whereas transactional leadership encourages prevention strategies, supporting Hypotheses 3 and 4. Beyond these encouragements, Hypotheses 5 and 6 predict that experienced regulatory fit, resulting from the correspondence between strategies encouraged by the leaders and followers’ regulatory focus, would elevate leaders’ social influence on the followers and result in a number of positive outcomes. In the remainder of this section, studies testing these predictions are summarized. The notion that followers’ self-regulation strategies, such as regulatory focus, moderate the social influence process of leaders is supported by a range of studies outside of “formal” leader–follower relationships (as already mentioned earlier). Lockwood et al. (2002) and Lockwood, Sadler, Fyman, and Tuck (2004), for example, showed that people were more motivated to follow role models when these role models fit with people’s dominant promotion vs prevention orientation. Looking at a more basic process important to leadership—persuasion—fit from regulatory focus has likewise been shown to increase the persuasiveness of influence attempts, such as persuasive messages (Lee & Aaker, 2004; Tykocinski et al., 1994).

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Research in the leadership domain has also documented a fit effect between transformational–transactional leadership and followers’ regulatory focus. Whitford and Moss (2009), for example, studied transformational leadership in distributed work groups. They found that transformational leadership was more strongly associated with job satisfaction of promotionfocused followers. As another example, Moss (2009) found that transformational leadership predicted a sense of engagement more strongly among promotion-focused followers. Hence, both of these studies report outcomes that seem likely to have resulted from regulatory fit, though they did not directly measure participants’ reports of regulatory fit (see next paragraph). Direct support for Hypotheses 5 and 6 comes from a study by Hamstra, Van Yperen, et al. (2014). In this study, leadership behavior was assessed from the perspective of the follower using the MLQ. The regulatory focus questionnaire was employed to assess followers’ regulatory focus. Additionally, regulatory fit was assessed using three items adopted from Sassenberg et al. (2007): (1) Working with this supervisor, I am able to do things in a way that suits me. (2) My supervisor and I fit together well. (3) My supervisor provides me with opportunities to act in the way that I like. Transformational leaders were seen as more effective by promotion-focused followers than by prevention-focused followers, whereas transactional leaders were seen as more effective by prevention-focused followers than by promotion-focused followers (see upper part of Fig. 4). In line with Hypotheses 5 and 6, we found that transformational leadership behavior led to the experience of regulatory fit among followers with a strong promotion focus, whereas transformational leadership behavior led to the same experience among followers with a strong prevention focus (see lower part of Fig. 4). Furthermore, experienced regulatory fit mediated the impact of the fitting conditions on leadership evaluation. Beyond these positive outcomes of regulatory fit, Hamstra, Van Yperen, Wisse, and Sassenberg (2011) found in another study that the regulatory fit between transformational–transactional leadership and followers’ regulatory focus was also associated with lower intentions to leave the organization. Taken together, all these findings indicate that the impact of transformational leadership and transactional leadership behavior on perceived leadership effectiveness depends on (i.e., is moderated by) followers’ regulatory focus. Of course, the effects of this type of leader–follower regulatory fit should extend to the followers’ behavior. When experiencing regulatory fit, people should engage more strongly in their goal-pursuit, leading them to exhibit persistence and extra effort. As an example of this, Hamstra, Sassenberg, et al. (2014) found that followers exhibited greater effort when

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their leaders’ behavior fit their own regulatory focus. As another example, Stam, van Knippenberg, and Wisse (2010) provided evidence that the impact of leaders’ instructions on followers’ performance was moderated by followers’ regulatory focus. Finally, Shin, Kim, Choi, Kim, and Oh (in press) found that fit between leader and follower led followers to exhibit greater effort for the organization, indicating that the behavioral consequences of leader–follower regulatory fit also extend to the organization’s benefit.

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Besides this evidence for the organizational relevance of the fit between leadership behavior and followers’ regulatory focus, there is also experimental evidence for the causal order of the process steps described in Hypotheses 3–6. Hamstra, Van Yperen, et al. (2014) used an experimental causal chain design (Spencer, Zanna, & Fong, 2005) to show that the regulatory fit between transformational–transactional leadership was mediated by the strategies these leaders encouraged followers to apply. As discussed earlier, a first set of studies showed that leadership styles led to differences in strategies followers felt their leader encouraged them to apply. One of these studies experimentally manipulated leadership behavior. In another experiment, the strategies leaders encouraged followers to adopt (rather than leadership behavior) were systematically manipulated via scenarios in which a leader explicitly requested followers to engage in promotion-like eager or prevention-like vigilant pursuit of their work goals. This experiment found that higher leadership effectiveness ratings and stronger experienced regulatory fit, indeed, resulted from the fit between encouraged strategies and followers’ regulatory focus. A follow-up study provided experimental evidence that these effects also apply to follower task engagement (assessed via persistence in terms of time spent working on a difficult task). Taken together, these studies indicate that leadership styles encourage self-regulation strategies, and that the fit of these strategies and followers’ regulatory focus facilitates positive leader evaluations and stronger follower task engagement. Combining the hypothesis on leaders’ intraindividual dynamics and the interindividual dynamics between leaders and followers leads to the prediction that a fit effect would also occur between leaders’ and followers’ regulatory focus (e.g., when both the leader and the follower have a promotion focus, or both have a prevention focus). Such an effect has, indeed, received support in several studies. For example, Ritchie (2009) found that a congruence between leader and follower regulatory focus predicted experiences of fit and enhanced the quality of the relationship between leaders and followers. Similarly, and extending these findings, Johnson and colleagues (unpublished manuscript) found that fit between leader and follower regulatory focus predicted relationship quality and commitment to the leader. Finally, in our group experiment mentioned in the summary of evidence for Hypotheses 1 and 2 (Hamstra, Sassenberg, et al., 2014), we also assessed followers’ regulatory focus (in addition to manipulating leaders’ regulatory focus). We found that fit between leader and follower regulatory focus led to

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followers feeling more valued by their leader. Importantly, in that experiment, we found the same fit effect when we examined the interplay between leaders’ styles and followers’ regulatory focus, and the results indicated that the effect of fit between the leader and follower focus was driven (mediated) by transformational–transactional leadership behavior. 3.3.2 Summary and Discussion All in all, these results indicate (a) leadership behaviors encourage specific strategies of goal-pursuit among followers and (b) the impact of leadership behavior is moderated by followers’ regulatory focus. The empirical evidence reviewed earlier provided consistent and repeated evidence for the social influence mechanisms and the interpersonal dynamics predicted by the SMLB. Coming back to an earlier question, this evidence also reveals that leadership behaviors do not (necessarily) change followers’ regulatory focus. In this regard, it is interesting to note that Hamstra, Van Yperen, et al. (2014) found that transformational and transactional leadership and followers’ chronic regulatory focus did not correlate to a significant extent (whereas the leadership styles and the regulatory foci interacted in their impact on perceived leader effectiveness). The leader–follower interpersonal dynamic outlined in Propositions 2 and 3 represents a more general principle: leadership behaviors that encourage followers to apply strategies that fit (rather than do not fit) the followers’ self-regulatory preferences should elicit a positive dynamic—this positive dynamic is not restricted to (the fit between) transformational and transactional leadership behaviors and followers’ regulatory focus, but also occurs for other types of leadership and for other self-regulation strategies. The following section will summarize evidence supporting Propositions 2 and 3 in relation to regulatory mode and need for cognitive closure.

3.4 Leaders’ Influence on Followers Depends on Regulatory Mode and Need for Cognitive Closure Evidence that the fit between leadership behavior and followers’ selfregulatory preferences affect leaders’ influence on followers (Proposition 3) also comes from studies examining followers’ locomotion vs assessment regulatory mode (Higgins, Kruglanski, & Pierro, 2003) and from studies examining followers’ need for cognitive closure (Kruglanski, 1980).

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Kruglanski, Pierro, Higgins, and Capozza (2007) studied leader–follower regulatory fit using locomotion and assessment regulatory modes of followers. The authors argued that a forceful type of leadership would fit the strategic preference of locomotion-oriented individuals, because such a style, in a sense, compels and drives individuals forward; in contrast, an advisory style was proposed to fit the strategic preference of assessment individuals, because such a style involves counseling, consultation, and mulling things over. Kruglanski et al. (2007) conducted four studies in which they tested their predictions. In their first three studies, they tested whether locomotion vs assessment individuals had a “preferred” or “ideal” leadership type that they liked their supervisor to exhibit. First, they measured participants’ regulatory mode (see Table 1 for sample items). Next, they asked participants about their leadership preferences, using statements such as “My supervisor should provide me with sound, job-related advice.” Locomotion mode strength (but not assessment mode strength) was positively related to preference for forceful leadership, whereas assessment mode strength (but not locomotion mode strength) was positively related to preference for advisory leadership. In their fourth study, Kruglanski et al. (2007), again, assessed locomotion and assessment orientations, but this time they measured the leadership behaviors of those supervisors with whom the participants had an actual, ongoing leader–follower relationship in their work context. They also measured participants’ job satisfaction. A forceful, directive leadership style was positively related to job satisfaction among high locomotion individuals, whereas an advisory style was positively related to job satisfaction among high assessment individuals. Hence, these studies indicate that people have preferred leadership types in line with the strategic preferences that are associated with their selfregulatory orientations. They also show that regulatory fit between those leadership types and followers’ regulatory orientations elicits positive consequences (see also Pierro et al., 2009). Another self-regulatory preference, the need for cognitive closure, has also been connected to preferences for leadership behaviors and to fit effects. Recall that the need for cognitive closure reflects the desire for an answer, as opposed to ambiguity and confusion (for a recent review, see Roets et al., 2015). Belanger et al. (2016) argued that followers who differ in the need for cognitive closure will exhibit diverging preferences for soft vs harsh power tactics used by their leaders. They argued that soft power tactics provide malleable and equivocal guidelines, whereas harsh tactics provide direct and

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unequivocal guidelines. Furthermore, high need for cognitive closure individuals seek clear rules and certainty, whereas low need for cognitive closure individuals like to keep their options open and avoid conclusions. Hence, they concluded that high need for cognitive closure individuals would prefer harsh tactics, whereas low need for cognitive closure individuals would prefer soft tactics. In two studies, Belanger and colleagues (2016) found that fit between followers’ need for cognitive closure and leaders’ power tactics determined the level of followers’ well-being in terms of burnout and stress. The consequences of such fit were also found to extend to other outcome variables. For example, followers high (vs low) in the need for cognitive closure were more willing to comply with their leaders’ harsh influence tactics and were less willing to comply with their leaders’ soft influence tactics (Belanger, Pierro, & Kruglanski, 2015). Additionally, high need for cognitive closure followers were more likely to use constructive forms of conflict resolution when their leaders exhibited harsh influence tactics, whereas low need for cognitive closure individuals were more likely to use constructive forms of conflict resolution when their leaders exhibited soft influence tactics (Belanger et al., 2015). Finally, high need for cognitive closure individuals’ performance was better when their leaders exhibited harsh vs soft influence tactics, whereas the opposite was found for low need for cognitive closure individuals (Pierro, Kruglanski, & Raven, 2012). Taken together, the studies summarized here provide evidence that Proposition 3 of the SMLB applies beyond transformational and transactional leadership behaviors and regulatory focus. As not all of the summarized research was designed to test the model, it is not surprising that there are still some gaps to be filled by additional empirical evidence. The key issues future research should address, in our opinion, are that (a) most current evidence is correlational and cross-sectional and (b) it is important to document that regulatory fit actually mediates the effect. Proposition 2 suggests that leaders signal to followers what they expect of them, more specifically, how they expect followers to pursue goals. Evidence for this proposition beyond transformational/transactional leadership behavior and regulatory focus is scarce. One of the rare examples comes from literature on authentic leadership, a style of leadership that is characterized by the leader’s showing awareness of his or her strengths and weaknesses and an active approach toward improving his or her effectiveness in leading followers (Avolio & Gardner, 2005). Mehmood, Nawab, & Hamstra (2016; see also Mehmood, Hamstra, Nawab, & Vriend, in press) argued and found that such leadership behavior encourages followers to build competence by

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pursuing mastery-related goals, characterized by the desire to improve and to develop one’s task-related competence. Hence, leadership characterized by an active approach toward improvement of one’s strengths and weaknesses predicted the same type of motivation in followers. Given that this is the only evidence for Proposition 2 beyond our work on regulatory focus and the two related leadership styles, there is a gap regarding this proposition that clearly calls for empirical endeavors.

4. DISCUSSION AND CONCLUSION 4.1 Summary of SMLB and Its Application to Regulatory Focus The SMLB articulates three propositions (see Fig. 5). It suggests (I) the type of leadership behavior a leader shows depends on the leaders’ dominant motivational state or self-regulatory orientation. This is because leadership behavior is goal-pursuit behavior, and a certain style of leadership behavior is, in essence, a self-regulation strategy applied in this context. This strategy should—like any other self-regulation strategy—be determined by the actors’ self-regulatory state. (II) Each style of leadership behavior encourages followers to opt for a certain self-regulation strategy, because leaders serve as role models and, by their organizational standing, are entitled to communicate expectations. (III) The impact of these encouragements—leaders’ social influence on followers—depends on the fit between the encouraged strategy and followers’ own self-regulatory preference. In case of a correspondence between the two, followers will experience regulatory fit, which will lead to higher task involvement, more positive evaluations of the leader, and potentially even generalize beyond the immediate context to the organization. Applied to the relation between promotion and prevention focus, on the one hand, and transformational and transactional leadership, on the other hand, these propositions suggest that leaders’ regulatory focus will determine Leader

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their leadership behavior—transformational in a promotion focus and transactional in a prevention focus—which encourages followers to apply the same self-regulation strategy as their leader holds. Due to the regulatory fit principle, leaders are particularly likely to successfully lead those followers who share their (i.e., the leaders’) regulatory focus. The research summarized earlier has provided clear evidence for all these predictions. By the application of a combination of experimental lab and field research, we were able to provide externally and internally valid evidence for all predictions of the SMLB regarding regulatory focus. Future research might add longitudinal studies to broaden the evidence for the SMLB.

4.2 The Application of the SMLB Beyond Regulatory Focus The three abstract propositions of the SMLB should also apply to other types of leadership behavior and other self-regulation models. As indicated in the summaries earlier, there is direct evidence for this regarding regulatory mode (Higgins, Kruglanski, & Pierro, 2003) and need for cognitive closure (Kruglanski & Webster, 1996). Leaders in an assessment mode have a preference for a controlling, monitoring leadership style, whereas leaders with a strong locomotion mode prefer autonomy-supporting leadership styles, in both cases because the leadership style is the strategy naturally arising from the respective regulatory mode (supporting Proposition 1). At the same time, followers with a strong assessment mode prefer leaders to show an advisory style, whereas followers with a strong locomotion mode prefer leaders to show a forceful leadership style. In both cases, followers show positive outcomes in response to their preferred leadership style, presumably because an advisory leadership style encourages followers to pursue goals in an assessment mode, and a forceful style encourages leaders to pursue goals in a locomotion mode (as suggested by Proposition 2). Hence, the effect on followers is likely to result from regulatory fit (in line with Proposition 3). Notably, even though all propositions are supported by these data, the overall pattern differs considerably from the findings on regulatory focus, transformational, and transactional leadership. Leaders and members with the same regulatory focus match, implying more social influence of the leader on the followers. However, in case of regulatory mode, leaders and followers with the same regulatory mode seem to be in favor of different leadership styles. Leaders in a locomotion mode prefer an autonomysupporting style, whereas followers in a locomotion mode prefer to be

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led with a forceful style. Moreover, leaders in an assessment mode are more likely to use a controlling, monitoring leadership style, whereas followers in this regulatory mode prefer an advisory leadership style. Hence, leaders and followers with the same regulatory modes do not match very well. In contrast, leaders and followers with a similar need for closure should (as in case of regulatory focus) match better than leaders and followers differing in their need for cognitive closure. Both prefer more hard (over soft) tactics, the stronger their need for cognitive closure is. In sum, there is first evidence for Proposition 1 (i.e., leadership behavior depends on leaders’ regulatory preferences) and Proposition 3 of the SMLB (i.e., the fit of leader behavior and followers’ preferred self-regulation strategies leads to positive outcomes). Proposition 2 (i.e., leadership behavior encourages certain self-regulatory strategies among followers) beyond regulatory focus has not yet been tested for regulatory mode and for need for cognitive closure, but the existing evidence for Proposition 3 uses arguments that are structurally similar to Proposition 2. Different patterns of results have been found for regulatory focus and need for cognitive closure, on the one hand, and regulatory mode, on the other hand. In the former case, leaders and followers sharing the same motivational states match, whereas in the latter case, leaders and followers with the same regulatory preferences do not match very well. At this point, we can only speculate about the sources of these divergent patterns. One reason might be that the behaviors that have, for leaders and followers, a regulatory fit with a motivational state have different implications for the leader–follower relationship. The preference for harsher leadership behavior among those (leaders and followers) with a strong need for cognitive closure results, in both cases, from a preference for low ambiguity about what to do—independently of whether this concerns own or others’ actions, both want cognitive closure. In contrast, the tendency of leaders pursuing their goals in an assessment mode to show controlling leadership behavior results from the strategy to evaluate the situation at hand that is associated with an assessment mode. Followers in the same regulatory mode, likewise, experience regulatory fit if they are encouraged to evaluate the situation. Thus, leaders and followers have a preference for evaluating potential action, performance, and task progress. In a leader–follower relationship, this might become conflictual, if both leaders and followers want to be (in charge of ) evaluating followers’ action. According to this reasoning, leaders and followers with the same regulatory preferences might be a less effective combination if

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the regulatory preferences in question have implications for the question “Who is in charge of what?” in the leadership process, and both leaders and followers claim control over the same aspect. Importantly, even when different self-regulation strategies lead to a better match, the mediating role of the follower behavior or strategy encouraged by the leadership behavior (i.e., Proposition 2) still holds. Leaders showing a controlling leadership style encourage followers to pursue the goals they set, and not to claim too much autonomy. Followers with a low interest in autonomy will be more likely to experience regulatory fit with these encouraged strategies and, thus, be more influenced by these leaders.

4.3 Avenues for Future Research This summary and the discussion of the research findings beyond regulatory focus clearly indicate questions that require further research. First, the propositions of the SMLB should be more systematically studied in the context of other self-regulation strategies beyond regulatory focus, in particular Proposition 2 of the SMLB. Besides regulatory mode and need for cognitive closure, for which initial studies already exist, other candidates might be approach and avoidance motivation (Carver & Scheier, 1990; Lewin, 1935) or state and action orientation (Kuhl, 1981). Second, research should not only aim to derive predictions concerning separate self-regulation strategies; it should also try to generate an overall picture of the conditions under which leaders and members holding the same or a different motivational state match better. Third, it has been questioned whether the four components of transformational leadership should really be treated as one concept, rather than separate components—among other things because a clear conceptual definition of transformational leadership is lacking, whereas the four components are well defined (van Knippenberg & Sitkin, 2013). Indeed, research has provided initial evidence that the separate components of transformational leadership behavior might resonate differently with followers, depending on their motivation. Followers’ affiliation motive predicted a preference for individualized consideration, their power motive predicted a preference for inspirational motivation, and their achievement motive predicted a preference for intellectual stimulation (Kehr, Amann, & Giessner, 2016). This suggests that further research should not treat transformational leadership, and perhaps also not transactional leadership, as unified concepts but rather consider the separate dimensions.

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Furthermore, the relations between the styles of leadership behavior and self-regulatory approaches might not be limited to the links we addressed earlier. Initial evidence for a connection between transformational and transactional leadership behaviors and regulatory mode already exists (Benjamin & Flynn, 2006). Finally, other styles of leadership behaviors beyond transformational and transactional that are well established in leadership research, such as ethical leadership (Brown & Trevin˜o, 2006) or servant leadership (Van Dierendonck, 2011), should also be considered.

4.4 The Relation Between the SMLB and the Conclusions in the Historic Overview How does the SMLB relate to the four key insights we derived in our historical overview of leadership research presented earlier? First, interindividual differences of leaders are included, because self-regulation strategies (also) vary chronically. Using self-regulation strategies that are more fine grained than personality traits seems to improve the predictive value of interindividual differences. Hence, they are generally suitable to be employed as criteria in leader selection. An organization searching for a transformational leader is, for instance, more likely to find the desired candidate among those with a chronic promotion focus. Second, self-regulation strategies are suitable for the prediction of certain leadership behaviors, in particular transformational and transactional leadership behaviors. This was one of our goals, because certain leadership behaviors, such as transformational and transactional leadership behaviors, had been shown to predict leadership success. Third, the SMLB is compatible with a contingency approach, but focusing on leader–follower contingencies rather than leader–task or leader–organization contingencies. Finally, the SMLB addresses both the goal-pursuit component and the social influence component of leadership behavior. It specifies how goal-pursuit processes affect the intrapersonal and interpersonal dynamics of leadership, and it contributes to the understanding of the social influence processes that are crucial for successful leadership. Taken together, the SMLB encompasses all desiderata specified in the theoretical section and, thus, advances leadership research in a way derived from and adding to earlier research. At the same time, it adds original elements by introducing a self-regulation perspective into leadership research and by considering both goal-pursuit and social influence components based on this perspective. The contributions to and implications for leadership research are outlined in the next section in a more detailed fashion.

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4.5 Contributions to and Implications for Leadership Research The SMLB assumes, and the summarized research has demonstrated, that considering leaders’ self-regulatory preferences adds precision to the prediction of leadership behavior—that is, the question of when and why a leader may be motivated to show a specific leadership behavior. Thus, approaching leaders as motivated individuals seems to be a promising approach. This is also in line with evidence from work on the motivation to lead (e.g., Chan & Drasgow, 2001; McClelland & Boyatzis, 1982; Winter, 1991). Future leadership research should also consider other aspects related to motivation, such as the leader’s personal goals, needs, and emotions. Not only leaders’ actual motivation, but also the motivation and meaning followers assume to underlie leadership behavior can contribute to the understanding of the leadership process. The research summarized here has clearly demonstrated that followers do interpret their leaders’ behavior (e.g., as an encouragement of certain strategic behaviors) and that followers act based on these interpretations—in other words, the interpersonal dynamic between leaders’ behavior and followers’ interpretation (and resulting behavior) needs to be taken into account. Hence, including followers’ interpretation of leadership behavior, which has been done very rarely so far, provides, again, a new avenue for leadership research. Leadership research should accordingly rely on a more interpersonal approach. This is also what distinguishes leadership research from power research, a field that has recently seen a boost in social psychology—with a strong focus on self-regulatory aspects (for theoretical contributions, see Guinote, 2007; Keltner, Gruenfeld, & Anderson, 2003; Magee & Smith, 2013). Power research studies the impact of holding power on the cognition and behavior of the powerful. In that research, those low in power are typically considered only as comparison standards or as those affected by the action of the powerful. Their own responses are rarely considered. In contrast, in leadership research, the actions and responses of those low in power (i.e., the followers) are a crucial element of the context. Leadership and power are distinct topics, but the fact that both deal with hierarchies and both profit from taking a self-regulation perspective should encourage researchers to consider the connections between the two fields more extensively and directly. We have started to examine this link by bringing together research on the antecedents of power and transformational leadership behavior (Sassenberg et al., 2007, 2013). This is, however, only a first step; an integrative effort to combine the two topics is likely to be highly productive.

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The social influence aspect of the SMLB (and, in particular, Proposition 3) suggests that the success of leadership behavior should be contingent on its fit with members’ motivational state or self-regulation preference. Thus, leadership research should (re)turn to a contingency approach (Fiedler, 1965) that considers both the leader and the followers. Including also the context might even further improve the precision of predictions (e.g., help to understand under which conditions specific leadership behaviors are more effective). There might, for instance, be situations or sectors where a certain leadership style is particularly appropriate (e.g., transformational leadership in marketing, because visions are important in this job domain).

4.6 Contributions to Self-Regulation Research The SMLB adds to a growing body of research applying self-regulation approaches, especially regulatory focus theory, to the interpersonal domain, including phenomena such as social influence (Lee & Aaker, 2004), perspective taking (Sassenrath et al., 2014), role models (Lockwood et al., 2002), close relationships (Righetti et al., 2011), and many others. Beyond showing the impact of self-regulation strategies and intrapersonal regulatory fit effects in yet another domain, the current research also adds a new aspect, namely interpersonal regulatory fit effects. Earlier research mostly focused on the regulatory fit between the person and certain aspects of the environment (e.g., persuasive communication). An exception in this respect is the work on close relationships by Righetti et al. (2011), who found that promotion-focused individuals’ goal pursuit was facilitated by partners sharing their regulatory focus. Importantly, the SMLB research summarized in this chapter is the first to predict and find symmetrical interpersonal regulatory fit effects across both dimensions of a self-regulation approach (i.e., promotion and prevention foci). SMLB research has also explored some of the interpersonal dynamics resulting from interpersonal regulatory fit effects. Our research suggests that the experience that something “feels right” can spread from interpersonal regulatory fit to the action itself, to goal engagement, and to leader evaluation. In additional research, we have shown that regulatory fit affects employees’ relation to their organization (Hamstra, Sassenberg, Van Yperen, Wisse, & Rietzschel, 2015; Sassenberg & Scholl, 2013). Given these broad effects, regulatory fit seems to be a lubricant for leadership influence and other dynamic social processes. Going from cross-sectional data to the assessment of time series effects, allowing the direct investigation of

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interpersonal and group dynamics, should provide additional insights about the effects of interpersonal regulatory fit. Another contribution to self-regulation research lies in the fact that our work brings together research applying different self-regulation approaches to one social phenomenon. Whereas regulatory fit theory (Higgins, 2000) was developed as an approach applicable across different self-regulation strategies, to the best of our knowledge, we are the first to investigate numerous self-regulation approaches (regulatory focus, regulatory mode, need for closure) applied to one phenomenon (i.e., leadership behavior). Such an integrative effort would certainly also be useful beyond leadership behavior.

4.7 Implications for Organizations For organizations, the SMLB first and foremost highlights the importance of considering self-regulation strategies. When intending to implement a certain leadership style, selecting leaders with the matching self-regulation strategy and creating conditions for leaders that are likely to induce that selfregulation style provide viable means to initiate and uphold organizational change. Furthermore, the implementation of a new leadership style will only foster certain beneficial outcomes if it fits the followers’ regulatory preferences—either those they hold chronically or those that are resulting from the organizational context. In other words, self-regulatory strategies can form the binding element of an organizational strategy, including personnel selection, training, and the design of the organizational context. Although this reasoning might, at first glance, suggest that an organization would be more successful if all employees held the one self-regulation strategy that is endorsed by the organizational context, this conclusion is in most cases likely to be wrong. Different self-regulation strategies facilitate performance on different tasks. A promotion focus facilitates, for instance, creativity, whereas a prevention focus eases deductive thinking (Friedman & F€ orster, 2005). Organizational success certainly requires performing a variety of different tasks and, thus, presumes the application of a variety of selfregulatory strategies. There is, indeed, evidence that couples and task groups with members holding different self-regulatory strategies show more positive outcomes (e.g., more well-being and better performance, Bohns et al., 2013; Mauro, Pierro, Mannetti, Higgins, & Kruglanski, 2009). The different strategies seem to allow for complementary contributions (Higgins, 2011). In line with this notion, organizational success might require that employees have

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opportunities to apply these different strategies—and, according to the SMLB, successful leadership in such a context requires that leaders also have the flexibility to encourage different strategies. Even within one leader’s team, the tasks associated with different roles might be performed better if different self-regulation strategies are applied. Hence, members with different self-regulation strategies will perform better on different jobs. This also suggests that different leadership behaviors are likely to increase leaders’ social influence on different members of a team. These leaders should switch between their self-regulation strategies when interacting with different team members. This might suggest (in line with the findings by Pierro, Kruglanski, & Raven, 2012, summarized earlier) that leaders high more than one self-regulation strategy are more successful— which is technically possible, because self-regulatory dimensions such as locomotion and assessment mode or promotion and prevention focus are usually independent of each other. Leaders who are better able to switch between regulatory strategies might achieve better outcomes within their teams. These speculations should be tested in further research, because most research on self-regulation dualities has not tested the implications of being high in both strategies (e.g., in locomotion and assessment), in particular not in relation to regulatory fit, even though the data would have allowed for it by entering the interaction effect between the two strategies into the analysis. Therefore, future research should consider focusing on the (potentially beneficial) effects of the presence of both strategies (i.e., locomotion and assessment mode or promotion and prevention focus), because these findings would not only be of scientific but also of applied relevance. There are, however, also teams or departments (but less likely organizations) in which the predominant application of one self-regulation strategy is likely to lead to success. For example, marketing requires creativity that is supported by a promotion focus, whereas accounting requires precision that is supported by a prevention focus. Leadership behavior and personnel’s dominant self-regulation strategies fitting the team’s tasks should— according the SMLB—facilitate the performance of these teams. Interestingly, research has shown that attraction to jobs and selection of personnel are also influenced by regulatory fit (Hamstra, Bolderdijk, & Veldstra, 2011; Hamstra, Van Yperen, et al., 2011; Hamstra et al., 2013; Sassenberg & Scholl, 2013). Applicants are more likely to apply to a job fitting their self-regulatory preferences. Likewise, applicants who display in their application the same self-regulation strategy held by the person doing the hiring evoke more interest than applications using a self-regulation

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strategy not held by the selecting person. Hence, there is some likelihood that interpersonal regulatory fit between leaders and followers will occur without any intervention—which is good for the social influence of leaders and for teams with homogenous jobs, but might be problematic if job performance is facilitated by regulatory heterogeneity. Finally, in organizational contexts, the regulatory fit between leaders’ preferred strategies and the strategies demanded by the organization (or preferred by followers) might be important. Leaders should be more motivated and engaged when their own strategies are required; they might also have lower turn-over intentions. These regulatory fit experiences of leaders should receive attention both in organizations and scholarly research. The SMLB highlights the relevance of social psychology for leadership research and vice versa. Our approach utilizes state-of-the-art theorizing on basic motivational processes (i.e., self-regulation) to facilitate the understanding of intrapersonal and interpersonal dynamics. In doing so, the model extends self-regulation to the domain of leadership and to interpersonal processes, thus connecting intrapersonal and interpersonal mechanisms in leadership within an integrative self-regulation model. At a more concrete level, the SMLB contributes to the understanding (and prediction) of what motivates leaders to behave in a certain way and which followers are influenced by leaders’ behavior. Furthermore, the model can be applied to several selfregulation approaches and types of leadership behavior. As such, this approach may provide a general framework for research on other interpersonal phenomena and group dynamics.

APPENDIX Sample item from the Problems at School Questionnaire (Deci et al., 1981). Jim is an average student who has been working at grade level. During the past 2 weeks, he has appeared listless and has not been participating during reading group. The work he does is accurate, but he has not been completing assignments. A phone conversation with his mother revealed no useful information. The most appropriate thing for Jim’s teacher to do is: Highly autonomy supportive: Let him know that he does not have to finish all of his work now and see if she can help him work out the cause of the listlessness.

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Moderately autonomy supportive: Let him see how he compares with the other children in terms of his assignments and encourage him to catch up with the others. Moderately controlling: She should impress upon him the importance of finishing his assignments since he needs to learn this material for his own good. Highly controlling: Make him stay after school until the day’s assignments are done.

ACKNOWLEDGMENTS This research was supported by a VIDI grant from the Netherlands Organisation for Scientific Research (NWO) awarded to Kai Sassenberg, grant number 452-07-006. The authors would like to thank Annika Scholl and Johannes Keller for their input on earlier versions of this manuscript and Nico W. Van Yperen and Barbara Wisse for their input in many discussions on the research and papers summarized here.

REFERENCES Aryee, S., Chen, Z. X., Sun, L. Y., & Debrah, Y. A. (2007). Antecedents and outcomes of abusive supervision: Test of a trickle-down model. Journal of Applied Psychology, 92, 191–201. http://dx.doi.org/10.1037/0021-9010.92.1.191. Avnet, T., & Higgins, E. T. (2003). Locomotion, assessment, and regulatory fit: Value transfer from “how” to “what”. Journal of Experimental Social Psychology, 39, 525–530. http:// dx.doi.org/10.1016/S0022-1031(03)00027-1. Avnet, T., Laufer, D., & Higgins, E. T. (2013). Are all experiences of fit created equal? Two paths to persuasion. Journal of Consumer Psychology, 23, 301–316. http://dx.doi.org/ 10.1016/j.jcps.2012.10.011. Avolio, B. J., & Gardner, W. L. (2005). Authentic leadership development: Getting to the root of positive forms of leadership. The Leadership Quarterly, 16, 315–338. http://dx.doi. org/10.1016/j.leaqua.2005.03.001. Bales, R. F. (1950). A set of categories for the analysis of small group interaction. American Sociological Review, 15, 257–263. http://dx.doi.org/10.2307/2086790. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. http://dx.doi.org/10.1037/0033-295X.84.2.191. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. NJ: Englewood Cliffs. Barbuto, J. E. (2005). Motivation and transactional, charismatic, and transformational leadership: A test of antecedents. Journal of Leadership and Organizational Studies, 11, 26–40. http://dx.doi.org/10.1177/107179190501100403. Bardes Mawritz, M. B., Mayer, D. M., Hoobler, J. M., Wayne, S. J., & Marinova, S. V. (2012). A trickle-down model of abusive supervision. Personnel Psychology, 65, 325–357. http://dx.doi.org/10.1111/j.1744-6570.2012.01246.x. Bass, B. M. (1985). Leadership and performance beyond expectations. New York: Free Press. Bass, B. M., & Avolio, B. J. (1995). MLQ multifactor leadership questionnaire for research. Redwood City, CA: Mind Garden.

248

K. Sassenberg and M.R.W. Hamstra

Bass, B. M., & Steidlmeier, P. (1999). Ethics, character, and authentic transformational leadership behavior. The Leadership Quarterly, 10, 181–217. http://dx.doi.org/10.1016/ S1048-9843(99)00016-8. Bass, B. M., Waldman, D. A., Avolio, B. J., & Bebb, M. (1987). Transformational leadership and the falling dominoes effect. Group & Organization Management, 12, 73–87. http://dx. doi.org/10.1177/105960118701200106. Belanger, J. J., Pierro, A., Barbieri, B., De Carlo, N. A., Falco, A., & Kruglanski, A. W. (2016). One size doesn’t fit all: The influence of supervisors’ power tactics and subordinates’ need for cognitive closure on burnout and stress. European Journal of Work and Organizational Psychology, 25, 287–300. http://dx.doi.org/10.1080/1359432X.2015.1061999. Belanger, J. J., Pierro, A., & Kruglanski, A. W. (2015). Social power tactics and subordinates’ compliance at work: The role of need for cognitive closure. Revue Europeenne de Psychologie Appliquee/European Review of Applied Psychology, 65, 163–169. http://dx.doi.org/ 10.1016/j.erap.2015.05.001. Benjamin, L., & Flynn, F. J. (2006). Leadership style and regulatory mode: Value from fit? Organizational Behavior and Human Decision Processes, 100, 216–230. http://dx.doi.org/ 10.1016/j.obhdp.2006.01.008. Berson, Y., Shamir, B., Avolio, B. J., & Popper, M. (2001). The relationship between vision strength, leadership style, and context. The Leadership Quarterly, 12, 53–73. http://dx.doi. org/10.1016/S1048-9843(01)00064-9. Bohns, V. K., Lucas, G. M., Molden, D. C., Finkel, E. J., Coolsen, M. K., Kumashiro, M., … Higgins, E. T. (2013). Opposites fit: Regulatory focus complementarity and relationship well-being. Social Cognition, 31, 1–14. http://dx.doi.org/10.1521/soco.2013.31.1.1. Bombari, D., Schmid, P. C., Schmid Mast, M., Birri, S., Mast, F. W., & Lobmaier, J. S. (2013). Emotion recognition: The role of featural and configural face information. The Quarterly Journal of Experimental Psychology, 66, 2426–2442. http://dx.doi.org/ 10.1080/17470218.2013.789065. Bono, J. E., & Judge, T. A. (2004). Personality and transformational and transactional leadership: A meta-analysis. Journal of Applied Psychology, 89, 901–910. http://dx.doi.org/ 10.1037/0021-9010.89.5.901. Brendl, C. M., & Higgins, E. T. (1996). Principles of judging valence: What makes events positive or negative? Advances in Experimental Social Psychology, 28, 95–160. http://dx.doi. org/10.1016/S0065-2601(08)60237-3. Brodscholl, J. C., Kober, H., & Higgins, E. T. (2007). Strategies of self-regulation in goal attainment versus goal maintenance. European Journal of Social Psychology, 37, 628–648. http://dx.doi.org/10.1002/ejsp.380. Brown, M. E., & Trevin˜o, L. K. (2006). Ethical leadership: A review and future directions. The Leadership Quarterly, 17, 595–616. http://dx.doi.org/10.1016/j.leaqua.2006.10.004. Brown, M. E., Trevin˜o, L. K., & Harrison, D. A. (2005). Ethical leadership: A social learning perspective for construct development and testing. Organizational Behavior and Human Decision Processes, 97, 117–134. http://dx.doi.org/10.1016/j.obhdp.2005.03.002. Burns, J. M. (1978). Leadership. New York, NY: Harper & Row. Carver, C. S., & Scheier, M. F. (1990). Origins and functions of positive and negative affect: A control-process view. Psychological Review, 97, 19–35. http://dx.doi.org/10.1037/0033295X.97.1.19. Cesario, J., Grant, H., & Higgins, E. T. (2004). Regulatory fit and persuasion: Transfer from feeling right. Journal of Personality and Social Psychology, 86, 388–404. http://dx.doi.org/ 10.1037/0022-3514.86.3.388. Chan, K. Y., & Drasgow, F. (2001). Toward a theory of individual differences and leadership: Understanding the motivation to lead. Journal of Applied Psychology, 86, 481–498. http:// dx.doi.org/10.1037/0021-9010.86.3.481.

A Self-Regulation Approach to Leadership

249

Choi, Y., & Mai-Dalton, R. R. (1999). The model of followers’ responses to self-sacrificial leadership: An empirical test. The Leadership Quarterly, 10, 397–421. http://dx.doi.org/ 10.1016/S1048-9843(99)00025-9. Conger, J. A., & Kanungo, R. N. (1987). Toward a behavioral theory of charismatic leadership in organizational settings. Academy of Management Review, 12, 637–647. http://dx. doi.org/10.5465/AMR.1987.4306715. Crowe, E., & Higgins, E. T. (1997). Regulatory focus and strategic inclinations: Promotion and prevention in decision-making. Organizational Behavior and Human Decision Processes, 69, 117–132. http://dx.doi.org/10.1006/obhd.1996.2675. Day, D. V. (Ed.), (2014). The Oxford Handbook of leadership and organizations. In New York, NY: Oxford University Press. http://dx.doi.org/10.1093/oxfordhb/ 9780199755615.001.0001. Deci, E. L., Schwartz, A. J., Sheinman, L., & Ryan, R. M. (1981). An instrument to assess adults’ orientations toward control versus autonomy with children: Reflections on intrinsic motivation and perceived competence. Journal of educational Psychology, 73, 642–650. http://dx.doi.org/10.1037/0022-0663.73.5.642. De Hoogh, A. H. B., Den Hartog, D. N., & Koopman, P. L. (2004). De ontwikkeling van de CLIO: een vragenlijst voor charismatisch leiderschap in organisaties [The development von CLIO: A questionnaire for charismtic leadership in organizations]. Gedrag en Organisatie, 17, 354–381. De Hoogh, A. H. B., Den Hartog, D. N., Koopman, P. L., Thierry, H., Van den Berg, P. T., Van der Weide, J. G., & Wilderom, C. P. M. (2005). Leader motives, charismatic leadership, and subordinates’ work attitude in the profit and voluntary sector. The Leadership Quarterly, 16, 17–38. http://dx.doi.org/10.1016/j.leaqua.2004.10.001. Den Hartog, D. N., Van Muijen, J. J., & Koopman, P. L. (1997). Transactional versus transformational leadership: An analysis of the MLQ. Journal of Occupational and Organizational Psychology, 70, 19–34. http://dx.doi.org/10.1111/j.2044-8325.1997. tb00628.x. Ehrhart, M. G., & Klein, K. J. (2001). Predicting followers’ preferences for charismatic leadership: The influence of follower values and personality. The Leadership Quarterly, 12, 153–179. http://dx.doi.org/10.1016/S1048-9843(01)00074-1. Fiedler, F. E. (1965). A contingency model of leadership effectiveness. In L. Berkowitz (Ed.), Advances in experimental social psychology: Vol. 1 (pp. 149–190). New York: Academic. Fleishman, E. (1953). The description of supervisory behavior. Journal of Applied Psychology, 37, 1–6. http://dx.doi.org/10.1037/h0056314. Ford, J. D., & Ford, L. W. (1994). Logics of identity, contradiction, and attraction in change. Academy of Management Review, 19, 756–785. F€ orster, J., & Dannenberg, L. (2010). GLOMOsys: A systems account of global versus local processing. Psychological Inquiry, 21, 175–197. http://dx.doi.org/10.1080/1047840X. 2010.487849. F€ orster, J., & Higgins, E. T. (2005). How global versus local perception fits regulatory focus. Psychological Science, 16, 631–636. http://dx.doi.org/10.1111/j.1467-9280.2005. 01586.x. Freitas, A. L., & Higgins, E. T. (2002). Enjoying goal-directed action: The role of regulatory fit. Psychological Science, 13, 1–6. http://dx.doi.org/10.1111/1467-9280.00401. Friedman, R. S., & F€ orster, J. (2001). The effects of promotion and prevention cues on creativity. Journal of Personality and Social Psychology, 81, 1001–1013. http://dx.doi.org/ 10.1037/0022-3514.81.6.1001. Friedman, R. S., & F€ orster, J. (2005). Effects of motivational cues on perceptual asymmetry: Implications for creativity and analytical problem solving. Journal of Personality and Social Psychology, 88, 263–275. http://dx.doi.org/10.1037/0022-3514.88.2.263.

250

K. Sassenberg and M.R.W. Hamstra

Galinsky, A. D., Leonardelli, G. J., Okhuysen, G. A., & Mussweiler, T. (2005). Regulatory focus at the bargaining table: Promoting distributive and integrative success. Personality and Social Psychology Bulletin, 31, 1087–1098. http://dx.doi.org/10.1177/ 0146167205276429. Gu, J., Bohns, V. K., & Leonardelli, G. J. (2013). Regulatory focus and interdependent economic decision-making. Journal of Experimental Social Psychology, 49, 692–698. http://dx. doi.org/10.1016/j.jesp.2012.11.008. Guinote, A. (2007). Behaviour variability and the situated focus theory of power. European Review of Social Psychology, 18, 256–295. http://dx.doi.org/10.1080/10463280701692813. Hamstra, M. R. W., Bolderdijk, J. W., & Veldstra, J. L. (2011). Everyday risk taking as a function of regulatory focus. Journal of Research in Personality, 45, 134–137. http://dx. doi.org/10.1016/j.jrp.2010.11.017. Hamstra, M. R. W., Rietzschel, E. F., & Groeneveld, D. M. (2015). To go or not to go for the sell: Regulatory focus and personal sales performance. Journal of Personnel Psychology, 14, 109–112. http://dx.doi.org/10.1027/1866-5888/a000134. Hamstra, M. R. W., Sassenberg, K., Van Yperen, N. W., & Wisse, B. (2009). Why leaders behave as they do: A self-regulation approach to leadership styles. In Paper presented at the conference of the Dutch Community for researchers in Work and Organizational Psychology (WAOP), November 2009, Amsterdam. Hamstra, M. R. W., Sassenberg, K., Van Yperen, N. W., & Wisse, B. (2014). Followers feel valued—When leaders’ regulatory focus makes leaders exhibit behavior that fits followers’ regulatory focus. Journal of Experimental Social Psychology, 51, 34–40. http://dx. doi.org/10.1016/j.jesp.2013.11.003. Hamstra, M. R. W., Sassenberg, K., Van Yperen, N. W., Wisse, B., & Rietzschel, E. F. (2015). Regulatory fit buffers against disidentification from groups. Motivation Science, 1, 184–201. http://dx.doi.org/10.1037/mot0000019. Hamstra, M. R. W., Van Yperen, N. W., Wisse, B., & Sassenberg, K. (2011). Transformational–transactional leadership styles and followers’ regulatory focus: Fit reduces followers’ turnover intentions. Journal of Personnel Psychology, 10, 182–186. http://dx.doi. org/10.1027/1866-5888/a000043. Hamstra, M. R. W., Van Yperen, N. W., Wisse, B., & Sassenberg, K. (2013). Like or dislike: Intrapersonal regulatory fit affects the intensity of interpersonal evaluation. Journal of Experimental Social Psychology, 49, 726–731. http://dx.doi.org/10.1016/j. jesp.2013.03.002. Hamstra, M. R. W., Van Yperen, N. W., Wisse, B., & Sassenberg, K. (2014). On the perceived effectiveness of transformational–transactional leadership: The role of encouraged strategies and followers’ regulatory focus. European Journal of Social Psychology, 44, 643–656. http://dx.doi.org/10.1002/ejsp.2027. Hamstra, M. R. W., Van Yperen, N. W., Rietzschel, E. F., Wisse, B., & Sassenberg, K., (unpublished data). The influence of leaders’ regulatory focus and leadership styles on team eagerness and vigilance. University of Maastricht, The Netherlands. Hanges, P. J., & Dickson, M. W. (2004). The development and validation of the Globe culture and leadership scales. In R. J. House, P. J. Hanges, M. Javidan, P. W. Dorfman, & V. Gupta (Eds.), Culture, leadership, and organizations: The GLOBE study of 62 societies: Vol. 1. (pp. 205–218). Thousand Oaks, CA: Sage. Haslam, S. A., Reicher, S. D., & Platow, M. J. (2015). Leadership: Theory and practice. In M. Mikulincer, P. R. Shaver, J. F. Dovidio, & J. A. Simpson (Eds.): Vol. 2. (pp. 67–94). Washington, DC: US: American Psychological Association. http://dx. doi.org/10.1037/14342-003. group processes. Hazlett, A., Molden, D. C., & Sackett, A. M. (2011). Hoping for the best or preparing for the worst? Regulatory focus and preferences for optimism and pessimism in predicting

A Self-Regulation Approach to Leadership

251

personal outcomes. Social Cognition, 29, 74–96. http://dx.doi.org/10.1521/ soco.2011.29.1.74. Henker, N., Sonnentag, S., & Unger, D. (2015). Transformational leadership and employee creativity: The mediating role of promotion focus and creative process engagement. Journal of Business and Psychology, 30, 235–247. http://dx.doi.org/10.1007/s10869014-9348-7. Henrich, J., & Gil-White, F. J. (2001). The evolution of prestige: Freely conferred deference as a mechanism for enhancing the benefits of cultural transmission. Evolution and Human Behavior, 22, 165–196. http://dx.doi.org/10.1016/S1090-5138(00)00071-4. Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 1280–1300. http://dx.doi.org/10.1037/0003-066X.52.12.1280. Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55, 1217–1230. http://dx.doi.org/10.1037/0003-066X.55.11.1217. Higgins, E. T. (2005). Value from regulatory fit. Current Directions in Psychological Science, 14, 209–213. http://dx.doi.org/10.1111/j.0963-7214.2005.00366.x. Higgins, E. T. (2011). Beyond pleasure and pain: How motivation works. Oxford University Press: Oxford, UK. Higgins, E. T., Friedman, R. S., Harlow, R. E., Idson, L. C., Ayduk, O. N., & Taylor, A. (2001). Achievement orientations from subjective histories of success: Promotion pride versus prevention pride. European Journal of Social Psychology, 31, 3–23. http://dx.doi.org/ 10.1002/ejsp.27. Higgins, E. T., Idson, L. C., Freitas, A. L., Spiegel, S., & Molden, D. C. (2003). Transfer of value from fit. Journal of Personality and Social Psychology, 84, 1140–1153. http://dx.doi. org/10.1037/0022-3514.84.6.1140. Higgins, E. T., Kruglanski, A. W., & Pierro, A. (2003). Regulatory mode: Locomotion and assessment as distinct orientations. In M. P. Zanna (Ed.), Advances in experimental social psychology: Vol. 35. (pp. 293–344). San Diego, CA: Elsevier Academic Press. http:// dx.doi.org/10.1016/S0065-2601(03)01005-0. Higgins, E. T., Roney, C., Crowe, E., & Hymes, C. (1994). Ideal versus ought predilections for approach and avoidance: Distinct self-regulatory systems. Journal of Personality and Social Psychology, 66, 276–286. http://dx.doi.org/10.1037/0022-3514.66.2.276. Higgins, E. T., & Silberman, I. (1998). Development of regulatory focus: Promotion and prevention as ways of living. In J. Heckhausen & C. S. Dweck (Eds.), Motivation and selfregulation across the life span (pp. 78–113). New York, NY: US: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511527869.005. Hogg, M. A., & van Knippenberg, D. (2003). Social identity and leadership processes in groups. Advances in Experimental Social Psychology, 35, 1–52. http://dx.doi.org/ 10.1016/S0065-2601(03)01001-3. House, R. J. (1971). A path-goal theory of leader effectiveness. Administrative Science Quarterly, 16, 321–338. http://dx.doi.org/10.2307/2391905. House, R. J. (1977). A 1976 theory of charismatic leadership. In J. G. Huns & L. L. Larson (Eds.), Leadership: The cutting edge. Carbondale, IL: Southern Illinois Press. House, R. J., & Aditya, R. N. (1997). The social scientific study of leadership: Quo vadis? Journal of Management, 23, 409–473. http://dx.doi.org/10.1177/014920639702300306. House, R. J., Spangler, W. D., & Woycke, J. (1991). Personality and charisma in the U.S. presidency: A psychological theory of leader effectiveness. Administrative Science Quarterly, 36, 364–396. http://dx.doi.org/10.2307/2393201. Howell, J. M., & Avolio, B. J. (1993). Transformational leadership, transactional leadership, locus of control, and support for innovation: Key predictors of consolidated-businessunit performance. Journal of Applied Psychology, 78, 891–902. http://dx.doi.org/ 10.1037/0021-9010.78.6.891.

252

K. Sassenberg and M.R.W. Hamstra

Idson, L. C., Liberman, N., & Higgins, E. T. (2000). Distinguishing gains from nonlosses and losses from nongains: A regulatory focus perspective on hedonic intensity. Journal of Experimental Social Psychology, 36, 252–274. http://dx.doi.org/10.1006/jesp.1999.1402. Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87, 765–780. http:// dx.doi.org/10.1037/0021-9010.87.4.765. Judge, T. A., Colbert, A. E., & Ilies, R. (2004). Intelligence and leadership: A quantitative review and test of theoretical propositions. Journal of Applied Psychology, 89, 542–552. http://dx.doi.org/10.1037/0021-9010.89.3.542. Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional leadership: A metaanalytic test of their relative validity. Journal of Applied Psychology, 89, 755–768. http://dx. doi.org/10.1037/0021-9010.89.5.755. Judge, T. A., Piccolo, R. F., & Ilies, R. (2004). The forgotten ones? The validity of consideration and initiating structure in leadership research. Journal of Applied Psychology, 89, 36–51. http://dx.doi.org/10.1037/0021-9010.89.1.36. Jung, D. I., Chow, C., & Wu, A. (2003). The role of transformational leadership in enhancing organizational innovation: Hypotheses and some preliminary findings. The Leadership Quarterly, 14, 525–544. http://dx.doi.org/10.1016/S1048-9843(03)00050-X. Kark, R., Katz-Navon, T., & Delegach, M. (2015). The dual effects of leading for safety: The mediating role of employee regulatory focus. Journal of Applied Psychology, 100, 1332–1348. http://dx.doi.org/10.1037/a0038818. Kark, R., & Van Dijk, D. (2007). Motivation to lead, motivation to follow: The role of the self-regulatory focus in leadership processes. Academy of Management Review, 32, 500–528. http://dx.doi.org/10.2307/20159313. Katz, D., & Kahn, R. L. (1952). Some recent findings in human relations research. In E. Swanson, T. Newcombe, & E. Hartley (Eds.), Readings in social psychology. NY: Holt. Kehr, H. M., Amann, D., & Giessner, S. (2016). Transformational leadership meets follower motives: Compatibility of dimensions of transformational leadership and follower motives determines work-related outcomes. In Paper presented at the Potsdam Leadership Symposium, April 2016, Potsdam, Germany. Keller, J. (2008). On the development of regulatory focus: The role of parenting styles. European Journal of Social Psychology, 38, 354–364. http://dx.doi.org/10.1002/ejsp.460. Keller, J., & Bless, H. (2006). Regulatory fit and cognitive performance: The interactive effect of chronic and situationally induced self-regulatory mechanisms on test performance. European Journal of Social Psychology, 36, 393–405. http://dx.doi.org/10.1002/ ejsp.307. Keller, J., & Bless, H. (2008). When positive and negative expectancies disrupt performance: Regulatory focus as a catalyst. European Journal of Social Psychology, 38, 187–212. http:// dx.doi.org/10.1002/ejsp.452. Keltner, D., Gruenfeld, D. H., & Anderson, C. (2003). Power, approach, and inhibition. Psychological Review, 110, 265–284. http://dx.doi.org/10.1037/0033-295X.110.2.265. Kruglanski, A. W. (1980). Lay epistemo-logic—Process and contents: Another look at attribution theory. Psychological Review, 87, 70–87. http://dx.doi.org/10.1037/0033295X.87.1.70. Kruglanski, A. W., Pierro, A., Higgins, E. T., & Capozza, D. (2007). “On the move” or “Staying put”: Locomotion, need for cognitive closure, and reactions to organizational change. Journal of Applied Social Psychology, 37, 1305–1340. http://dx.doi.org/10.1111/ j.1559-1816.2007.00214.x. Kruglanski, A. W., Thompson, E. P., Higgins, E. T., Atash, M., Pierro, A., Shah, J. Y., & Spiegel, S. (2000). To “do the right thing” or to “just do it”: locomotion and assessment as distinct self-regulatory imperatives. Journal of Personality and Social Psychology, 79, 793–815. http://dx.doi.org/10.1037/0022-3514.79.5.793.

A Self-Regulation Approach to Leadership

253

Kruglanski, A. W., & Webster, D. M. (1996). Motivated closing of the mind: “Seizing” and “freezing”. Psychological Review, 103, 263–283. http://dx.doi.org/10.1037/0033295X.103.2.263. Kuhl, J. (1981). Motivational and functional helplessness: The moderating effect of state versus action orientation. Journal of Personality and Social Psychology, 40, 155–170. http://dx.doi.org/10.1037/0022-3514.40.1.155. Kuhnert, K. W., & Lewis, P. (1987). Transactional and transformational leadership: A constructive/developmental analysis. Academy of Management Review, 12, 648–657. http://dx.doi.org/10.2307/258070. Lee, A. Y., & Aaker, J. L. (2004). Bringing the frame into focus: The influence of regulatory fit on processing fluency and persuasion. Journal of Personality and Social Psychology, 86, 205–218. http://dx.doi.org/10.1037/0022-3514.86.2.205. Lee, A. Y., Keller, P. A., & Sternthal, B. (2010). Value from regulatory construal fit: The persuasive impact of fit between consumer goals and message concreteness. Journal of Consumer Research, 36, 735–747. http://dx.doi.org/10.1086/605591. Lewin, K. (1935). A dynamic theory of personality. New York, NY: McGraw-Hill. Liberman, N., Idson, L. C., Camacho, C. J., & Higgins, E. T. (1999). Promotion and prevention choices between stability and change. Journal of Personality and Social Psychology, 77, 1135–1145. http://dx.doi.org/10.1037/0022-3514.77.6.1135. Liden, R. C., Wayne, S. J., Liao, C., & Meuser, J. D. (2014). Servant leadership and serving culture: Influence on individual and unit performance. Academy of Management Journal, 57, 1434–1452. http://dx.doi.org/10.5465/amj.2013.0034. Liu, D., Liao, H., & Loi, R. (2012). The dark side of leadership: A three-level investigation of the cascading effect of abusive supervision on employee creativity. Academy of Management Journal, 55, 1187–1212. http://dx.doi.org/10.5465/amj.2010.0400. Lockwood, P., Jordan, C. H., & Kunda, Z. (2002). Motivation by positive or negative role models: Regulatory focus determines who will best inspire us. Journal of Personality and Social Psychology, 83, 854–864. http://dx.doi.org/10.1037/0022-3514.83. 4.854. Lockwood, P., Sadler, P., Fyman, K., & Tuck, S. (2004). To do or not to do: Using positive and negative role models to harness motivation. Social Cognition, 22, 422–450. http://dx. doi.org/10.1521/soco.22.4.422.38297. Magee, J. C., & Smith, P. K. (2013). The social distance theory of power. Personality and Social Psychology Review, 17, 158–186. http://dx.doi.org/10.1177/1088868312472732. Markman, K. D., McMullen, M. N., Elizaga, R. A., & Mizoguchi, N. (2006). Counterfactual thinking and regulatory fit. Judgment and Decision Making, 1, 98–107. Masi, R. J., & Cooke, R. A. (2000). Effects of transformational leadership on subordinate motivation, empowering norms, and organizational productivity. International Journal of Organizational Analysis, 8, 16–47. http://dx.doi.org/10.1108/eb028909. Mauro, R., Pierro, A., Mannetti, L., Higgins, E. T., & Kruglanski, A. W. (2009). The perfect mix: Regulatory complementarity and the speed-accuracy balance in group performance. Psychological Science, 20, 681–685. http://dx.doi.org/10.1111/j.1467-9280.2009. 02363.x. Mayer, D. M., Kuenzi, M., Greenbaum, R., Bardes, M., & Salvador, R. B. (2009). How low does ethical leadership flow? Test of a trickle-down model. Organizational Behavior and Human Decision Processes, 108, 1–13. http://dx.doi.org/10.1016/j. obhdp.2008.04.002. McClelland, D. C., & Boyatzis, R. E. (1982). Leadership motive pattern and long-term success in management. Journal of Applied Psychology, 67, 737–743. http://dx.doi.org/ 10.1037/0021-9010.67.6.737. McClelland, D. C., & Burnham, D. (1976/2003). Power is the great motivator. Harvard Business Review, 54(100–110), 159–166.

254

K. Sassenberg and M.R.W. Hamstra

McClelland, D. C., & Burnham, D. H. (2003). Power is the great motivator. Harvard Business Review, 81, 117–123. Mehmood, Q., Hamstra, M. R. W., Nawab, S., & Vriend, T. (in press). Authentic leadership and followers’ in-role and extra-role performance: The mediating role of followers’ learning goal orientation. Journal of Occupational and Organizational Psychology. doi: 10.1111/joop.12153. Mehmood, Q., Nawab, S., & Hamstra, M. R. W. (2016). Does authentic leadership predict work engagement and in-role performance? Considering learning goal orientation’s role. Journal of Personnel Psychology, 15, 139–142. http://dx.doi.org/10.1027/1866-5888/ a000164. Miller, N. E., & Dollard, J. (1941). Social learning and imitation. New Haven, CT: US: Yale University Press. Molden, D. C., & Hui, C. M. (2011). Promoting de-escalation of commitment a regulatoryfocus perspective on sunk costs. Psychological Science, 22, 8–12. http://dx.doi.org/ 10.1177/0956797610390386. Morhart, F. M., Herzog, W., & Tomczak, T. (2009). Brand-specific leadership: Turning employees into brand champions. Journal of Marketing, 73, 122–142. http://dx.doi. org/10.1509/jmkg.73.5.122. Moss, S. (2009). Cultivating the regulatory focus of followers to amplify their sensitivity to transformational leadership. Journal of Leadership and Organizational Studies, 15, 241–259. http://dx.doi.org/10.1177/1548051808327880. Nowicki, S., & Duke, M. P. (2001). Nonverbal receptivity: The Diagnostic Analysis of Nonverbal Accuracy (DANVA). In S. Nowicki, M. P. Duke, J. A. Hall, & F. J. Bernieri (Eds.), Interpersonal sensitivity: Theory and measurement (pp. 183–198). Mahwah, NJ: US: Lawrence Erlbaum Associates Publishers. Oke, A., Munshi, N., & Walumbwa, F. O. (2009). The influence of leadership on innovation processes and activities. Organizational Dynamics, 38, 64–72. http://dx.doi.org/10.1016/ j.orgdyn.2008.10.005. Oyserman, D., Uskul, A. K., Yoder, N., Nesse, R., & Williams, D. (2007). Unfair treatment and self-regulatory focus. Journal of Experimental Social Psychology, 43, 505–512. http://dx. doi.org/10.1016/j.jesp.2006.05.014. Peterson, R. S., & Behfar, K. J. (2005). Leadership as group regulation. In D. M. Messick & R. M. Kramer (Eds.), The psychology of leadership: New perspectives and research. Mahwah, NJ: US: Lawrence Erlbaum Associates Publishers. xii. Pierro, A., Giacomantonio, M., Mannetti, L., Higgins, T. E., & Kruglanski, A. W. (2012). Leaders as planners and movers: Supervisors’ regulatory modes and subordinates’ performance. Journal of Applied Social Psychology, 42, 2564–2582. http://dx.doi.org/10.1037/ t00038-000. Pierro, A., Kruglanski, A. W., & Raven, B. H. (2012). Motivational underpinnings of social influence in work settings: Bases of social power and the need for cognitive closure. European Journal of Social Psychology, 42, 41–52. http://dx.doi.org/10.1002/ejsp.836. Pierro, A., Mannetti, L., De Grada, E., Livi, S., & Kruglanski, A. W. (2003). Autocracy bias in informal groups under need for cognitive closure. Personality and Social Psychology Bulletin, 29, 405–417. http://dx.doi.org/10.1177/0146167203251191. Pierro, A., Presaghi, F., Higgins, T. E., & Kruglanski, A. W. (2009). Regulatory mode preferences for autonomy supporting versus controlling instructional styles. British Journal of Educational Psychology, 79, 599–615. http://dx.doi.org/10.1348/978185409X 412444. Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on followers’ trust in leader, satisfaction, and organizational citizenship behaviors. The Leadership Quarterly, 1, 107–142. http://dx.doi.org/ 10.1016/1048-9843(90)90009-7.

A Self-Regulation Approach to Leadership

255

Rafferty, A. E., & Griffin, M. A. (2004). Dimensions of transformational leadership: Conceptual and empirical extensions. The Leadership Quarterly, 15, 329–354. http:// dx.doi.org/10.1016/j.leaqua.2004.02.009. Raven, B. H. (2001). Power/interaction and interpersonal influence: Experimental investigations and case studies. In A. Lee-Chai & J. Bargh (Eds.), The use and abuse of power: Multiple perspectives on the causes of corruption (pp. 217–240). New York: Psychology Press. Rietzschel, E. F. (2011). Collective regulatory focus predicts specific aspects of team innovation. Group Processes & Intergroup Relations, 14, 337–345. http://dx.doi.org/ 10.1177/1368430210392396. Righetti, F., Finkenauer, C., & Rusbult, C. (2011). The benefits of interpersonal regulatory fit for individual goal pursuit. Journal of Personality and Social Psychology, 101, 720–736. http://dx.doi.org/10.1037/a0023592. Righetti, F., Rusbult, C., & Finkenauer, C. (2010). Regulatory focus and the Michelangelo phenomenon: How close partners promote one another’s ideal selves. Journal of Experimental Social Psychology, 46, 972–985. http://dx.doi.org/10.1016/j. jesp.2010.06.001. Ritchie, S. A. (2009). The impact of leader–follower regulatory focus congruence on regulatory fit and relationship quality. University of Akron. Retrieved via: Doctoral dissertation, http:// rave.ohiolink.edu/etdc/view?acc_num¼akron1248469182. Roets, A., Kruglanski, A. W., Kossowska, M., Pierro, A., & Hong, Y. Y. (2015). The motivated gatekeeper of our minds: New directions in need for cognitive closure theory and research. Advances in Experimental Social Psychology, 52, 221–283. http://dx.doi.org/ 10.1016/bs.aesp.2015.01.001. Rubin, R. S., Munz, D. C., & Bommer, W. H. (2005). Leading from within: The effects of emotion recognition and personality on transformational leadership behavior. Academy of Management Journal, 48, 845–858. http://dx.doi.org/10.5465/AMJ.2005.18803926. Ruiz, P., Ruiz, C., & Martı´nez, R. (2011). Improving the “leader–follower” relationship: Top manager or supervisor? The ethical leadership trickle-down effect on follower job response. Journal of Business Ethics, 99, 587–608. http://dx.doi.org/10.1007/s10551-010-0670-3. Sassenberg, K., Brazy, P. C., Jonas, K. J., & Shah, J. Y. (2013). When gender fits selfregulatory preferences: The impact of regulatory fit on gender-based ingroup favoritism. Social Psychology, 44, 4–15. http://dx.doi.org/10.1027/1864-9335/a000095. Sassenberg, K., Ellemers, N., & Scheepers, D. (2012). The attraction of social power: The influence of construing power as opportunity versus responsibility. Journal of Experimental Social Psychology, 48, 550–555. http://dx.doi.org/10.1016/j.jesp.2011.11.008. Sassenberg, K., & Hansen, N. (2007). The impact of regulatory focus on affective responses to social discrimination. European Journal of Social Psychology, 37, 421–444. http://dx.doi. org/10.1002/ejsp.358. Sassenberg, K., Jonas, K. J., Shah, J. Y., & Brazy, P. C. (2007). Why some groups just feel better: The regulatory fit of group power. Journal of Personality and Social Psychology, 92, 249–267. http://dx.doi.org/10.1037/0022-3514.92.2.249. Sassenberg, K., Kessler, T., & Mummendey, A. (2003). Less negative ¼ more positive? Social discrimination as avoidance or approach. Journal of Experimental Social Psychology, 39, 48–58. http://dx.doi.org/10.1016/S0022-1031(02)00519-X. Sassenberg, K., Landkammer, F., & Jacoby, J. (2014). The influence of regulatory focus and group vs. individual goals on the evaluation bias in the context of group decision making. Journal of Experimental Social Psychology, 54, 153–164. http://dx.doi.org/10.1016/j. jesp.2014.05.009. Sassenberg, K., & Scholl, A. (2013). If I can do it my way …The influence of regulatory focus on job-related values and job selection. Journal of Economic Psychology, 38, 58–70. http:// dx.doi.org/10.1016/j.joep.2012.09.003.

256

K. Sassenberg and M.R.W. Hamstra

Sassenberg, K., & Woltin, K.-A. (2008). Group-based self-regulation: The effects of regulatory focus. European Review of Social Psychology, 19, 126–164. http://dx.doi.org/ 10.1080/10463280802201894. Sassenrath, C., Sassenberg, K., Ray, D. G., Scheiter, K., & Jarodzka, H. (2014). A motivational determinant of facial emotion recognition: Regulatory focus affects recognition of emotions in faces. PLoS One, 9(11). e112383. http://dx.doi.org/10.1371/ journal.pone.0112383. Scholl, A., Sassenrath, C., & Sassenberg, K. (2015). Attracted to power: Challenge/threat and promotion/prevention focus differentially predict the attractiveness of group power. Frontiers in Psychology, 6. http://dx.doi.org/10.3389/fpsyg.2015.003. orster, J. (2004). Stereotype threat and performance: How self-stereotypes Seibt, B., & F€ influence processing by inducing regulatory foci. Journal of Personality and Social Psychology, 87, 38–56. http://dx.doi.org/10.1037/0022-3514.87.1.38. Shamir, B., House, R. J., & Arthur, M. B. (1993). The motivational effects of charismatic leadership: A self-concept based theory. Organization Science, 4, 577–594. http://dx. doi.org/10.1287/orsc.4.4.577. Shin, Y., & Kim, M. (2013). Regulatory focus in work teams: Antecedents and consequences of team prevention and promotion foci. Academy of Management Proceedings, 2013(1), 10806. Shin, Y., Kim, M. S., Choi, J. N., Kim, M., & Oh, W. (in press). Does leader–follower regulatory fit matter? The role of regulatory fit in followers’ organizational citizenship behavior. Journal of Management. doi: 10.1177/0149206314546867. Sosik, J. J., & Dionne, S. D. (1997). Leadership styles and Deming’s behavior factors. Journal of Business and Psychology, 11, 447–462. http://dx.doi.org/10.1007/BF02195891. Spangler, W. D., & House, R. J. (1991). Presidential effectiveness and the leadership motive profile. Journal of Personality and Social Psychology, 60, 439–455. http://dx.doi.org/ 10.1037/0022-3514.60.3.439. Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89, 845–851. http://dx.doi.org/ 10.1037/0022-3514.89.6.845. Spiegel, S., Grant-Pillow, H., & Higgins, E. T. (2004). How regulatory fit enhances motivational strength during goal pursuit. European Journal of Social Psychology, 34, 39–54. http://dx.doi.org/10.1002/ejsp.180. Stam, D. A., van Knippenberg, D., & Wisse, B. (2010). The role of regulatory fit in visionary leadership. Journal of Organizational Behavior, 31, 499–518. http://dx.doi.org/10.1002/ job.624. Stogdill, R. M. (1974). Handbook of leadership: A survey of theory and research. New York, NY: Free Press. Strube, M. J., & Garcia, J. E. (1981). A meta-analytic investigation of Fiedler’s contingency model of leadership effectiveness. Psychological Bulletin, 90, 307–321. http://dx.doi.org/ 10.1037/0033-2909.90.2.307. Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117, 440–463. http://dx.doi.org/10.1037/a0018963. Tr€ otschel, R., B€ undgens, S., H€ uffmeier, J., & Loschelder, D. D. (2013). Promoting prevention success at the bargaining table: Regulatory focus in distributive negotiations. Journal of Economic Psychology, 38, 26–39. http://dx.doi.org/10.1016/j.joep.2012.03.006. Tykocinski, O., Higgins, E. T., & Chaiken, S. (1994). Message framing, self-discrepancies, and yielding to persuasive messages: The motivational significance of psychological situations. Personality and Social Psychology Bulletin, 20, 107–115. http://dx.doi.org/ 10.1177/0146167294201011.

A Self-Regulation Approach to Leadership

257

Van Dierendonck, D. (2011). Servant leadership: A review and synthesis. Journal of Management, 37, 1228–1261. http://dx.doi.org/10.1177/0149206310380462. van Knippenberg, D., & Sitkin, S. B. (2013). A critical assessment of charismatictransformational leadership research: Back to the drawing board? The Academy of Management Annals, 7, 1–60. http://dx.doi.org/10.1080./19416520.2013.759433. Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual differences in holistic processing predict face recognition ability. Psychological Science, 23, 169–177. http:// dx.doi.org/10.1177/0956797611420575. Weber, M. (1921/engl. 1978). Wirtschaft und Gesellschaft. Grundriss der verstehenden Soziologie [Economy and society: An outline of interpretive sociology]. T€ ubingen, Germany: MohrSiebeck. Whitford, T., & Moss, S. A. (2009). Transformational leadership in distributed work groups: The moderating role of follower regulatory focus and goal orientation. Communication Research, 36, 810–837. http://dx.doi.org/10.1177/0093650209346800. Winter, D. G. (1988). The power motive in women—And men. Journal of Personality and Social Psychology, 54, 510–519. http://dx.doi.org/10.1037/0022-3514.54.3.510. Winter, D. G. (1991). A motivational model of leadership: Predicting long-term management success from TAT measures of power motivation and responsibility. The Leadership Quarterly, 2, 67–80. http://dx.doi.org/10.1016/1048-9843(91)90023-U. Woltin, K. A., Corneille, O., & Yzerbyt, V. Y. (2012). Improving communicative understanding: The benefits of global processing. Journal of Experimental Social Psychology, 48, 1179–1182. http://dx.doi.org/10.1016/j.jesp.2012.03.004. Woltin, K. A., Corneille, O., Yzerbyt, V. Y., & F€ orster, J. (2011). Narrowing down to open up for other people’s concerns: Empathic concern can be enhanced by inducing detailed processing. Journal of Experimental Social Psychology, 47, 418–424. http://dx.doi.org/ 10.1016/j.jesp.2010.11.006. Woltin, K. A., & Yzerbyt, V. (2015). Regulatory focus in predictions about others. Personality and Social Psychology Bulletin, 41, 379–392. http://dx.doi.org/10.1177/ 0146167214566188. Yukl, G. (1998). Leadership in organizations. Englewood Cliffs, NJ: Prentice-Hall. Zhang, S., Higgins, E. T., & Chen, G. (2011). Managing others like you were managed: How prevention focus motivates copying interpersonal norms. Journal of Personality and Social Psychology, 100, 647–663. http://dx.doi.org/10.1037/a0021750. Zhu, R., & Meyers-Levy, J. (2007). Exploring the cognitive mechanism that underlies regulatory focus effects. Journal of Consumer Research, 34, 89–96. http://dx.doi.org/ 10.1086/513049.

CHAPTER FIVE

Sex Differences in Jealousy: A 25-Year Retrospective J.E. Edlund*,1,2, B.J. Sagarin†,1 *Rochester Institute of Technology, Rochester, NY, United States † Northern Illinois University, DeKalb, IL, United States 2 Corresponding author: e-mail address: [email protected]

Contents 1. The Theory of Evolved Sex Differences in Jealousy 2. Confounding Sex Differences in the Interpretation of Questions 3. Psychometric Utility of the Question 4. Do Actual Experiences Mirror Imagined Reactions? 5. Is Automaticity Relevant? 6. Physiological Manifestations 7. Meta-Analyses 8. Sexual Orientation and the Sex Difference in Jealousy 9. Other Moderators of the Sex Difference in Jealousy 10. Where the Debate Stands 11. Looking Toward the Future 12. Coda Acknowledgments References

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Abstract The theory of evolved sex differences in jealousy has emerged as one of evolutionary psychology’s most prominent lines of research. In this paper, we offer a 25-year retrospective on the theory. We begin with a review of the theory itself and the statistical implications of the theory. We then discuss many of the prominent challenges to the theory. These challenges include: a suggestion that sex differences in the interpretation of the questions often used in sex difference in jealousy studies confound the results, psychometric concerns regarding the response scales used to assess the sex difference in jealousy, whether actual experiences with infidelity mirror participants’ hypothetical reactions, potential cognitive influences on the sex difference in jealousy, ambiguous results regarding physiological manifestations of the sex difference in jealousy, meta-analyses that reach seemingly different conclusions regarding the existence of the sex difference in jealousy, and moderators (including sexual orientation)

1

Both authors contributed equally to the manuscript.

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that attenuate the sex difference in jealousy. Finally, we evaluate the state of the theory in light of the evidence we review, we consider why researchers from different subfields of psychology appear to have such different interpretations of the evidence for sex differences in jealousy, and we outline recommendations for future research directions.

In 1992, Buss, Larsen, Westen, and Semmelroth (1992) published “Sex differences in jealousy: Evolution, physiology, and psychology.” Based on earlier work by Symons (1979) and Daly, Wilson, and Weghorst (1982), Buss et al. predicted a sex difference in jealousy based on the differential reproductive challenges faced by ancestral men and women. In particular, Buss et al. theorized that men’s unique challenge of paternal uncertainty selected for greater jealousy in response to sexual infidelity, whereas women’s unique challenge of ensuring paternal investment selected for greater jealousy in response to emotional infidelity. Buss et al. (1992) offered three studies in support of their theory. Study 1 asked participants to: Please think of a serious committed romantic relationship that you have had in the past, that you currently have, or that you would like to have. Imagine that you discover that the person with whom you’ve been seriously involved became interested in someone else. What would distress or upset you more (please circle only one): (A) Imagining your partner forming a deep emotional attachment to that person. (B) Imagining your partner enjoying passionate sexual intercourse with that other person.

A subsequent question asked participants to choose between: (A) Imagining your partner trying different sexual positions with that other person. (B) Imagining your partner falling in love with that other person.

Across both pairs of infidelity scenarios, a significantly greater proportion of women than men chose the emotional infidelity as causing greater distress. Study 2 measured the physiological responses of men and women as they imagined a sexual infidelity and as they imagined an emotional infidelity. Men showed greater electrodermal activity and pulse rate in response to sexual infidelity imagery compared to emotional infidelity imagery, whereas women showed greater electrodermal activity in response to emotional infidelity imagery compared to sexual infidelity imagery. Study 3 presented a similar pair of infidelity scenarios as used in Study 1, finding that men

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who had experienced a significant relationship that featured sex showed a significantly greater tendency to choose the sexual infidelity as more distressing compared to men who had not experienced a significant relationship that featured sex. In the 25 years since its publication, Buss et al. (1992) has been cited over 400 times (according to the Web of Science; over 1200 times according to the more inclusive Google Scholar). It has inspired numerous attempts to replicate, moderate, or refute its findings. It has provoked philosophical, statistical, and psychometric debates. It, and the studies that followed, has fueled three published meta-analyses. And it, and the studies that followed, has emerged, both within and beyond academia, as one of evolutionary psychology’s most prominent lines of research. In this paper, we offer a 25-year retrospective on the theory of evolved sex differences in jealousy. We begin with a review of the theory itself and the statistical implications of the theory. We then discuss challenges to the theory stemming from (a) confounding sex differences in the interpretation of the questions often used in sex difference in jealousy studies, (b) psychometric concerns regarding the scales used to assess the sex difference in jealousy, (c) potential differences between responses to hypothetical infidelity scenarios and actual infidelity experiences, (d) potential cognitive influences, (e) mixed results regarding physiological manifestations of the effect, (f ) meta-analyses that reach different conclusions regarding the existence and robustness of the sex difference in jealousy, and (g) moderators of the sex difference in jealousy including some that heavily attenuate the sex difference. Finally, we evaluate the state of the theory in light of the reviewed evidence, consider why researchers from different subfields of psychology appear to have such different interpretations of the evidence for sex differences in jealousy, and outline recommendations for future research directions. Before we jump in, we wanted to acknowledge a couple of points. First, the two of us have been embroiled in this debate for quite a few years. Each of us was inspired to join the fray after reading Daly et al. (1982) and Buss et al. (1992) in a graduate evolutionary psychology seminar (Brad’s seminar taught by Doug Kenrick and John’s seminar taught by Brad). Both of us were fascinated by the theory and by the elegant simplicity of Buss et al.’s test of the theory, which, in its most concise form, required only a twoquestion survey. Both of us immediately started wondering about moderators (Brad about sexual orientation, Sagarin, Becker, Guadagno, Nicastle, & Millevoi, 2003; John about mate value, Edlund & Sagarin, 2014). And both

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of us subsequently devoted a substantial portion of our research efforts to studying sex differences in jealousy, publishing 13 papers on the topic separately and together. Needless to say, we have a horse in this race. We wanted to state this up front because, although we are confident that we are correct in all the theorizing, interpretation, and (why not? in for a penny, as they say) speculation contained in this paper, it is possible that we are wrong. Sex differences in jealousy are a controversial area of research, and the topic has garnered some very smart critics whose perspectives are worth taking into account. Second, we want to warn readers that the upcoming sections vary from the anecdotal and (we hope) amusing to the ponderous and pedantic. We have tried to avoid being unnecessarily cumbersome, but in some spots (perhaps most particularly the upcoming section on the theory and the statistical implications of the theory), we have sacrificed brevity for clarity. We hope the trade-off is worth it.

1. THE THEORY OF EVOLVED SEX DIFFERENCES IN JEALOUSY The theory of evolved sex differences in jealousy posits that the sex difference stems from the unique reproductive challenges faced by ancestral men and women. With respect to men’s unique reproductive challenge, Buss et al. (1992) explain: In species with internal female fertilization and gestation … males face an adaptive problem not confronted by females—uncertainty in their paternity of offspring. Maternity probability in mammals rarely or never deviates from 100%. Compromises in paternity probability come at substantial reproductive cost to the male—the loss of mating effort expended, including time, energy, risk, nuptial gifts, and mating opportunity costs. A cuckolded male also loses the female’s parental effort, which becomes channeled to a competitor’s gametes …. These multiple and severe reproductive costs should have imposed strong selection pressure on males to defend against cuckoldry …. Symons (1979), Daly et al. (1982), and Wilson and Daly (1992) have hypothesized that male sexual jealousy evolved as a solution to this adaptive problem (but see Hupka, 1991, for an alternative view). Men who were indifferent to sexual contact between their mates and other men presumably experienced lower paternity certainty, greater investment in competitors’ gametes, and lower reproductive success than did men who were motivated to attend to cues to infidelity and to act on those cues to increase paternity probability (p. 251).

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Likewise, with respect to women’s unique reproductive challenge, Buss et al. (1992) explain: Although females do not risk maternal uncertainty, in species with biparental care they do risk the potential loss of time, resources, and commitment from a male if he deserts or channels investment to alternative mates (Buss, 1988; Thornhill & Alcock, 1983; Trivers, 1972). The redirection of a mate’s investment to another female and her offspring is reproductively costly for a female, especially in environments where offspring suffer in survival and reproductive currencies without investment from both parents. In human evolutionary history, there were likely to have been at least two situations in which a woman risked losing a man’s investment. First, in a monogamous marriage, a woman risked having her mate invest in an alternative woman with whom he was having an affair (partial loss of investment) or risked his departure for an alternative woman (large or total loss of investment). Second, in polygynous marriages, a woman was at risk of having her mate invest to a larger degree in other wives and their offspring at the expense of his investment in her and her offspring. Following Buss (1988) and Mellon (1981), we hypothesize that cues to the development of a deep emotional attachment have been reliable leading indicators to women of potential reduction or loss of their mate’s investment (p. 251).

From these, Buss et al. (1992) hypothesize a sex difference in jealousy: Following Symons (1979) and Daly et al. (1982), our central hypothesis is that the events that activate jealousy physiologically and psychologically differ for men and women because of the different adaptive problems they have faced over human evolutionary history in mating contexts. Both sexes are hypothesized to be distressed over both sexual and emotional infidelity, and previous findings bear this out (Buss, 1989). However, these two kinds of infidelity should be weighted differently by men and women (p. 251).

The theory of evolved sex differences in jealousy is, thus, concerned with the relative intensity of four responses: women’s jealousy in response to sexual infidelity, women’s jealousy in response to emotional infidelity, men’s jealousy in response to sexual infidelity, and men’s jealousy in response to emotional infidelity. The question of which of these responses the theory compares has been an ongoing source of controversy (see, e.g., Harris, 2005; Sagarin, 2005). Later, we consider the possibilities from the most stringent (exclusivity of men’s and women’s jealous responses to sexual infidelity and emotional infidelity) to the least stringent (relative differences in the jealous responses of men and women to sexual vs emotional infidelity). In evaluating these possibilities, we consider the description of the theory provided by Buss et al. and the implications of the statistical analyses performed by

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Buss et al. This latter consideration enables us to “reverse engineer” aspects of the theory that are not specified explicitly in the text. Exclusive jealous responses. Does the theory specify that men will experience jealousy only in response to sexual infidelity (and not in response to emotional infidelity), and that women will experience jealousy only in response to emotional infidelity (and not in response to sexual infidelity)? As just quoted earlier, Buss et al. (1992) are clear that the theory does not specify this type of exclusivity but instead recognizes that both sexes will be distressed over any infidelity, although there will be a sex difference in the weighting of the two types of infidelity. Within-sex differences in responses to sexual infidelity and emotional infidelity. Does the theory specify a difference in men’s jealousy in response to sexual infidelity compared to men’s jealousy in response to emotional infidelity? Likewise, does the theory specify a difference in women’s jealousy in response to sexual infidelity compared to women’s jealousy in response to emotional infidelity? Buss et al.’s (1992) description of the theory leaves this question open. The description of the theory states only that, “these two kinds of infidelity should be weighted differently by men and women” (p. 251). It is possible that these different weightings imply that men will feel more jealous in response to sexual infidelity than they will in response to emotional infidelity and that women will feel more jealous in response to emotional infidelity than they will in response to sexual infidelity. But it is also possible that these different weightings will take a different form (e.g., that both men and women will feel more jealous in response to sexual infidelity than they will in response to emotional infidelity, but that the difference will be greater for men than for women). Buss et al.’s (1992) statistical analyses also leave this question open. On the one hand, the forced-choice responses in Studies 1 and 3 are analyzed using chi-square tests that compare the proportion of men who chose the sexual infidelity as causing greater distress to the proportion of women who chose the sexual infidelity as causing greater distress. This implies that the critical comparison is the relative weight that men vs women apply to sexual vs emotional infidelity. The tests of whether men apply greater weight to sexual infidelity vs emotional infidelity and whether women apply greater weight to emotional infidelity vs sexual infidelity would require a different statistical analysis: a test of whether the proportion of men who chose the sexual infidelity as causing greater distress was greater than 50%, and a test of whether the proportion of women who chose the sexual infidelity as causing greater distress was less than 50%.

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On the other hand, the physiological responses in Study 2 are analyzed using t-tests that compare men’s responses to sexual infidelity imagery to men’s responses to emotional infidelity imagery and that compare women’s responses to sexual infidelity imagery to women’s responses to emotional infidelity imagery. These tests imply that the critical comparisons are the weights that men apply to sexual infidelity vs emotional infidelity and the weights that women apply to sexual infidelity vs emotional infidelity. A test of the relative weights, in contrast, would have involved an interaction between participant sex and infidelity type. Our view is that the theory should not be interpreted as specifying a difference in men’s jealousy in response to sexual infidelity compared to men’s jealousy in response to emotional infidelity or a difference in women’s jealousy in response to sexual infidelity compared to women’s jealousy in response to emotional infidelity. Sexual infidelity and emotional infidelity differ in numerous ways beyond the threats of increased paternal uncertainty and decreased likelihood of paternal investment, respectively (e.g., sexual infidelity might be perceived as more volitional compared to emotional infidelity). These differences might mean that sexual infidelity is, on the whole, more jealousy provoking than is emotional infidelity (or vice versa). An overall difference of this type is not incompatible with a sex difference in jealousy. But the possibility of such an overall difference is incompatible with an interpretation of the theory as specifying within-sex differences in responses to sexual infidelity and emotional infidelity. Sex differences in response to sexual infidelity and in response to emotional infidelity. Does the theory specify a sex difference in response to sexual infidelity? Does the theory specify a sex difference in response to emotional infidelity? As with the within-sex differences in response to sexual infidelity and emotional infidelity, Buss et al.’s (1992) description of the theory is not completely clear about this. However, it is notable that Buss et al. separately describe the ramifications of men’s unique reproductive challenge of paternal uncertainty (and the theorized impact of this challenge on responses to sexual infidelity) and the ramifications of women’s unique reproductive challenge of ensuring paternal investment (and the theorized impact of this challenge on responses to emotional infidelity). Thus, the theory of evolved sex differences in jealousy is actually a combination of two theories: (1) the theory that men’s unique challenge of paternal uncertainty selected for greater jealousy in response to sexual infidelity, and (2) the theory that women’s unique challenge of ensuring paternal investment selected for greater jealousy in response to emotional infidelity.

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Buss et al.’s (1992) statistical analyses do not test for sex differences in response to each type of infidelity, however. In fact, the forced-choice methodology used for Studies 1 and 3 does not allow for such a test. The methodology used for Study 2 would allow for such a test by comparing men’s responses to sexual infidelity imagery to women’s responses to sexual infidelity imagery (and likewise for emotional infidelity imagery), but Buss et al. do not present the results of this test. Our view is that these two aspects of the theory could, in principle, be tested separately. However, such tests would require a control condition assessing men’s and women’s general reactivity to either sexually related items or emotionally related items or assessing men’s and women’s reactions to a jealousy-provoking situation outside the domain of sexual or romantic relationships (e.g., sibling jealousy, jealousy among friends). Such a control condition has seldom been included in sex difference in jealousy studies (but see Baschnagel & Edlund, 2016, for an exception). A control condition would be needed to eliminate confounding factors that could affect the presence, magnitude, and direction of a sex difference in response to sexual infidelity or in response to emotional infidelity. For example, Feldman Barrett, Robin, Pietromonaco, and Eyssell (1998) found that women report more intense emotions than men on global, retrospective measures. If a study simply compared women’s and men’s responses to emotional infidelity, then the phenomenon identified by Feldman Barrett et al. could produce a sex difference that could be misinterpreted as supporting the evolutionary psychological theory. In such a study, the control condition might take the form of assessing men’s and women’s reactions to strong emotions beyond jealousy (such as global retrospective reactions to falling in love). This control condition would, presumably, manifest the phenomenon identified by Feldman Barrett et al. (and other analogous phenomena) and would, therefore, allow a more valid test of whether women and men differ in response to emotional infidelity. Buss et al. (1992) and the studies that followed essentially used responses to sexual infidelity as the control condition for responses to emotional infidelity and responses to emotional infidelity as the control condition for responses to sexual infidelity (that is, each participant’s response to the sexual infidelity provides a control for their response to the emotional infidelity and vice versa). This eliminated confounds such as the phenomenon identified by Feldman Barrett et al. (1998), but at a cost of increasing the ambiguity of interpretation of the results. That is, it is unclear in such studies whether an observed sex difference stems from a sex difference in responses to sexual

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infidelity, a sex difference in response to emotional infidelity, or both. Unfortunately, for the reasons just discussed, this ambiguity cannot be resolved simply by separately examining sex differences in response to each type of infidelity—resolution would require a control condition to which responses to sexual infidelity and responses to emotional infidelity could be compared. Resolving this ambiguity represents an important direction for future research. Relative differences in men’s and women’s responses to sexual versus emotional infidelity. Perhaps the clearest comparison specified by the theory is the prediction that men, relative to women, will report greater jealousy in response to sexual infidelity compared to emotional infidelity. Buss et al.’s (1992) specification of the theory (“Both sexes are hypothesized to be distressed over both sexual and emotional infidelity, and previous findings bear this out (Buss, 1989). However, these two kinds of infidelity should be weighted differently by men and women,” p. 251) specifies these relative differences, and Studies 1 and 3 of Buss et al. test this relative comparison. Buss et al.’s analysis of Study 2 adds some ambiguity, as the most straightforward test of the relative comparison would have been the participant sex by infidelity-type interaction (which Buss et al. do not report). Nevertheless, the predicted results for the analyses performed for Study 2 (that men would respond more strongly to sexual infidelity than to emotional infidelity and that women would respond more strongly to emotional infidelity than to sexual infidelity) imply an interaction (although they provide what we argue is an overly stringent test). In sum, it is our contention that the theory of evolved sex differences in jealousy unambiguously predicts that men, relative to women, will report greater jealousy in response to sexual infidelity compared to emotional infidelity. We also contend that the two subcomponents of the theory offer separate predictions: that men will report greater jealousy in response to sexual infidelity than will women, and that women will report greater jealousy in response to emotional infidelity than will men, but that testing these separate predictions would require a control condition not present in most sex difference in jealousy studies. In the absence of this control condition, we argue that the most appropriate test of the theory of evolved sex differences in jealousy for studies that separately measure women’s and men’s responses to sexual infidelity and to emotional infidelity is the participant sex by infidelity-type interaction (and not the simple effects of infidelity type within each sex, which is irrelevant for the theory, or the simple effects of sex within each infidelity type, which is ambiguous in the absence of a

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control condition; see Edlund & Sagarin, 2009 or Sagarin, Martin, et al., 2012, for an extended discussion regarding the sole relevance of the interaction for studies of this form). Likewise, we argue that the most appropriate test for studies that measure women’s and men’s responses to a forced-choice dilemma is a chi-square that compares the proportions of each sex who choose the sexual infidelity as causing greater distress (and not the test of whether the proportion of men or the proportion women differs from 50%, which is irrelevant for the theory).

2. CONFOUNDING SEX DIFFERENCES IN THE INTERPRETATION OF QUESTIONS One class of threats to the theory of evolved sex differences in jealousy is based on potential confounding sex differences in the interpretation of the questions used to assess responses to sexual infidelity and emotional infidelity. The first of these focuses on sex differences in the extent to which one type of infidelity implies the other. DeSteno and Salovey (1996) refer to this as the “double-shot hypothesis”: We suspect that the findings presented by Buss et al. (1992) can be explained by what we term the double-shot hypothesis. Simply stated, some individuals believe that emotional and sexual infidelity are not independent events. Consequently, they will select the type of infidelity that more implies the occurrence of the other when asked to indicate which one would make them more jealous. For instance, emotional infidelity, for certain individuals, may imply that sexual infidelity has occurred or soon will occur. These perceptions of nonindependence, moreover, may be correlated with sex in some samples, with women more likely than men to expect that emotional infidelity by their partners implies associated sexual infidelity (p. 368).

Harris and Christenfeld (1996) make a similar argument, but whereas DeSteno and Salovey (1996) specify that women (but not men) will perceive asymmetry regarding one type of infidelity implying the other, Harris and Christenfeld specify that both women and men will perceive this type of asymmetry: We suggest instead that men and women may be equally upset by each type of infidelity and that the crucial difference may lie in how much they think that each form of infidelity signals the other. Imagine a man returning from work one day to discover incontrovertible proof of his wife’s sexual infidelity. He might well think that because women have sex only when in love, it is quite certain that she has fallen in love with this other man as well. A woman, however, finding the same evidence about her husband, might think that because men often have sex without being in love, there is no reason

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to assume he is in love with the other woman. The man, then, is upset by what he takes to be sexual and emotional infidelity, whereas the woman is concerned only about sexual infidelity. The man will be more bothered by the sexual infidelity than is the woman because he draws a more troubling conclusion from that evidence. The man should have a stronger response to sexual infidelity even if the man and woman care equally about their spouses’ actual sexual exploits. The situation should be reversed with evidence of emotional infidelity. The man, on coming across evidence of this sort, should reason that women can be in love without having sex, and so he need not assume that there is sexual infidelity as well. The woman, however, thinking that men in love are certainly having sex, will assume that both sorts of treachery have occurred, and be doubly bothered. Thus, emotional infidelity should especially trouble women, and sexual infidelity should especially trouble men. This prediction follows not from any postulated innate difference in responses to the specific infidelities, but rationally from the hypothesis that men think women have sex only when in love and women think men have sex without love (p. 364).

DeSteno and Salovey (1996) test the “double-shot hypothesis” in two studies in which the differential infidelity implication (the extent to which one type of infidelity implies the other) is used as a simultaneous predictor along with participant sex to predict which type of infidelity participants find more distressing. The differential infidelity implication is measured with two questions that ask about B.F., “a typical member of the opposite sex”: “If B.F. develops a deep emotional attachment to someone of your gender, how likely is it that B.F. and this other individual are now, or soon will be sleeping together?” and “If B.F. has slept with someone of your gender, how likely is it that B.F. is forming, or will form, a deep emotional attachment to this individual?” (p. 368). Both studies replicate the sex difference in jealousy. However, both studies also find that participant sex is no longer a significant predictor when the differential infidelity implication is included in the logistic regression equation. Instead, the differential infidelity implication is the only significant predictor. Harris and Christenfeld (1996) adopt a similar approach, asking participants to: “Please think of a serious romantic relationship you have had in the past, currently have, or would like to have. Imagine that you discover that your mate is engaging in sexual intercourse with someone else. How likely do you think it is that your mate is in love with this person?” and “Please think of a serious romantic relationship you have had in the past, currently have, or would like to have. Imagine that you discover that your mate is in love with someone else. How likely do you think it is that your mate is also engaging in sex with this other person?” (pp. 364–365).

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Harris and Christenfeld (1996) replicate the sex difference in jealousy and find evidence for an asymmetry in one type of infidelity implying the other. Specifically, Harris and Christenfeld “found … that men think that sex implies love for their partners more than do women, whereas women think that love implies sex more than do men, … F(1, 132) ¼ 11.32, p < .001” (p. 365; Harris & Christenfeld did not conduct a logistic regression analysis of the type performed by DeSteno & Salovey, 1996). Later, Harris and Christenfeld note “that the dominant effect was that women think men can have sex without being in love” (p. 365). Buss et al. (1999) conducted three studies designed to test the plausibility of DeSteno and Salovey’s (1996) double-shot hypothesis as an alternative explanation for sex differences in jealousy. Buss et al. asked participants from the United States, Korea, and Japan to respond to a series of forced-choice infidelity dilemmas, some of which were written to preclude a double-shot explanation. For example, one dilemma asked participants to choose between an exclusively sexual infidelity (“Imagining your partner forming a deep emotional (but not sexual) relationship with that person”) and an exclusively emotional infidelity (“Imagining your partner enjoying a sexual (but not emotional) relationship with that person,” p. 130). Another dilemma posited that both types of infidelity had occurred and asked participants “which aspect of your partner’s involvement would upset you more?” American, Korean, and Japanese participants showed significant sex differences across all six dilemmas with one exception: Japanese participants did not show a significant sex difference on one exclusive infidelity dilemma. In addition, Buss et al. measured differential infidelity implications in the American and Japanese samples. In both samples, a replication of DeSteno and Salovey’s logistic regression methodology found that participant sex, but not differential infidelity implications, was a significant unique predictor. Buss et al. conclude that the weight of the evidence supports the evolutionary explanation rather than the double-shot hypothesis, although they do not offer an account of DeSteno and Salovey’s discrepant results. Brase, Adair, and Monk (2014) also tested the double-shot hypothesis, with results that replicate Buss et al. (1999). More recently, Kato (2014) posited that the sex difference in jealousy stems from men’s greater tendency than women to vividly imagine sexual infidelity. Kato had men and women respond to Buss et al.’s (1992) first forced-choice infidelity dilemma. Participants who were in a committed relationship then attended two laboratory sessions: one, 40–60 days after the initial survey; the other, 30–40 days after the first laboratory session.

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During one session, participants were shown vivid pictorial and auditory stimuli of a sexual infidelity and an emotional infidelity and were asked to choose which infidelity caused greater distress or upset. During the other session, participants were shown nonvivid versions of a sexual infidelity and an emotional infidelity and were asked to choose which infidelity caused greater distress or upset. As part of a pretest, using Buss et al.’s (1992) forced-choice infidelity dilemma, Kato (2014) found that participants who were not in a committed relationship (n ¼ 325) showed a significant sex difference with 51% of men and 24% of women choosing the sexual infidelity as causing greater distress. Participants who were in a committed relationship (n ¼ 128), in contrast, did not show a significant sex difference with 61% of men and 44% of women choosing the sexual infidelity as causing greater distress (which stands in contrast to the results found by Buss et al., 1992, who found that the sex difference in jealousy was magnified in participants who had experienced a committed sexual relationship). In a later session, these participants who were in a committed relationship also did not show a significant sex difference when asked to imagine infidelity vividly (with 73% of men and 67% of women choosing the sexual infidelity as causing greater distress) or when responding to nonvivid infidelities (with 61% of men and 47% of women choosing the sexual infidelity as causing greater distress). Consistent with Kato’s hypothesis regarding men’s tendency to vividly imagine sexual infidelity, the vividness manipulation significantly increased women’s (but not men’s) tendency to choose the sexual infidelity as causing greater distress. In sum, DeSteno and Salovey (1996), Harris and Christenfeld (1996), and Kato (2014) have offered alternative explanations for sex differences in jealousy based on potential confounding sex differences in the interpretation of the questions used to assess responses to sexual infidelity and emotional infidelity. DeSteno and Salovey (1996) and Harris and Christenfeld (1996) argue that the sex difference in jealousy stems from asymmetric differences in men’s and women’s perceptions of the extent to which one type of infidelity implies the other, and consistent with this alternative explanation, DeSteno and Salovey (1996) and Harris and Christenfeld (1996) found evidence of differential infidelity implications, and DeSteno and Salovey found evidence that these differential infidelity implications fully accounted for the sex difference in which type of infidelity caused greater distress. However, Buss et al. (1999) found significant sex differences in jealousy using scenarios that precluded a differential infidelity

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implication-based explanation, and Buss et al.’s replication of DeSteno and Salovey showed a significant unique effect of participant sex. These results reduce the plausibility of differential infidelity implications as fully accounting for sex differences in jealousy. Kato (2014) posited a different alternative explanation, arguing that the sex difference stems from men’s greater tendency than women to vividly imagine sexual infidelity. Consistent with this explanation, Kato found that women (but not men) were significantly more likely to choose the sexual infidelity as causing greater distress when they underwent an experimental manipulation designed to encourage vivid imagery of the infidelities. As Kato notes, however, the data are somewhat inconsistent with past findings in that Kato found a significant sex difference in jealousy only among participants not in a committed relationship when responding to the original Buss et al. (1992) forced-choice scenario; Kato did not find a significant sex difference in jealousy among participants in a committed relationship when responding to the original Buss et al. forced-choice scenario or when responding to Kato’s vivid condition or nonvivid condition. Further, because support for Kato’s alternative explanation is based, in part, on nonsignificant findings, replication with larger sample sizes is needed. Finally, if replication supports Kato’s findings, additional research will be needed to determine whether the phenomenon identified by Kato represents an alternative explanation for the sex difference or a mechanism whereby the sex difference in jealousy manifests.

3. PSYCHOMETRIC UTILITY OF THE QUESTION The first years of research into the sex difference in jealousy focused primarily on the form of assessment initially used by Buss et al. (1992)—the forced-choice methodology (e.g., Buss et al., 1999; DeSteno & Salovey, 1996; Harris & Christenfeld, 1996). The robustness of this approach was seen in the first meta-analysis of the sex difference in jealousy (Harris, 2003). In this meta-analysis, Harris found that the sex difference in jealousy emerged consistently when assessed via the forced-choice methodology. Although the sex difference in jealousy using forced-choice measures was beginning to be well established, a number of researchers criticized the reliance on this methodology. The first such critique was made by DeSteno and Salovey (1996) in their “double-shot” paper:

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One further point is also worthy of note. Although the sex difference in infidelity choice reported by Buss et al. (1992) is readily replicable using the forced-choice paradigm, we have been unable to replicate it using continuous measures asking individuals to rate the amount of distress experienced in response to each type of infidelity …. A study recently conducted in our laboratory illustrates the case. Sixtyfive participants (34 men. 31 women) completed the standard forced-choice measure as well as a six-item jealousy measure (Cronbach’s α ¼ .90) for each of the two types of infidelity. Ratings were recorded on 9-point scales with the sum representing each participant’s jealousy score. As expected, we replicated the usual relation between sex and choice of infidelity type for the forced-choice measure, χ2(1, N ¼ 65) ¼ 10.20. p ¼ .001. The majority of women reported that emotional infidelity (f ¼ 27) would cause more distress than sexual infidelity: only 4 women selected sexual infidelity as more distressing. Men, however, showed no such differential preference for sexual (f ¼ 17) or emotional (f ¼ 17) infidelity. Again, the lack of a differential preference in men is difficult to explain from the perspective of evolutionary psychology. More important, analyses of the continuous measures failed to show a relation between sex and the intensity of jealousy in response to the two types of infidelity. A 2 (sex) X 2 (infidelity type) mixed analysis of variance provided no evidence for the predicted interaction (for men, Msexual infidelity ¼ 43.62, SD ¼ 12. 16, and Memotional infidelity ¼ 42.21, SD ¼ 12.32; for women, Msexual infidelity ¼ 47.03, SD ¼ 8.97, and Memotional infidelity ¼ 48.47, SD ¼ 7.67; F[l, 62]¼ 1.78, p ¼ .19). (p. 371).

Subsequently, DeSteno, Bartlett, Braverman, and Salovey (2002) offered an expanded test of the sex difference in jealousy using continuous measures. DeSteno et al. replicated the sex difference using a forced-choice measure but failed to replicate the sex difference on any of three continuous measures (a scale indicating the intensity of six emotions experienced in response to sexual infidelity and in response to emotional infidelity, a five-item agree– disagree scale completed in response to each type of infidelity, and a checklist of adjectives completed in response to each type of infidelity). Harris (2003) reviewed these and other studies, finding that “data are steadily accumulating that suggest that the sex differences predicted by the JSIM model are rarely found when measures other than the forced-choice items are used to assess jealousy over hypothetical infidelity” (p. 105). Sagarin (2005) saw the evidence as somewhat stronger but still recognized that the sex difference was more readily replicable using forced-choice measures: Clearly, sex differences in jealousy manifest most reliably using the forced-choice response format. But as this review demonstrates, significant sex differences have also been observed in multiple studies using non-forced-choice measures. Theoryconsistent directional differences were found in 10 of 11 studies (counting the two studies of Sagarin et al., 2003, separately) and these were significant in 4 of the 11 cases (p. 68).

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This critique represented a serious challenge to the theory of evolved sex differences in jealousy. If the effect was limited to occurring only when the forced-choice methodology was employed, the effect might reasonably be considered simply an artifact of measurement. However, psychometric decisions made in some of the studies that tested sex differences in jealousy using continuous measures might have limited their ability to find an effect. Krosnick (1999) offers a number of psychometric recommendations for designing scales that maximize useful variance. For example, Krosnick recommends that unipolar constructs (e.g., “not at all happy” to “very happy”) be measured using 5-point scales, whereas bipolar constructs (e.g., “very unhappy” to “very happy”) be measured using 7-point scales. Regardless of whether the scale is unipolar or bipolar, Krosnick recommends that all points are labeled to ensure that participants interpret the questions similarly (the importance of this point was recently demonstrated in Engelbrecht & Edlund, 2016). To more clearly disambiguate whether the scale response format matters, Edlund and Sagarin (2009) directly tested the impact of scaling decisions on the ability to find the sex difference in jealousy. Edlund and Sagarin collected data using 14 parallel response scale formats (looking at responses to hypothetical infidelity scenarios); each version of the scale had a minimum of 76 participants (and 25 men). Edlund and Sagarin manipulated the length of the scale (5-, 7-, 9-, 10-point scales, along with a feeling thermometer), the labeling of the points (ends only, mid-point and ends, or all points labeled), and the upper-most anchor (“extremely” or “very”), all of which were manipulated between subjects. Across the 14 versions of the scale, only 5 were independently significant when analyzed separately; however, when the 14 versions were combined meta-analytically, the overall effect was highly significant. Of the three factors tested, only the labeling of the points significantly moderated the effect, such that scales with all points labeled were less likely to find an effect compared to scales with ends only labeled or mid-point and ends labeled (a finding that runs contrary to the recommendations of Krosnick, 1999; we will return to this issue in a moment). Two studies using continuous response scales obtained nationally representative samples through TESS (Time-sharing Experiments for the Social Sciences; http://www.tessexperiments.org). Green and Sabini (2006) reported no difference between men’s and women’s jealousy in response to emotional infidelity and sexual infidelity. However, a reanalysis of the data conducted as part of Sagarin, Martin, et al.’s (2012) meta-analysis suggested that a significant interaction was present in the theory-supportive

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direction (when the interaction was analyzed within-subjects). Zengel, Edlund, and Sagarin (2013) also collected data through TESS. Zengel et al. found a significant effect for the forced-choice measure, but did not find a significant effect for the continuous measure. Despite these inconsistent findings when sex differences in jealousy are tested using continuous measures, Sagarin, Martin, et al.’s (2012) metaanalysis found a significant overall effect across studies. Nevertheless, it is clear that the forced-choice measure is a more robust method for finding the effect—a phenomenon that seems at odds with standard psychometric recommendations that dichotomous DVs should be avoided, that continuous measures are preferred, that these continuous measures should consist of 5-point scales when assessing unipolar constructs (e.g., “not at all jealous” to “extremely jealous”), and that all points on the scale should be labeled. However, these recommendations are predicated on an assumption that participants’ responses will be distributed across the full range of response options. In the case of jealousy about a partner’s infidelity, it seems likely that this assumption will often be violated because most participants will have extreme reactions to a partner’s infidelity. This is evidenced by the number of participants that select the maximum point in many studies using continuous measures (e.g., Green & Sabini, 2006; where approximately ¾ of participants selected the maximum response for emotional infidelity). We suspect that this is the reason that the forced-choice measure is more reliable in finding the sex difference—it prevents participants from indicating that they are equally upset by the sexual infidelity and the emotional infidelity. Instead, participants must choose which type of infidelity is worse, and this forced-choice uncovers the sex difference.

4. DO ACTUAL EXPERIENCES MIRROR IMAGINED REACTIONS? In Buss et al.’s (1992) seminal study, participants were asked to imagine their reactions to a number of hypothetical infidelity scenarios while thinking of a relationship that they were currently in, previously had been in, or would like to be in. Many of the studies that followed have employed this approach in which participants are asked to imagine their reactions to hypothetical infidelity scenarios, occurring, in some cases, in hypothetical relationships.

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However, we know that people’s ability to accurately imagine their reactions to hypothetical situations can be limited. For instance, studies have shown people are inaccurate in predicting the intensity and longevity of their affective reactions to a number of situations ranging from predicting how they will feel when their sports team wins (Gaunt, Sindic, & Leyens, 2005) to how divorce will impact their lives (Lavner, Karney, & Bradbury, 2013), although people rarely make errors in predicting the type of feeling they would experience, such as thinking they would be happy if their favorite team lost. Some researchers have questioned how these inaccuracies would apply in the context of people’s reactions to infidelity. The first study to explore this issue was Harris (2002). Harris first assessed participants’ reactions to the traditional forced-choice question, where she found a significant difference in men’s and women’s responses to the hypothetical infidelity scenarios. Next, she asked participants if they had ever been cheated on. Participants who said yes were then asked to report how much they focused on each aspect of the infidelity on a 5-point scale ranging from “not at all” to “completely.” Men and women who had experienced an infidelity did not differ significantly in how much they focused on either the sexual or emotional aspects of the infidelity. Berman and Frazier (2005) also examined men’s and women’s responses to actual infidelity experiences. Berman and Frazier critiqued Harris’s (2002) use of the term focus instead of distress or upset and Harris’s assessment of focus using continuous measures instead of a forced-choice measure, questioning whether the change in term and methodology might have led to the lack of a significant effect. Berman and Frazier presented a single forced-choice question to participants who had experienced an infidelity: Thinking about your partner’s infidelity as a whole, please tell us which distressed or upset you more (check only one): Your partner’s emotional attachment to the other person. Your partner enjoying sexual activities with that other person.

Berman and Frazier (2005) replicated the traditional sex difference among men and women who had not been victims of infidelity (and were, thus, reporting their reactions to a hypothetical infidelity scenario). But Berman and Frazier also found that men and women who had experienced an infidelity showed almost no difference in which aspect of the infidelity was more distressing or upsetting, χ 2(1, N ¼ 64) ¼ 0.003. p ¼ .96. Berman and Frazier’s use of a forced-choice question that asked about distress or

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upset addressed a number potential limitations they had identified in Harris (2002) and provided a greater opportunity for a sex difference to emerge (although, it is worth noting that the small sample size, 64, and the low number of men who had experienced an infidelity, 18, makes a confident assertion of the null effect difficult). Harris’s and Berman and Frazier’s failure to find a sex difference in response to actual infidelity experiences dealt a blow to the theory of evolved sex differences in jealousy. In response to these concerns, Edlund, Heider, Scherer, Farc, and Sagarin (2006) tested whether sex differences in jealousy would emerge in response to actual experiences with infidelity using both forced-choice and continuous measures that assessed both jealousy and focus. In the first study, Edlund et al. found significant sex differences in jealousy in participants who had experienced an infidelity using both continuous (p ¼ .038) and forced-choice measures (p ¼ .019). When asked about focus, the continuous measures showed a marginally significant sex difference (p ¼ .053) and the forced-choice measure showed a nonsignificant sex difference (p ¼ .136). In this study, when both participants who had and had not experienced an infidelity were included in the analyses, the hypothetical reactions to the infidelity were significant (continuous p < .001, forced choice p < .001). In the second study, Edlund et al. employed a snowball data collection approach to obtain a more diverse sample of participants (in the snowball data collection technique, students in various classes recruited nonstudent working adults to participate in the study on the students’ behalf ). In this study, they again found significant differences in jealousy using both response formats (in response to focus, a significant sex difference emerged on the continuous measure and a marginally significant sex difference emerged on the forced-choice measure). Again, when both participants who had and had not experienced an infidelity were included in the analyses, the hypothetical reactions to the infidelity were significant with the forced choice (p < .01), whereas the interaction was marginally significant for the continuous measures (p ¼ .085). Finally, Zengel et al. (2013) employed a national panel data collection approach to examine the sex difference in jealousy in a nationally representative sample of Americans. In this sample, the sex difference in jealousy emerged when looking at people’s actual experiences with infidelity on the forced-choice measure, but the sex difference was nonsignificant on the continuous measures. However, although studies measuring participants’ responses to actual infidelity experiences avoid many problems associated with hypothetical infidelity scenarios, the retrospective nature of these studies raises concerns

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regarding memory effects. One novel attempt to resolve this employed coding of in vivo jealousy responses featured on a reality television show (Kuhle, 2011). Kuhle coded a number of episodes of the reality show “Cheaters” finding the predicted sex difference in the nature of the on-screen interrogations: The victim interrogated the cheater about the nature of the affair in 30 of the 42 cases involving male victims and in 45 of the 60 cases involving female victims. Common questions included: “Did you have sex with him?,” “Do you love her?,” and “Who do you love more?” As predicted, (1) men were more likely than women (57% vs. 29%) to focus more on the sexual aspect of their partners’ infidelities, whereas (2) women were more likely than men (71% vs. 43%) to focus more on the emotional aspect of their partners’ infidelities. This distribution of responses was significantly sex-differentiated, χ2(1, N ¼ 75) ¼ 5.78, p ¼ .016, Φ ¼ 0.28 (p. 1045).

In sum, fewer studies have examined the sex difference in jealousy using retrospective reports of actual infidelity experiences than using responses to hypothetical infidelity scenarios, and these studies have produced mixed results, with some (Berman & Frazier, 2005; Harris, 2002; Zengel et al.’s, 2013, continuous measures) not finding evidence of sex differences but others (Edlund et al., 2006; Zengel et al.’s, 2013, forced-choice measure) finding evidence of sex differences. Sagarin, Martin, et al.’s (2012) meta-analysis of continuous measure studies found a significant overall effect: “A significant, theory-supportive effect also emerged across seven studies assessing reactions to actual infidelities, g* ¼ 0.234, 95% CI [0.020, 0.448], p ¼ .03” (p. 595), but this was across only seven studies. Clearly, more work remains to be done to determine whether reactions to actual infidelity experiences mirror responses to hypothetical infidelity scenarios.

5. IS AUTOMATICITY RELEVANT? Social psychology has long approached conscious responses as only part of the equation—nonconscious processes have long been considered important to the study of psychological phenomena (e.g., Greenwald, McGhee, & Schwartz, 1998; Wegner, Schneider, Carter, & White, 1987). This idea has also been applied to the study of the sex difference in jealousy in a number of different ways. Some of these approaches have examined automatic responses to prompts (reviewed in this section), whereas other approaches have dealt with the physiological manifestations of the sex difference (reviewed in the next section).

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The first study that explored the automaticity issue was DeSteno et al. (2002). In this paper, DeSteno and colleagues first tested the sex difference in jealousy using both forced-choice and continuous measures and replicated the sex difference in jealousy only on the forced-choice measure (see earlier section on psychometric issues for a more extensive review of this study). In their next study, DeSteno and colleagues placed some participants under cognitive load and found that “the sex difference on the forced-choice measure disappeared under conditions of cognitive constraint” (p. 1103). The results found by DeSteno et al. (2002) have been used to suggest that the sex difference in jealousy is simply an artifact of measurement, because DeSteno et al. argued an evolved mechanism should be automatic. However, there have been a number of concerns raised by researchers in response to DeSteno et al. (2002). For instance, Barrett, Frederick, Haselton, and Buss (2006) challenged DeSteno et al.’s underlying assumption that an evolved mechanism should be immune to any form of context or mental resource challenge. Edlund and Sagarin (2009) and Sagarin, Martin, et al. (2012) have argued that social norms, available mental resources, and any of a number of other factors could impact the observed reactions of participants to infidelity scenarios. Other researchers have more directly questioned the methodology used by DeSteno et al. (2002) and the lack of counterbalancing of conditions and responses. Sch€ utzwohl (2008a) notes: Specifically, the participants could reproduce the digits upon a decision in a forcedchoice scenario or after 10 s without a response. Thus, in order to do well on the memory task, the participants might have used rather simple decision strategies to speed up with the forced-choice task to reproduce the digits as quickly as possible. A first methodological peculiarity that might have promoted the use of a simple decision strategy especially in the infidelity scenario concerns the description of the pertinent two response alternatives. The description of the responses alternatives in the infidelity scenario (the third of the five forced-choice scenarios) and hence the required reading time was considerably longer than any of the other scenarios. In fact, it consisted of 68 letters … whereas the length of the other scenarios varied between only 19 and 36 letters … The time for reading the response alternatives and for making a decision was confined to 10 s at most for all scenarios. However, a second methodological peculiarity is that … the first response alternative in the infidelity scenario happened to always be sexual infidelity. In this context, it is also informative that none of the men … but 6 out of 37 women … failed to make a decision within the allotted 10 s (p. 129, Sch€ utzwohl, 2008a).

When response order is controlled through counterbalancing, the expected sex difference in jealousy effect emerges (Sch€ utzwohl, 2008a). Finally,

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Sagarin (2005) reanalyzed DeSteno et al.’s data, finding that, “under cognitive load, a significantly higher proportion of men (92% of 24/26) than women (65% or 20/31) indicated that a sexual infidelity would cause greater distress than an emotional infidelity χ 2 (1, N ¼ 57) ¼ 6.20, p ¼ .013” (p. 68) and then concluded “cognitive load attenuated the sex difference, but a significant sex difference remained under cognitive constraint” (p. 68). In a similar vein, Sch€ utzwohl (2008b) explored how well participants could disengage from various cues (a proxy for how much attention these cues were taking from available cognitive resources). Sch€ utzwohl found that men had significantly more difficulty disengaging from sexually related cues (e.g., You notice that your partner seems bored when you have sex), whereas women had significantly more difficulty disengaging from emotionally related cues (e.g., Your partner does not respond anymore when you tell him that you love him). Additionally, participants’ responses were most pronounced when they were already in a romantic relationship. Additional studies have explored the cognitive manifestations of the sex difference in jealousy in a few other ways. For instance, Sch€ utzwohl (2006) explored what kinds of information people seek with regards to a partner’s infidelity. Sch€ utzwohl found that men sought more information related to the sexual components of a partner’s potential infidelity, whereas women sought more information related to the emotional components of a partner’s infidelity. Other studies have dealt with how quickly men and women reach thresholds for jealousy. Sch€ utzwohl (2005) found that there was no difference in how quickly men and women reach the threshold for the first pangs of jealousy. However, once the threshold had been reached, men reacted much more powerfully to cues to sexual infidelity (needing fewer sexual cues to reach a point where the jealousy would be unbearable), whereas women reacted much more powerfully to cues to emotional infidelity (needing fewer emotional infidelity cues to reach a point where the jealousy would be unbearable). Finally, researchers have explored other sex differences in jealousy using implicit measures. For instance, Maner, Miller, Rouby, and Gailliot (2009) explored what targets would elicit the most jealousy in participants using implicit measures of attention. Maner et al. found that attractive same-sex rivals captured the most cognitive attention of the participants and that the sex of the participant moderated the responses, where women showed a significantly larger response to the attractiveness manipulation. This manifested itself in a number of ways, ranging from stronger memory of the rivals to forming more negative implicit evaluations. This pattern was

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expected as these traits (e.g., attractiveness in women) are the relevant cue to heterosexual men, and hence, women intuitively respond to these characteristics in a rival (e.g., Buunk & Dijkstra, 2001). Importantly, these patterns were also seen on the more overt (and traditional) measures of jealousy, demonstrating that the automatic responses were likely mirroring the more conscious responses. Although Maner et al. did not test the theory of evolved sex differences in jealousy directly, their methods could be applied to research in this area. Sch€ utzwohl’s (2005, 2006, 2008a, 2008b) work demonstrates a number of cognitive manifestations of the sex difference in jealousy (beyond responding to an overt measure of affective reactions). Men and women show the sex difference in jealousy when under cognitive load, in the ability to disengage from potential infidelity cues, as well as analogs in the kinds of information sought about a rival.

6. PHYSIOLOGICAL MANIFESTATIONS Study 2 of Buss et al. (1992) collected measures of physiological arousal (specifically measures of electodermal activity [EDA], pulse rate [PR], and electromyographic [EMG] activity) when participants imagined sexual infidelity and emotional infidelity. Using these measures, the researchers found that men had significantly higher EDA and PR activity in response to the sexual infidelity scenario (compared to the emotional infidelity scenario) and women showed higher EDA in response to the emotional infidelity scenario (compared to the sexual infidelity scenario). The result for the EMG measure was not significant, but Buss et al. noted that the pattern of results was in the theoretically predicted direction. Based on this evidence, Buss and colleagues concluded that the physiological measures showed converging evidence of an evolved predisposition to certain jealousy triggers. Mixed results on physiological measures have been seen in other studies. Grice and Seely (2000) investigated the sex difference in jealousy using the same measures as Buss et al. Grice and Seely found support for the theory when using the PR measure; results for the EDA ran counter to the results found by Buss et al. (where women showed a marginally significant increase in response to the sexual items); the EMG measure did not result in any difference between men and women. Pietrzak, Laird, Stevens, and Thompson (2002) measured four physiological responses (EDA, EMG, PR, and skin temperature), while participants engaged in three imagery tasks: neutral

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imagery, sexual infidelity imagery, and emotional infidelity imagery. Pietrzak et al. found significant theory-supportive effects across all four measures for both men and women. Harris (2000), on the other hand, used similar measures (PR, systolic blood pressure, diastolic blood pressure, and EDA) and failed to find theory-supportive effects. Beyond the questions raised by these mixed results, Harris (2000) specifically questioned the methodology used in Buss et al. (1992). Specifically, Harris suggested that Buss et al.’s findings might have stemmed from men’s greater physiological responsiveness to sexual imagery in general rather than sexual infidelity imagery in particular. To test this, in a study of only men, Harris instructed men to imagine a sexual infidelity (their partner having sexual intercourse with a rival) and an emotional infidelity (their partner falling in love with a rival) or to imagine themselves having sexual intercourse with their partner and themselves falling in love with their partner. Across the physiological measures, men showed greater reactivity to sexual imagery than to emotional imagery, but the effect did not differ based on whether the imagery involved infidelity or interaction with a partner. These results supported Harris’s contention that men’s greater physiological responsiveness to sexual imagery could explain the results found by Buss et al. Another study (Takahashi et al., 2006) attempted to move beyond EDA, PR, and EMG methodologies and instead looked at brain activity directly using fMRI. In this study, participants completed self-report measures of jealousy (featuring sexual infidelity, emotional infidelity, as well as a neutral condition) and had their brains scanned during imagery activities dealing with infidelity. The self-report methodology did not result in a significant sex difference (which is not surprising given the small sample of 11 men and 11 women). However, when brain activation was analyzed, there were differences between men’s and women’s responses to the jealousy situations. Specifically, men showed greater activation in the amygdala and the hypothalamus (which is thought to tie into reproductive behavior: Sisk & Foster, 2004), whereas women showed greater activation in the posterior superior temporal sulcus [which has been shown to relate to detection of deception (Calarge, Andreasen, & O’Leary, 2003) and violations of social norms (Takahashi et al., 2004)]. A subsequent study, Baschnagel and Edlund (2016), was designed to address a number of Harris’s (2000, 2005) critiques. Baschnagel and Edlund asked participants to respond to jealousy-provoking scenarios that dealt with emotional and sexual infidelity using continuous measures of jealousy while collecting physiological data using affective modification of the startle

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eyeblink. Baschnagel and Edlund also included a number of control items designed to allow the researchers to statistically control for men’s greater response to sexually related items more generally. Baschnagel and Edlund found a marginally significant (p ¼ .051) interaction between sex and imagery type such that men showed a greater response to sexual infidelity items (compared to the emotional infidelity items and the sexuality control items) on the startle eyeblink measure. Women had a much smaller mean difference in the startle eyeblink with slightly larger startle responses to the emotional infidelity items (p > .10). In sum, studies assessing physiological responses to infidelity scenarios suggest that when using sensitive measures (Takahashi et al., 2006) and appropriate controls (Baschnagel & Edlund, 2016), there is evidence for the sex difference in jealousy in physiological responses. However, the small number of studies and the inconsistent results across those studies highlight the importance of additional work in this area.

7. META-ANALYSES In 2003, Harris published the first meta-analysis on sex differences in jealousy as part of a critical review of the literature. Harris meta-analyzed forced-choice responses from 32 independent samples. As Harris reported, almost all of the studies used one or both of the two questions from Study 1 of Buss et al. (1992), most in the exact form, some with slight wording changes. Harris found a significant overall effect across samples (log-odds ratio: 1.00, 95% confidence interval [0.81, 1.19]). This effect was significantly moderated by sexual orientation, with heterosexual participants showing a stronger sex difference (log-odds ratio: 1.09) than gay and lesbian participants (log-odds ratio: 0.26, the negative value indicating that lesbians chose sexual infidelity as more distressing more often than did gay men). The effect was also significantly moderated by the age of the sample, with college students showing a stronger effect (log-odds ratio: 1.20) than older samples (log-odds ratio: 0.67). Geographical region did not significantly moderate the effect, but as Harris noted, relatively few samples were collected outside the United States, limiting the ability of the meta-analysis to detect geographical effects. Nine years later, Carpenter (2012) and Sagarin, Martin, et al. (2012) published meta-analyses of the literature on sex differences in jealousy, seemingly reaching opposite conclusions. Carpenter meta-analyzed 172 effect sizes from 54 articles, examining responses to both forced-choice and

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continuous measures. Sagarin et al. meta-analyzed 209 effect sizes from 40 articles, examining responses only to continuous measures. Carpenter concluded: The data were not consistent with the EP [Evolutionary Psychology] predictions because men tended to respond in the predicted manner in only the U.S. student samples, whereas the rest of the data were largely consistent with the social– cognitive theory. Specifically [with the exception of male U.S. students], both sexes tended to be more upset by emotional than sexual infidelity when forced to choose which type of infidelity was more distressing. Both sexes indicated that sexual infidelity was more distressing than emotional when asked to rate their level of distress separately for each using continuous measures (p. 25).

Sagarin, Martin, et al. (2012), in contrast, concluded: A significant, theory-supportive sex difference emerged across 45 independent samples using continuous measures of responses to hypothetical infidelities … A significant, theory-supportive effect also emerged across seven studies assessing reactions to actual infidelities … Results demonstrate that the sex difference in jealousy neither is an artifact of response format nor is limited to responses to hypothetical infidelities (p. 595).

These opposite conclusions were not based on differing statistical results— the statistical results themselves were quite consistent across both meta-analyses. The critical difference was the interpretation of the results. Carpenter (2012) based his conclusions on the simple effect of infidelity type within each sex: Controversies have arisen concerning which statistical comparison is the proper way to test the EP predictions (Buller, 2005; Buss & Haselton, 2005; Harris, 2005; Sagarin, 2005). Buller (2005) explained that when one conducts a study measuring which type of infidelity is more upsetting to which sex, there are a number of ways to analyze the resulting data set. Buller suggested that each sex should be examined separately, and the size of the preference for that sex’s predicted type of infidelity should be measured. Essentially, one looks at how much more men are distressed by sexual infidelity than emotional and then one looks at how much more women are distressed by emotional infidelity than sexual. This choice preserves the original EP predictions of Buss et al. (1992) that men would be more likely to be upset by sexual infidelity than emotional and women would be more likely to be upset by emotional than sexual (Lishner, Nguyen, Stocks, & Zillmer, 2008). Buller’s recommendations will be used here to analyze my data (p. 29).

Then, based on the findings that (a) both men and women tended to choose the emotional infidelity as more distressing on forced-choice measures, and (b) both men and women tended to rate the sexual infidelity as more jealousy provoking on continuous measures, Carpenter concluded that the data

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support the social-cognitive perspective rather than the evolutionary psychology perspective. Sagarin, Martin, et al. (2012) based their conclusions, instead, on the participant sex by infidelity-type interaction: One controversy in this area stems from a proper assessment of the predictions made by the theory of evolved sex differences in jealousy. For example, consider a study in which women and men indicate using continuous measures how much jealousy they would feel in response to emotional infidelity and in response to sexual infidelity. Sagarin (2005) and Edlund and Sagarin (2009) have argued that the only effect relevant to the theory is the Participant Sex  Infidelity Type interaction: Men should show a relatively greater difference than women in their responses to sexual infidelity and emotional infidelity (i.e., men’s responses to sexual infidelity minus their responses to emotional infidelity are predicted to be greater than women’s responses to sexual infidelity minus their responses to emotional infidelity).

Then, based on the findings of an overall interaction in the theorysupportive direction, Sagarin et al. concluded that the data support the theory of evolved sex differences in jealousy. Carpenter (2012) notes that his data also show this pattern of interaction: On the other hand, even though most people chose emotional infidelity as more distressing, heterosexual women were more likely to choose emotional infidelity as more distressing than heterosexual men. This result is consistent with EP predictions if one uses the more liberal criteria of Sagarin (2005) that only required an interaction between participants’ sex and the type of infidelity that distresses them more (p. 33).

Our perspective is that all three meta-analyses show consistent patterns in which men, relative to women, report greater jealousy in response to sexual infidelity than in response to emotional infidelity, and that this pattern appears on both forced-choice measures (Carpenter, 2012; Harris, 2003) and continuous measures (Carpenter, 2012; Sagarin, Martin, et al., 2012). Further, based on our earlier argument that the simple effects of infidelity type within each sex are irrelevant for the theory of evolved sex differences in jealousy, we interpret all of the meta-analyses as offering support for the theory. That said, we also acknowledge that these simple effects are inconsistent across forced-choice and continuous measures, based on Carpenter’s findings that both men and women tend to rate the emotional infidelity as causing greater distress on forced-choice measures but the sexual infidelity as provoking more jealousy on continuous measures. Future research that offered insight into this inconsistency would be of value.

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Carpenter (2012) and Sagarin, Martin, et al. (2012) also identified a number of moderators of the sex difference in jealousy. Consistent with Harris (2003), Carpenter found that lesbian and gay respondents tended to show smaller or reversed patterns of responses on forced-choice measures compared to heterosexual respondents. Carpenter also found cultural differences on forced-choice measures, such that men outside the United States tended to choose the emotional infidelity as causing greater distress, whereas men within the United States tended to choose the sexual infidelity as causing greater distress. Finally, Carpenter found that scenarios that precluded a double-shot interpretation (DeSteno & Salovey, 1996) tended to show smaller sex differences compared to scenarios that allowed for a double-shot interpretation, suggesting that the double-shot hypothesis might explain some, but not all, of the sex difference in jealousy. Sagarin, Martin, et al. (2012) also found evidence for moderation: Measured emotion significantly moderated effect size. Effects were strongest when measures assessed distress/upset (g* ¼ 0.337) and jealousy (g* ¼ 0.309). Other commonly measured negative emotions yielded weaker effects, including hurt (g* ¼ 0.161), anger (g* ¼ 0.074), and disgust (g* ¼ 0.012). Across the 45 independent samples, six significant moderators emerged: random sampling, population type (student vs. nonstudent samples), age, inclusion of a forced-choice question, number of points in the response scale, and year of publication (p. 595).

Taken together, we conclude that these meta-analyses offer strong evidence that the sex difference in jealousy occurs in response to hypothetical infidelity scenarios when using either forced-choice measures or continuous measures. We also believe that there is significant meta-analytic evidence that the sex difference in jealousy occurs in response to actual infidelities as well.

8. SEXUAL ORIENTATION AND THE SEX DIFFERENCE IN JEALOUSY As the meta-analyses by Carpenter (2012) and Harris (2003) demonstrate, the sex difference in jealousy has proven quite robust when measured using forced-choice infidelity dilemmas. There is, however, one moderator that consistently attenuates or even reverses the sex difference: sexual orientation. As noted earlier, in Harris’s meta-analysis, heterosexual participants showed a stronger sex difference (log-odds ratio: 1.09) than gay and lesbian participants (log-odds ratio: 0.26, the negative value indicating that lesbians chose sexual infidelity as more distressing more often than did gay men). In Carpenter’s meta-analysis, three forced-choice scenarios included

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samples with heterosexual, lesbian, and gay participants. These scenarios showed overall effects of r ¼ .40, r ¼ .49, and r ¼ .33. In contrast, on the same scenarios, the samples of lesbian and gay participants showed effects of r ¼ .23, r ¼ .09, and r ¼ .18. Likewise, Bailey, Gaulin, Agyei, and Gladue (1994) reported significant sex differences in jealousy among heterosexual women and men, but no sex differences in jealousy among lesbians and gay men. Sexual orientation has also been examined in the relationship between the unfaithful partner and the rival. Sagarin et al. (2003) presented heterosexual participants with a forced-choice infidelity dilemma in which their opposite sex partner had become interested in a rival of either the opposite sex or the same sex. Participants considering a rival of the opposite sex as the partner (i.e., men contemplating their female partners becoming involved with a rival man, and women contemplating their male partners becoming involved with a rival woman) showed the typical sex difference in jealousy, with significantly more men than women choosing the sexual infidelity as more distressing. Participants considering a rival of the same sex as the partner (i.e., men contemplating their female partners becoming involved with a rival woman, and women contemplating their male partners becoming involved with a rival man) showed no sex difference in jealousy. In an attempt to account for the finding that the sex difference in jealousy tends to disappear among lesbians and gay men as well as among heterosexual women and men contemplating their opposite sex partners becoming involved with a rival of the same sex as the partner (Sagarin et al., 2003), Sagarin, Becker, Guadagno, Wilkinson, and Nicastle (2012) offer “a reproductive threat-based model of evolved sex differences in jealousy that predicts that the sexes will differ only when the jealous perceivers’ reproductive outcomes are differentially at risk” (p. 487; see Bailey et al., 1994; Harris, 2002; Sheets & Wolfe, 2001, for prior theorizing on the relation between sexual orientation and the sex difference in jealousy). As Sagarin et al. explain: The reproductive threat-based model of evolved sex differences in jealousy is based on two premises: (a) Non-exclusive same-sex sexual behavior (both by individuals in their sexual behavior and by individuals’ partners in their extra-pair sexual behavior) occurred sufficiently often in the environment of evolutionary adaptedness (EEA) that an evolved response could have taken it into account, and (b) The fitness implications of an infidelity in which a mating partner became involved with a rival of the same sex were sufficiently different from the implications of an infidelity in which a mating partner became involved with a rival of the opposite sex, and, likewise, the fitness implications of an infidelity committed by a same-

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sex sexual partner were sufficiently different from the implications of an infidelity committed by an opposite-sex sexual partner, that it would have been adaptively beneficial for a sexually dimorphic jealous response to take these differences into account (pp. 489–490).

Sagarin, Becker, et al. (2012) tested the model with “a web-based study in which lesbians, gay men, bisexual women and men, and heterosexual women and men responded to a hypothetical infidelity scenario with the sex of the rival randomly determined” (p. 487). Consistent with the reproductive threat-based model of evolved sex differences in jealousy, the sex difference in jealousy emerged only among heterosexual women and men contemplating an opposite-sex infidelity. Heterosexual women and men contemplating a same-sex infidelity, and lesbians and gay men contemplating either an opposite-sex infidelity or a same-sex infidelity showed no sex difference. Scherer, Akers, and Kolbe (2013) tested the model with a sample of bisexual women and men. Participants reported whether they were currently dating a man or a woman and responded to the first forced-choice infidelity question from Study 1 of Buss et al. (1992). Consistent with the reproductive threat-based model of evolved sex differences in jealousy, bisexual men dating women and bisexual women dating men showed a significant sex difference in jealousy, but bisexual men dating men and bisexual women dating women showed no sex difference.

9. OTHER MODERATORS OF THE SEX DIFFERENCE IN JEALOUSY In addition to sexual orientation, researchers have proposed and tested a number of other moderators of the sex difference in jealousy. These include relationship experience (Buss et al., 1992), relationship status (Becker, Sagarin, Guadagno, Millevoi, & Nicastle, 2004), infidelity experience (Sagarin et al., 2003; Tagler, 2010), mate value (Edlund & Sagarin, 2014), demographic variables (Zengel et al., 2013), attachment style (Brase et al., 2014; Levy & Kelly, 2010), sex-roles, sociosexual orientation, trust, and beliefs (Brase et al., 2014), and type of emotion (Sagarin, Martin, et al., 2012). Buss et al. (1992) theorized that experience in romantic relationships would increase men’s jealousy in response to sexual infidelity and women’s jealousy in response to emotional infidelity. As Buss et al. explain: “The rationale was that direct experience of the relevant context during

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development may be necessary for the activation of the sex-linked weighting of jealousy activation” (p. 254). Women showed a small, nonsignificant difference in their choice of which type of infidelity caused greater distress based on relationship experience. Men, in contrast, were significantly more likely to choose the sexual infidelity as causing greater distress if they had been in a committed sexual relationship (compared to men who had not been in a committed sexual relationship). Becker et al. (2004) found that participants in a committed relationship reported more intense reactions to both sexual infidelity and emotional infidelity compared to participants who were casually dating and participants who were not in a relationship (relationship status did not interact with infidelity type or gender). Infidelity experience has produced inconsistent results when tested as a possible moderator of sex differences in jealousy. Sagarin et al. (2003) found that “male victims and female perpetrators of infidelity reported greater distress in response to a sexual infidelity” (p. 17). Experience as a perpetrator of infidelity did not significantly impact men’s responses, and experience as a victim of infidelity did not significantly impact women’s responses. Tagler (2010), in contrast, found that among adults, “sex differences were found only among adults who had not previously experienced real partner infidelity” (p. 353). However, Sagarin, Martin, et al.’s (2012) meta-analysis found a significant sex difference in jealousy when past victims of infidelity were asked about their reactions to the sexual aspects and the emotional aspects of the infidelity. Edlund and Sagarin (2014) examined mate value as a possible moderator. As Edlund and Sagarin explain: “Based on the research that suggests that evolutionarily influenced preferences are magnified in higher mate value participants (e.g., Millar, 2013), we hypothesized that participants who were higher in mate value would show increased levels of sex differentiated jealousy in response to an actual infidelity” (p. 74). Participants who had experienced an infidelity were asked “To what degree did you experience jealousy over the emotional/sexual aspects of your partner’s infidelity” (p. 75). Mate value significantly moderated the sex difference in jealousy, such that participants higher in mate value showed a stronger sex difference in jealousy in the theory-consistent direction. Zengel et al. (2013) examined sex differences in jealousy as part of a large survey conducted on a representative sample of American adults. The survey included a number of demographic variables. Sex differences in jealousy emerged on the forced-choice measures but not on the continuous

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measures. On the forced-choice measures, the sex difference in jealousy in response to actual infidelity was not moderated by any of the demographic variables. The sex difference in jealousy in response to hypothetical infidelity scenarios, in contrast, was moderated by a number of demographic variables including “household size, the number of adult household members, the presence of children between the age of 2 and 5, household income, status of being the head of the household, and the ownership status of the living quarters” (p. 49). The psychological mechanisms underlying these moderating effects are unclear at the present time but are worth further exploration. Levy and Kelly (2010) examined attachment style as a possible moderator of sex differences in jealousy. Participants responded to the first question of Study 1 of Buss et al. (1992) and indicated their attachment style on Bartholomew and Horowitz’s (1991) Relationship Questionnaire. Participants with fearful and dismissing attachment styles showed significant sex differences in jealousy, but participants with secure and preoccupied attachment styles showed nonsignificant sex differences. Brase et al. (2014) also examined attachment style as a possible moderator; despite finding significant sex differences in jealousy on all of their forced-choice measures (p’s < .001), they found no significant participant sex by attachment style interaction (p ¼ .400). Brase et al. (2014) tested a number of potential moderators beyond attachment style (“Bem Sex Role Inventory, Personal Attributes Questionnaire, Male Role Norms Scale, Attitudes Toward Women Scale, Rotter Interpersonal Trust Scale, Sociosexual Orientation Inventory, and Culture of Honor Scale,” p. 86) in a regression equation that included participant sex, the moderators, and the interactions between participant sex and each of the moderators. No significant interactions emerged, suggesting that these variables do not moderate the sex difference in jealousy when all variables are tested simultaneously. Finally, a number of studies have measured men’s and women’s responses to sexual infidelity and emotional infidelity across a variety of emotions, sometimes (but not always) including jealousy (e.g., Becker et al., 2004; Geary, Rumsey, Bow-Thomas, & Hoard, 1995; Shackelford, LeBlanc, & Drass, 2000). Results have typically demonstrated differences across different emotions. As noted earlier, Sagarin, Martin, et al. (2012) examined emotion as a possible moderator meta-analytically, finding that the sex difference was strongest for distress/upset and jealousy and weaker for other negative emotions.

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10. WHERE THE DEBATE STANDS As the breadth of research we have reviewed reveals, the theory of evolved sex differences in jealousy has attracted substantial support and faced numerous challenges. The strongest support has come from studies using the forced-choice hypothetical infidelity dilemmas pioneered by Buss et al. (1992). Meta-analyses by Harris (2003) and Carpenter (2012) have found that, when faced with a choice of which type of infidelity (sexual vs emotional) would cause greater distress, men consistently choose the sexual infidelity more often than do women. Strong support has also come from studies using continuous measures to assess responses to hypothetical infidelities. Although individual studies sometimes fail to produce a significant sex difference, a meta-analysis by Sagarin, Martin, et al. (2012) found a highly significant overall effect across continuous measure studies. Some support has come from studies measuring retrospective reports of actual infidelity experiences, but the limited number of such studies has left their support tenuous. In particular, although Sagarin et al. found a significant overall effect across seven studies measuring responses to actual infidelities, “the classic fail-safe N indicated that only 12 null studies would be needed to bring the overall p value to >.05” (p. 609). Likewise, some support has come from studies measuring physiological responses to imagined infidelities, but the limited number of physiological studies and the somewhat inconsistent findings also limit the strength of support they provide. Finally, some support has come from creative methodologies that extend beyond the self-report, including cognitive effects identified by Sch€ utzwohl (2005, 2006, 2008a, 2008b) and an analysis of reality television conducted by Kuhle (2011). At the same time, critics of the theory have challenged it on a number of grounds. These challenges have included questions as to whether the sex difference in jealousy is a function of confounding sex differences in the interpretation of the questions posed, whether the sex difference is an artifact of the forced-choice methodology, whether actual experiences with jealousy mirror reactions to hypothetical scenarios, whether automatic or deliberative processes matter more in the jealousy reactions, which statistical comparisons are most appropriate for testing the theory, and whether observed moderators weaken support for the theory. Although a number of these challenges have been addressed (e.g., DeSteno & Salovey’s, 1996, double-shot hypothesis; DeSteno et al.’s, 2002, concerns

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regarding continuous measures; Harris’s, 2002, concerns regarding responses to actual infidelity experiences), others remain unanswered (e.g., Kato’s, 2014, alternative explanation based on men’s greater tendency than women’s toward vivid sexual imagery) or only partially answered (e.g., Harris’s, 2000, alternative explanation based on men’s greater physiological reactivity than women’s in response to sexual imagery). In addition, as noted earlier, some areas of research have tended to produce theory-supportive findings, but this support is weakened by the limited number of studies in those areas. Finally, a number of potential moderators of the sex difference in jealousy have been identified, but additional work is needed to determine which moderators support the theory, which threaten the theory, and which motivate refinement of the theory. Nevertheless, although there are still many questions to be answered (which we will address in the next section), we view the weight of the evidence as supporting the evolutionary psychological account of the sex difference in jealousy. However, despite the consistent picture that emerges across the literature in support of the theory, our impression is that various subfields of psychology have reached very disparate conclusions on where the debate stands. At conferences and in discussions with our colleagues, many evolutionary psychologists express the view that the debate is essentially over—that the theory of evolved sex differences in jealousy has emerged victorious. (To our consternation, we have found that this view is often shared by editors and reviewers in journals that specialize in evolutionary psychology; we have received multiple reviews over the years suggesting that the theorysupportive evidence in our manuscripts was superfluous—the debate was over and the evolutionary psychological theory offered by Buss et al., 1992, had already won.) Our discussions with social psychological colleagues have gone quite differently. In response to our work, colleagues have expressed surprise that we were wasting our time on a disproved theory and concern that working on this line of inquiry would stunt our careers. These colleagues seemed unaware of how many studies had found evidence of the effect. In submitting manuscripts to social psychological (and general psychological) journals, we have received reviews that placed little value on data that ostensibly supported an already refuted theory or, at best, that suggested that debates on the “nuances” of an evolutionary psychological theory would not appeal to readers of the journal and encouraged us to submit to a subfield journal. We acknowledge the possibility that our observations were simply an attempt to find an external attribution for our manuscripts being rejected.

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But we also wondered whether there was something more systematic going on. Was there a verifiable reason why researchers from different theoretical perspectives would reach such different conclusions on the state of the literature? Our initial suspicion was that many more of the theory-refutational studies were published in journals that would be read by social (and more general) psychologists, whereas the theory-supportive studies were most typically published in journals that would not commonly be read by social (and more general) psychologists. To explore this question, we compiled a list of journals that have published articles related to the sex difference in jealousy and asked colleagues to rate the perceived likelihood of that journal being regularly read by general, social, and evolutionary psychologists. We did not discuss our hypothesis— we simply told the colleagues that it was in support of a manuscript we were preparing. Two self-identified social psychologists and two self-identified evolutionary psychologists provided ratings of their perception of the journals readership. In looking at the ratings of readership, we saw a large degree of overlap between the general readership category and the social psychology category, so we collapsed across the categories. Next, we coded all of the articles related to the sex difference in jealousy reviewed in this paper for whether the conclusions and results offered by the original authors were framed as supporting or refuting the theory of evolved sex differences in jealousy. A total of 58 articles were coded, 32 supportive, 25 refutational, and 1 that did not fit into either category. When we crossed these codes with the categorization of journals, our analysis suggests that social psychologists are likely to encounter a higher proportion of theoryrefutational articles (they were likely to have seen 44% [11/25] of the published refutational papers) than theory-supportive articles (they were likely to have seen 25% [8/32] of the published supportive papers). In contrast, evolutionary psychologists are likely to encounter a slightly higher proportion of theory-supportive articles (they were likely to have seen 53% [17/32] of the published supportive papers) than theory-refutational articles (they were likely to have seen 48% [12/25] of the published refutational papers. These data support our supposition that social and evolutionary psychologists may reach different conclusions on that state of the literature based solely on the kinds of journals they regularly read. We should note that this effect does not appear to represent a more general bias against evolutionary psychological studies in social psychology journals. Some of the top social and general psychology journals have published numerous evolutionary psychological articles, and, indeed, Buss et al. (1992) appeared in the prestigious

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journal Psychological Science. But reflecting the trend we observed in our data, the most recent paper in Psychological Science on this topic (DeSteno, 2010) goes against the theory. Why might this have occurred? We speculate that it might have to do with the actual (and anticipated) review process at social psychology journals and at evolutionary psychology journals. At social psychology journals, new evolutionary psychological theories and findings might be seen as interesting, novel, and potentially publishable. In addition, at these journals, data that challenge existing evolutionary psychological theories might also be seen as interesting, novel, and publishable. Data that support existing theories, in contrast, might be seen as less interesting and novel, and because evolutionary psychological findings might not be seen as central to the mission of social psychology journals, the lack of interest and novelty might lead to a decision not to publish and a recommendation to seek publication in evolutionary psychology journals. At evolutionary psychology journals, in contrast, data supporting a long-standing theory might be seen as interesting and relevant for the mission of the journal, even if the data are not completely novel. In addition, we suspect that critics of the theory are more likely to be invited to review papers sent to social psychology journals than they are to be invited to review papers sent to evolutionary psychology journals. Whatever the reason, it does appear that psychologists from different subfields are being exposed to different portions of the literature and that this differential exposure is leading to different conclusions regarding the state of the theory. We anticipate, though, that the field’s recent reappraisal of the importance of replication (and the willingness of journals to publish replication studies) might help to mitigate this effect in the future.

11. LOOKING TOWARD THE FUTURE Having looked back at 25 years of work on the sex difference in jealousy, we can conclude with confidence that there are meaningful differences between men and women in terms of how they experience jealousy in romantic relationships. Much work remains to be done, however, to more fully understand this effect. We believe that researchers should continue to investigate moderators of the sex difference in jealousy, expand the work looking at physiological manifestations of the jealousy, develop methodologies to examine real-time experiences of jealousy, further explore the

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theoretical underpinnings of the sex difference in jealousy, and consider how cross-cultural variability impacts the effect and what this means for the underlying theory. The first avenue for exploration is the search for moderators that may help to explain what factors lead some men to be more distressed by emotional infidelity and what factors lead some women to be more distressed by sexual infidelity. Past research has suggested a number of possible candidates including relationship experience (Buss et al., 1992), relationship status (Becker et al., 2004), infidelity experience (Sagarin et al., 2003; Tagler, 2010), mate value (Edlund & Sagarin, 2014), and attachment style (Levy & Kelly, 2010). Replication and extension of these findings would be of value, as would research testing new potential moderators. One fruitful area to examine for moderators might be social and learning influences on reactions to infidelity. It seems plausible, for example, that as adolescents develop views on sex and sexuality, peers may influence their perceptions of the appropriate responses to infidelity. Likewise, research (e.g., Amato & Booth, 2001) has shown that many characteristics of parents’ relationships get transmitted to the next generation. Thus, parents’ views of infidelity might influence their children. We would also like to see research that tried to disentangle the seemingly discrepant findings that men and women report greater jealousy in response to sexual infidelity when using continuous measures but men and women more commonly choose the emotional infidelity as causing greater distress in response to a forced-choice measure (although, as discussed at length above, we do not see this discrepancy as threatening the theory because both methodologies have been shown to produce theory-supportive sex differences). To date, there have been no studies that have attempted to explain this finding, and we believe that insight into this would represent a valuable contribution to both evolutionary psychology and psychometrics. As we reviewed, evidence looking at men’s and women’s jealous reactions is varied and, for some methodologies, quite robust. However, one area that has received relatively little attention is the physiological manifestations of jealousy. This gap in the research is particularly problematic because Harris’s (2000) critiques of the techniques used by Buss et al. (1992) remain mostly unanswered. What physiological techniques should be used by researchers examining the sex difference in jealousy? fMRI techniques (such as the approach used by Takahashi et al., 2006), when feasible, represent a promising avenue for future research. Similarly, transcranial magnetic stimulation (TMS) may provide an option for investigating what regions in the brain are responsible

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for the sex difference in jealousy. Alternatively, startle eyeblink (as used by Baschnagel & Edlund, 2016) represents a more cost effective method of looking at physiological responses than fMRI and TMS. Other techniques such as EDA, PR, and EMG have shown mixed results in previously published research (e.g., Buss et al., 1992; Grice & Seely, 2000; Harris, 2000; Pietrzak et al., 2002). These techniques are relatively accessible to researchers and quality research using these measures would be of particular use to the field. Finally, cortisol and testosterone (hormones associated with stress and dominance/aggression, respectively) can be measured with salivary assays and could provide useful measures of reactions to relationship threats. Next, we recommend that researchers consider methods of examining real-time experiences of jealousy. One study (i.e., Kuhle, 2011) has attempted to explore this using a reality TV sample (although, as noted by Kuhle, the target program, “Cheaters,” has faced accusations that some of the scenarios were staged). However, an investigation of real-time jealousy experiences outside of reality television would have significant ethical issues to overcome. Certainly, any investigation that could damage an existing couple’s relationship would be unethical to do; however, one avenue that may exist for researchers to explore would be to create relationships in the laboratory between strangers using a guided disclosure approach (Aron, Melinat, Aron, Vallone, & Bator, 1997). Once the relationship is established, researchers could test jealous reactions in response to different types of relationship threats. This approach would allow for the testing of real-time reactions, although it might not generalize to jealousy in previously established sexual relationships. Other techniques for generating jealousy effectively and ethically have been developed by DeSteno, Valdesolo, and Bartlett (2006) and Harmon-Jones, Peterson, and Harris (2009) and would be worth examining for possible application to the study of sex differences in jealousy. We also think that researchers should continue to explore the theoretical underpinnings of the sex difference in jealousy. As noted earlier, the theory of evolved sex differences in jealousy is a combination of two theories: (1) the theory that men’s unique challenge of paternal uncertainty selected for greater jealousy in response to sexual infidelity, and (2) the theory that women’s unique challenge of ensuring paternal investment selected for greater jealousy in response to emotional infidelity. And also as noted earlier, these two theories could, in principle, be tested separately, although few extant studies allow for this. We see great value in the design of future studies to disentangle and separately test these two theories.

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One additional avenue for theoretical exploration has been suggested by Buller (2005). Buller argues that, although men’s offer of paternal investment was sufficient to motivate women to enter into pair bonds, women’s offer of increased paternal certainty was insufficient to motivate men to enter pair bonds. Buller cites two pieces of evidence in support of his argument. First, Buller cites Hawkes, Rogers, and Charnov’s (1995) game-theoretic models that demonstrate “even in a model of a pair-bonded population in which males are assured of paternity, and hence assured that their parental care is not misspent, males still allocate very little effort to parental care and the vast majority of their effort to promiscuous mating” (p. 266). Ultimately, even in models where men were assured of their paternity, the models could not account for the observed levels of paternal care of children found in humans. Thus, assurance of increased paternal certainty seems insufficient to motivate men to provide paternal investment. Second, Buller (2005) cites Smuts and Gubernick’s (1992) mating effort hypothesis, which considers male parental care as mating effort, not parenting effort. Smuts and Gubernick note that, across species, paternity certainty is unrelated to males’ willingness to provide care. Further, in some species with male care (e.g., savanna baboons, vervet monkeys, and gelada), males frequently provide care to a female’s offspring prior to mating with the female, with the female often providing subsequent paternity opportunities, presumably in response to the male’s willingness to provide care (van Schaik & Paul, 1996). This suggests an alternative model for pair bonding: Men offer women paternal investment, and women offer men paternity opportunities. Buller then applies this argument to sex differences in jealousy, positing that women will feel particularly jealous in response to situations that threaten paternal investment (i.e., emotional infidelity) and that men will feel particularly jealous in response to situations that threaten paternity opportunities (e.g., sexual infidelity, but also other types of situations such as rejection by a romantic target after costly courting). Buller’s (2005) arguments, if correct, would require a modification to the theory—namely, the traditional paternity uncertainty explanation would represent a subset of scenarios where one might expect to see increases in men’s jealousy. One would also expect to see men’s jealousy increase any time significant mating effort has been expended and there is a loss of perceived paternity opportunities even if cuckolding is not possible. We look forward to future tests of Buller’s intriguing argument. Finally, we believe researchers should further explore the reasons for the cross-cultural variability of the sex difference in jealousy. Although the sex

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difference in jealousy has been found in nearly every culture across the world that has been investigated, rates of endorsement of the various infidelity scenarios vary widely from a minority of men endorsing the sexual infidelity option as the most distressing in some Western samples to 96% of men endorsing the sexual infidelity option in the Himba (Scelza, 2014). Women also show significant variability across cultures with fewer than 10% of women endorsing the sexual infidelity option in some Western samples to over half of women endorsing the sexual infidelity option among the Himba. We have argued in this paper that evolutionary influences are an important contributor to men’s and women’s responses to infidelity; however, this represents only one portion that factors into a person’s observed emotional reaction (Edlund & Sagarin, 2009). Many additional factors contribute to an observed reaction, ranging from individual differences, to local ecological concerns, to cultural constraints. A deeper exploration of the issues associated with cross-cultural variability and how they interact with evolutionary influences (e.g., Schmitt, 2015) would be of value.

12. CODA Looking back at 25 years of research investigating the sex difference in jealousy, we believe that the weight of the evidence supports the conclusion that the phenomenon is a real effect with evolutionary underpinnings. We also believe that the thrust and parry that has occurred between the theory’s staunch critics and its staunch proponents has strengthened the scientific standing of this line of inquiry, and we look forward to the next 25 years of the debate.

ACKNOWLEDGMENTS We want to thank Martie Haselton for her insightful comments during the development of this paper. Martie’s questions about the theory and its predictions challenged us to think about the effect in new ways. We also want to thank Adrianne Edlund and Kimberly Lawler-Sagarin; our discussions with them have also helped to hone our thinking about this line of research.

REFERENCES Amato, P. R., & Booth, A. (2001). The legacy of parents’ marital discord: Consequences for children’s marital quality. Journal of Personality and Social Psychology, 81(4), 627–638. http://dx.doi.org/10.1037/0022-3514.81.4.627. Aron, A., Melinat, E., Aron, E. N., Vallone, R. D., & Bator, R. J. (1997). The experimental generation of interpersonal closeness: A procedure and some preliminary findings. Personality and Social Psychology Bulletin, 23, 363–377.

Sex Differences in Jealousy

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Bailey, J. M., Gaulin, S., Agyei, Y., & Gladue, B. A. (1994). Effects of gender and sexual orientation on evolutionary relevant aspects of human mating psychology. Journal of Personality and Social Psychology, 66, 1081–1093. Barrett, H. C., Frederick, D. A., Haselton, M. G., & Kurzban, R. (2006). Can manipulations of cognitive load be used to test evolutionary hypotheses? Journal of Personality and Social Psychology, 91(3), 513–518. http://dx.doi.org/10.1037/0022-3514.91.3.513. Bartholomew, K., & Horowitz, L. M. (1991). Attachment styles among young adults: A four-category model of attachment. Journal of Personality and Social Psychology, 61, 226–241. Baschnagel, J., & Edlund, J. E. (2016). Affective modification of the startle eye-blink response during sexual and emotional infidelity scripts. Evolutionary Psychological Science, 2, 114–122. Becker, D. V., Sagarin, B. J., Guadagno, R. E., Millevoi, A., & Nicastle, L. D. (2004). When the sexes need not differ: Emotional responses to the sexual and emotional aspects of infidelity. Personal Relationships, 11, 529–538. Berman, M. I., & Frazier, P. A. (2005). Relationship power and betrayal experience as predictors of reactions to infidelity. Personality and Social Psychology Bulletin, 31(12), 1617–1627. http://dx.doi.org/10.1177/0146167205277209. Brase, G. L., Adair, L., & Monk, K. (2014). Explaining sex differences in reactions to relationship infidelities: Comparisons of the roles of sex, gender, beliefs, attachment, and sociosexual orientation. Evolutionary Psychology, 12(1), 73–96. http://dx.doi.org/ 10.1177/147470491401200106. Buller, D. J. (2005). Adapting minds: Evolutionary psychology and the persistent quest for human nature. Cambridge, MA: MIT Press/Bradford Books. Buss, D. M. (1988). From vigilance to violence: Tactics of mate retention in American undergraduates. Ethology & Sociobiology, 9(5), 291–317. http://dx.doi.org/10.1016/0162-3095 (88)90010-6. Buss, D. M. (1989). Conflict between the sexes: Strategic interference and the evocation of anger and upset. Journal of Personality and Social Psychology, 56(5), 735–747. http://dx.doi. org/10.1037/0022-3514.56.5.735. Buss, D. M., & Haselton, M. G. (2005). The evolution of jealousy. Trends in Cognitive Science, 9, 506–507. Buss, D. M., Larsen, R. J., Westen, D., & Semmelroth, J. (1992). Sex differences in jealousy: Evolution, physiology, and psychology. Psychological Science, 3, 251–255. Buss, D. M., Shackelford, T. K., Kirkpatrick, L. A., Choe, J. C., Lim, H. K., Hasegawa, M., et al. (1999). Jealousy and the nature of beliefs about infidelity: Tests of competing hypotheses about sex differences in the United States, Korea, and Japan. Personal Relationships, 6, 125–150. Buunk, B. P., & Dijkstra, P. (2001). Evidence from a homosexual sample for a sex-specific rival-oriented mechanism: Jealousy as a function of a rival’s physical attractiveness and dominance. Personal Relationships, 8(4), 391–406. http://dx.doi.org/10.1111/ j.1475-6811.2001.tb00047.x. Calarge, C., Andreasen, N. C., & O’Leary, D. S. (2003). Visualizing how one brain understands another: A PET study of theory of mind. The American Journal of Psychiatry, 160(11), 1954–1964. http://dx.doi.org/10.1176/appi.ajp.160.11.1954. Carpenter, C. J. (2012). Meta-analyses of sex differences in responses to sexual versus emotional infidelity: Men and women are more similar than different. Psychology of Women Quarterly, 36, 25–37. Daly, M., Wilson, M., & Weghorst, S. J. (1982). Male sexual jealousy. Ethology and Sociobiology, 3, 11–27. DeSteno, D. (2010). Mismeasuring jealousy: A cautionary comment on Levy and Kelley. Psychological Science, 21, 1355–1356.

300

J.E. Edlund and B.J. Sagarin

DeSteno, D., Bartlett, M. Y., Braverman, J., & Salovey, P. (2002). Sex differences in jealousy: Evolutionary mechanism or artifact of measurement? Journal of Personality and Social Psychology, 83(5), 1103–1116. http://dx.doi.org/10.1037/0022-3514.83.5.1103. DeSteno, D. A., & Salovey, P. (1996). Evolutionary origins of sex differences in jealousy? Questioning the “fitness” of the model. Psychological Science, 7, 367–372. DeSteno, D., Valdesolo, P., & Bartlett, M. Y. (2006). Jealousy and the threatened self: Getting to the heart of the green-eyed monster. Journal of Personality and Social Psychology, 91, 626–641. Edlund, J. E., Heider, J. D., Scherer, C. R., Farc, M. M., & Sagarin, B. J. (2006). Sex differences in jealousy in response to actual infidelity experiences. Evolutionary Psychology, 4, 462–470. Edlund, J. E., & Sagarin, B. J. (2009). Sex differences in jealousy: Misinterpretation of non-significant results as refuting the theory. Personal Relationships, 16, 67–78. Edlund, J. E., & Sagarin, B. J. (2014). The mate value scale. Personality and Individual Differences, 64, 72–77. Engelbrecht, J. L., & Edlund, J. E. (2016). Formatting and presentation as confounds in online studies. Quantitative Methods in Psychology, 12(3), 243–252. Feldman Barrett, L., Robin, L., Pietromonaco, P. R., & Eyssell, K. M. (1998). Are women the “more emotional” sex? Evidence from emotional experiences in social context. Cognition and Emotion, 12, 555–578. Gaunt, R., Sindic, D., & Leyens, J. (2005). Intergroup relations in soccer finals: People’s forecasts of the duration of emotional reactions of in-group and out-group soccer fans. The Journal of Social Psychology, 145(2), 117–126. http://dx.doi.org/10.3200/SOCP. 145.2.117-126. Geary, D. C., Rumsey, M., Bow-Thomas, C. C., & Hoard, M. K. (1995). Sexual jealousy as a facultative trait: Evidence from the pattern of sex differences in adults from China and the United States. Ethology and Sociobiology, 16, 355–383. Green, M. C., & Sabini, J. (2006). Gender, socioeconomic status, age, and jealousy: Emotional responses to infidelity in a national sample. Emotion, 6(2), 330–334. http:// dx.doi.org/10.1037/1528-3542.6.2.330. Greenwald, A. G., McGhee, D. E., & Schwartz, J. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. http://dx.doi.org/10.1037/0022-3514.74.6.1464. Grice, J. W., & Seely, E. (2000). The evolution of sex differences in jealousy: Failure to replicate previous results. Journal of Research in Personality, 34(3), 348–356. http:// dx.doi.org/10.1006/jrpe.2000.2284. Harmon-Jones, E., Peterson, C. K., & Harris, C. R. (2009). Jealousy: Novel methods and neural correlates. Emotion, 9, 113–117. Harris, C. R. (2000). Psychophysiological responses to imagined infidelity: The specific innate modular view of jealousy reconsidered. Journal of Personality and Social Psychology, 78(6), 1082–1091. http://dx.doi.org/10.1037/0022-3514.78.6.1082. Harris, C. R. (2002). Sexual and romantic jealousy in heterosexual and homosexual adults. Psychological Science, 13, 7–12. Harris, C. R. (2003). A review of sex differences in sexual jealousy, including self-report data, psychophysiological responses, interpersonal violence, and morbid jealousy. Personality and Social Psychology Review, 7, 102–128. Harris, C. R. (2005). Male and female jealousy, still more similar than different: Reply to Sagarin (2005). Personality and Social Psychology Review, 9, 76–86. Harris, C. R., & Christenfeld, N. (1996). Gender, jealousy, and reason. Psychological Science, 7, 364–366. Hawkes, K., Rogers, A. R., & Charnov, E. L. (1995). The male’s dilemma: Increased offspring production is more paternity to steal. Evolutionary Ecology, 9, 662–677.

Sex Differences in Jealousy

301

Hupka, R. B. (1991). The motive for the arousal of romantic jealousy: Its cultural origin. In P. Salovey (Ed.), The psychology of jealousy and envy (pp. 252–270). New York, NY: Guilford Press. Kato, T. (2014). A reconsideration of sex differences in response to sexual and emotional infidelity. Archives of Sexual Behavior, 43, 1281–1288. Krosnick, J. A. (1999). Maximizing questionnaire quality. In J. P. Robinson, P. R. Shaver, L. S. Wrightsman, J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of political attitudes (pp. 37–57). San Diego, CA: Academic Press. Kuhle, B. X. (2011). Did you have sex with him? Do you love her? An in vivo test of sex differences in jealous interrogations. Personality and Individual Differences, 51(8), 1044–1047. http://dx.doi.org/10.1016/j.paid.2011.07.034. Lavner, J. A., Karney, B. R., & Bradbury, T. N. (2013). Newlyweds’ optimistic forecasts of their marriage: For better or for worse? Journal of Family Psychology, 27(4), 531–540. http://dx.doi.org/10.1037/a0033423. Levy, K. N., & Kelly, K. M. (2010). Sex differences in jealousy: A contribution from attachment theory. Psychological Science, 21, 168–173. Lishner, D. A., Nguyen, S., Stocks, E. L., & Zillmer, E. J. (2008). Are sexual and emotional infidelity equally upsetting to men and women? Making sense of forced-choice responses. Evolutionary Psychology, 6(4), 667–675. http://dx.doi.org/10.1177/147470490800600412. Maner, J. K., Miller, S. L., Rouby, D. A., & Gailliot, M. T. (2009). Intrasexual vigilance: The implicit cognition of romantic rivalry. Journal of Personality and Social Psychology, 97(1), 74–87. http://dx.doi.org/10.1037/a0014055. Mellon, L. W. (1981). The evolution of love. San Francisco: W.H. Freeman. Millar, M. (2013). Menstrual cycle changes in mate preferences for cues associated with genetic quality: The moderating role of mate value. Evolutionary Psychology, 11(1), 18–35. http://dx.doi.org/10.1177/147470491301100103. Pietrzak, R. H., Laird, J. D., Stevens, D. A., & Thompson, N. S. (2002). Sex differences in human jealousy: A coordinated study of forced-choice, continuous rating-scale, and physiological responses on the same subjects. Evolution and Human Behavior, 23(2), 83–94. http://dx.doi.org/10.1016/S1090-5138(01)00078-2. Sagarin, B. J. (2005). Reconsidering evolved sex difference in jealousy: Comment on Harris (2003). Personality and Social Psychology Review, 9, 62–75. Sagarin, B. J., Becker, D. V., Guadagno, R. E., Nicastle, L. D., & Millevoi, A. (2003). Sex differences (and similarities) in jealousy: The moderating influence of infidelity experience and sexual orientation of the infidelity. Evolution and Human Behavior, 24, 17–23. Sagarin, B. J., Becker, D. V., Guadagno, R. E., Wilkinson, W. W., & Nicastle, L. D. (2012). A reproductive threat-based model of evolved sex differences in jealousy. Evolutionary Psychology, 10, 487–503. Sagarin, B. J., Martin, A. L., Coutinho, S. A., Edlund, J. E., Patel, L., Skowronski, J. J., et al. (2012). Sex differences in jealousy: A meta-analytic examination. Evolution and Human Behavior, 33, 595–614. Scelza, B. A. (2014). Jealousy in a small-scale, natural fertility population: The roles of paternity, investment and love in jealous response. Evolution and Human Behavior, 35(2), 103–108. http://dx.doi.org/10.1016/j.evolhumbehav.2013.11.003. Scherer, C. R., Akers, E. G., & Kolbe, K. L. (2013). Bisexuals and the sex differences in jealousy hypothesis. Journal of Social and Personal Relationships, 30, 1064–1071. Schmitt, D. P. (2015). The evolution of culturally-variable sex differences: Men and women are not always different, but when they are... It appears not to result from patriarchy or sex role socialization. In T. K. Shackelford & R. D. Hansen (Eds.), The evolution of sexuality (pp. 221–256). Cham, Switzerland: Springer International Publishing. http://dx.doi. org/10.1007/978-3-319-09384-0_11.

302

J.E. Edlund and B.J. Sagarin

Sch€ utzwohl, A. (2005). Sex differences in jealousy: The processing of cues to infidelity. Evolution and Human Behavior, 26(3), 288–299. http://dx.doi.org/10.1016/ j.evolhumbehav.2004.09.003. Sch€ utzwohl, A. (2006). Sex differences in jealousy: Information search and cognitive preoccupation. Personality and Individual Differences, 40(2), 285–292. http://dx.doi.org/ 10.1016/j.paid.2005.06.024. utzwohl, A. (2008a). The crux of cognitive load: Constraining deliberate and effortful Sch€ decision processes in romantic jealousy. Evolution and Human Behavior, 29(2), 127–132. http://dx.doi.org/10.1016/j.evolhumbehav.2007.11.005. Sch€ utzwohl, A. (2008b). The disengagement of attentive resources from task-irrelevant cues to sexual and emotional infidelity. Personality and Individual Differences, 44(3), 633–644. http://dx.doi.org/10.1016/j.paid.2007.09.022. Shackelford, T. K., LeBlanc, G. J., & Drass, E. (2000). Emotional reactions to infidelity. Cognition and Emotion, 14, 643–659. Sheets, V. L., & Wolfe, M. D. (2001). Sexual jealousy in heterosexuals, lesbians, and gays. Sex Roles, 44, 255–276. Sisk, C. L., & Foster, D. L. (2004). The neural basis of puberty and adolescence. Nature Neuroscience, 7, 1040–1047. Smuts, B. B., & Gubernick, D. J. (1992). Male-infant relationships in nonhuman primates: Paternal investment or mating effort? In B. S. Hewlett (Ed.), Father-child relations: Cultural and bio-social contexts (pp. 1–30). Hawthorne, NY: Aldine de Gruyter. Symons, D. (1979). The evolution of human sexuality. New York: Oxford University Press. Tagler, M. J. (2010). Sex differences in jealousy: Comparing the influence of previous infidelity among college students and adults. Social Psychological and Personality Science, 1, 353–360. Takahashi, H., Matsuura, M., Yahata, N., Koeda, M., Suhara, T., & Okubu, Y. (2006). Men and women show distinct brain activations during imagery of sexual and emotional infidelity. NeuroImage, 32, 1299–1307. Takahashi, H., Tahata, N., Koeda, M., Matsuda, T., Asai, K., & Okubo, Y. (2004). Brain activation associated with evaluative processes of guilt and embarrassment: An fMRI study. NeuroImage, 23, 967–974. Thornhill, R., & Alcock, J. (1983). The evolution of insect mating systems. Cambridge, MA: Harvard University Press. Trivers, R. L. (1972). Parental investment and sexual selection. In B. Campbell (Ed.), Sexual selection and the decent of man (pp. 136–179). Chicago: Aldine-Atherton. van Schaik, C. P., & Paul, A. (1996). Male care in primates: Does it ever reflect paternity? Evolutionary Anthropology, 5, 152–156. Wegner, D. M., Schneider, D. J., Carter, S. R., & White, T. L. (1987). Paradoxical effects of thought suppression. Journal of Personality and Social Psychology, 53(1), 5–13. http://dx.doi. org/10.1037/0022-3514.53.1.5. Wilson, M., & Daly, M. (1992). The man who mistook his wife for a chattel. In J. H. Barkow, L. Cosmides, & J. Tooby (Eds.), The adapted mind: Evolutionary psychology and the generation of culture (pp. 289–322). New York, NY: Oxford University Press. Zengel, B., Edlund, J. E., & Sagarin, B. J. (2013). Sex differences in jealousy in response to infidelity: Evaluation of demographic moderators in a national random sample. Personality and Individual Differences, 54, 47–51.

INDEX Note: Page numbers followed by “f ” indicate figures, and “t” indicate tables.

A Amazon’s Mechanical Turk (MTurk) online software, 116–117 American Society of Human Genetics, 143–144 Ancestry, genetic, 161–166 Anger, 92–93, 114

B Behavioral genetics first law of, 151–152 founder, 171–172 fourth law of, 149–152 The Bell Curve, 164 Bipolar disorder, 95–96 Body perception, top-down effects on, 28–29

C

CAG. See Cytosine–adenine–guanine (CAG) Category-based knowledge activation, 33–41 ACC activity, 57 downstream consequences, 41–48 caring about outgroups, 44–45 emotion identification, 41–44 intergroup behaviors, 45–48 implicit identification, 33–35, 38–41 implicit prejudice, 37–41 implicit stereotypes, 35–41 reduction strategies, 48–58 implicit bias, 54–58 implicit identification, increasing, 49–50 implicit prejudice, decreasing, 51–54 implicit stereotypes, changing, 50–51 Cognitive processes, 7–10, 93, 145 Concealable social category, 14–16 Configural face encoding, 7–8, 10–20 Connectionist model, 6 Coping with trauma, 96–97

Criminality, 166–168 Cytosine–adenine–guanine (CAG), 158–159

D Diagnostic Analysis of Nonverbal Accuracy (DANVA), 210–211 Dialecticism, 119 Direct-to-consumer (DTC) genomics company, 138, 149, 152, 162–163 Dysphoria, 93–95

E Ebola-related anxiety, 109–110 Emotional infidelity chi-square tests, 264 men’s and women’s responses, 267–268 sex difference responses, 265–267 t-tests, 265 within-sex difference, 264–265 Emotion identification, 41–44 Emotion recognition, 210–212 Essences, 139, 171–173 feature of, 140–142 genes and, 143–144 metaphysical, 146–147 and natural kinds, 141 transformational leadership in, 197 Essentialism genetic (see Genetic essentialism) psychological, 139–152 Eugenics, 171–173 Event-related potentials (ERP), 9–10 Evolutionary psychology, 261–262, 266, 284–285, 294–295 Explicit biases, 4

F Face categorization, and memory, 29–32 Face composite effect, 21–22 Face encoding processing, novel group effects on, 22–27 303

304 Face perception processes in, 7–10 upright faces, 8f Facial width-to-height ratio (fWHR), 12 Fusiform cortex, 9f Fusiform face area (FFA), 9–10, 9f

G

GEF. See Genetic Essentialism Framework (GEF) Gender differences, 153–155 Gene-by-environment interaction, 176 Genetically modified organisms (GMOs), 147, 174–175 Genetic essentialism, 142–148, 150–152 biases, 145, 148–150, 160–161, 164 homogeneity/discreteness bias, 146–147 immutability/determinism bias, 145–146 long-term efforts, 177–178 naturalness bias, 147 perniciousness of, 175–178 short-term efforts, 175–177 Genetic Essentialism Framework (GEF), 145, 154 Genetic Essentialist Tendencies Scale (GETS), 147–148 Genetics, 138–139 attribution, 138, 145–147 affects sex and gender, 153–155 criminality behaviors, 166–168 essences and eugenics, 171–173 health behaviors, 158–161 on LGB, 156–158 on people’s perceptions, 153–175 political orientation, 168–170 race and ancestry, 161–166 role of, 159–160 for sexual orientation, 155–158 behavioral (see Behavioral genetics) engineering, 173–175 on Mendelian models, 144 strong genetic explanation, 149 weak genetic explanation, 149–150 Genome sequencing, 138 GETS. See Genetic Essentialist Tendencies Scale (GETS) GMOs. See Genetically modified organisms (GMOs)

Index

Goal-pursuit process, 194–195, 200 leaders’ intrapersonal dynamics, 200–219

H Health behaviors, genetics attribution, 158–161 Heritability, 149–152, 168

I Implicit biases, 4 Inferior occipital cortex, 9f Intellectual humility, 119 Intergroup effects, 2–4, 7–20 Interpersonal dynamics, SMLB, 219–237, 221f Intrapersonal dynamics, SMLB, 200–219, 207f Introspection, 101, 104

J Jealousy, sex difference in, 260, 294–298 automaticity issue, 278–281 Buller’s intriguing argument, 297 confounding, 268–272 cross-cultural variability, 297–298 debates, 291–294 double-shot hypothesis, 268–270 electodermal activity, 281–282 electromyographic activity, 281–283 emotional infidelity, 264–267 evolutionary psychology, 261–262, 266, 284–285, 294–295 exclusive jealous responses, 264 fMRI techniques, 282, 295–296 forced-choice measures, 272–275, 277, 279, 289–290 meta-analyses on, 283–286 moderators of, 288–290 physiological measures, 281–283 psychometric utility of question, 272–275 sexual infidelity, 264–267 sexual orientation and, 286–288 social psychology journals, 293–294 theoretical exploration, 297 theory of evolved, 261–268, 296 transcranial magnetic stimulation, 295–296

Index

L Leadership, 194 behavior (see Self-regulation model of leadership behavior (SMLB)) elements, 194 on favorability, 196–197 interpersonal dynamics, 219–237 intrapersonal dynamics, 200–219 selective history of, 195–200 social identity theory, 198–199 Learning styles, 150–151 Lexical decision task (LDT), 11

M Major depressive disorder (MDD), 93–95 Memory, face categorization and, 29–32 Moore’s law, 138 Mormon faces, 16 Multifactor Leadership Questionnaire (MLQ), 208–210, 212–213, 231 Multivoxel pattern analysis (MVPA), 36–37

N Natal sex, 153–154 National Assessment of Education Progress (NAEP), 143–144 Need for cognitive closure, 216–219, 234–237 Neural correlates, 98–99 Neural structures, 7–10 Non-Mormon faces, 16

O Occipital face area (OFA), 9f Orbital frontal cortex (OFC), 9f Own group bias (OGB) face categorization and memory, 30–32 fMRI study, 21

P Perceptual dehumanization, 10–13 Person construals, 5–32 bottom-up processes, 6–20 basic social categories, 13–14 concealable categories from perceptual cues, 14–16 configural face processing, 10–20

305 face perception, 7–10 intergroup relations, 10–20 unambiguous categories, 13–14 top-down processes, 20–32 body perception, 28–29 face categorization and memory, 29–32 face encoding processing, 22–27 reverse correlations, stimuli creation in, 24–25f visual processing, 20–22 Persons construed, category-based knowledge activation, 33–41 downstream consequences, 41–48 caring about outgroups, 44–45 emotion identification, 41–44 intergroup behaviors, 45–48 implicit identification, 33–35, 38–41 implicit prejudice, 37–41 implicit stereotypes, 35–41 strategies to reduce, 48–58 implicit bias, 54–58 implicit identification, increasing, 49–50 implicit prejudice, decreasing, 51–54 implicit stereotypes, changing, 50–51 Phenotypes, 149–150 Physical health, implications for, 97–98 Political affiliation, 16 Political orientation, 168–170 Posterior superior temporal sulcus (STS), 9f Power motive, 208–210, 240 Prefrontal cortex (PFC), 9f, 37, 56–58 Prejudice, 142, 147–148, 163–164, 177 control model, 56–58 ethnic, 163–164 implicit, 37–41, 51–54 Prevention focus, 203, 245 promotion vs., 222, 237–238 transactional leadership behaviors, 205–206, 215 Promotion focus, 203, 223, 245 emotion recognition, 211–212 vs. prevention focus, 222, 237–238 and striving for social power, 210 transformational leadership behaviors, 204–205, 214, 228–230 work-specific, 228–229

306 Propinquity, 161–162 Prosopagnosia, 8 Psychological essentialism, 139–152

R Race and genetics, 161–166 Regulatory fit, 222–224, 231, 233 Regulatory focus case of, 225–234 on leadership behavior, 206–213 SMLB, 206–213, 237–240 transactional/transformational leadership behaviors, 203–216, 209t, 214f Regulatory mode, 209t, 216–219, 234–237

S Self-distancing, 83–86 from adults to children, 92–93 aggression, 91 behavioral implications, 91–92 bipolar disorder, 95–96 conceptual framework, 85–86 coping with trauma, 96–97 definition, 83–84 dysphoria, 93–95 experimental results, 88–90 major depressive disorder, 93–95 neural correlates, 98–99 participants, 87–100 from past to future, 100 physical health, implications for, 97–98 from self-talk to mental time travel, 111–115 converging evidence, 114–115 experimental evidence, 111–113 individual differences, 113–114 spontaneous self-distancing, 90–91 training cognitive interventions, 120–122 converging evidence, 117–118 intergroup relationships, 122–123 laboratory training intervention, 115–116 online training intervention, 116–117 reasoning, 119–120 social support, 123–124 from visual imagery to self-talk, 100–111 challenge vs. threat construals, 104–105

Index

clinical implications, 109–110 converging evidence, 110–111 effortless form of self-control, 107–109 follow-up study, 110–111 implications for emotion regulation, 103–104 initial studies, 101–102 from lab to daily life, 105–107 Self-efficacy, 145 Self-immersed perspective, 85 Self-reflection, 82–83 psychologically immersed perspective, 83–84 self-distancing, 83–86 Self-regulation model of leadership behavior (SMLB), 194–195, 237f application, 237–240 contributions to, 242–244 dimensions, 196 future research, 240–241 as goal-pursuit, 200–219 and historic overview, 241 implications for, 242–246 interpersonal dynamics, 219–237, 221f intrapersonal dynamics, 200–219, 207f motivational antecedents, 200 need for cognitive closure, 216–219 regulatory focus and, 206–213, 237–240 regulatory mode theory, 216–219 as social influence, 219–237 task-/relationship-focused, 196–197 Self-regulation strategy, 201, 223, 237, 245–246 Sex and gender, 153–155 Sex difference in jealousy, 260, 294–298 automaticity issue, 278–281 Buller’s intriguing argument, 297 confounding, 268–272 cross-cultural variability, 297–298 debates, 291–294 double-shot hypothesis, 268–270 electodermal activity, 281–282 electromyographic activity, 281–283 emotional infidelity, 264–267 evolutionary psychology, 261–262, 266, 284–285, 294–295 exclusive jealous responses, 264 fMRI techniques, 282, 295–296

307

Index

forced-choice measures, 272–275, 277, 279, 289–290 meta-analyses on, 283–286 moderators of, 288–290 physiological measures, 281–283 psychometric utility of question, 272–275 sexual infidelity, 264–267 sexual orientation and, 286–288 social psychology journals, 293–294 theoretical exploration, 297 theory of evolved, 261–268, 296 transcranial magnetic stimulation, 295–296 Sexual infidelity chi-square tests, 264 men’s and women’s responses, 260–261, 267–268 sex difference responses, 265–267 t-tests, 265 within-sex difference, 264–265 Sexual orientation, 155–158 and sex difference in jealousy, 286–288 Social categorization from bodily cues, 17–18 bottom-up features, 5–6 causes and consequences, 3f concealable categories from perceptual cues, 14–16 connectionist model, 6 feed-forward process, 5–6 mutually constrained categories, 18–20 Social identity theory of leadership, 198–199 Social influence, 194–195, 203–204 component, 195

interpersonal dynamics, 219–237 leadership behavior on followers, 200 perspective, 199 SMLB, 243 Social psychology journals, 293–294 Stereotypic explanatory bias (SEB), 47

T Temporoparietal junction (TPJ), 9f Time-sharing Experiments for the Social Sciences (TESS), 274–275 Transactional leadership behaviors, 197–200, 209t case of, 225–234 leaders’ regulatory focus to, 203–216 prevention focus, 205–206 regulatory focus, 214f Transcranial magnetic stimulation (TMS), 295–296 Transformational leadership behaviors, 197–200, 209t case of, 225–234 components, 197 leaders’ regulatory focus to, 203–216 promotion focus, 204–205, 214, 228–230 regulatory focus, 214f

V Visual processing attention and, 25 group-based influences on, 20–22

W Work Regulatory Focus Scale, 228–229

CONTENTS OF OTHER VOLUMES Volume 1 Cultural Influences upon Cognitive Processes Harry C. Triandis The Interaction of Cognitive and Physiological Determinants of Emotional State Stanley Schachter Experimental Studies of Coalirion Formation William A. Gamson Communication Networks Marvin E. Shaw A Contingency Model of Leadership Effectiveness Fred E. Fiedler Inducing Resistance to Persuasion: Some Contemporary Approaches William J. McGuire Social Motivation, Dependency, and Susceptibility to Social Influence Richard H. Walters and Ross D. Purke Sociability and Social Organization in Monkeys and Apes William A. Mason Author Index—Subject Index

Volume 2 Vicarious Processes: A Case of No-Trial Learning Albert Bandura Selective Exposure Jonathan L. Freedman and David O. Sears Group Problem Solving L. Richard Hoffman Situational Factors in Conformity Vernon L. Allen Social Power John Schopler

From Acts to Dispositions: The Attribution Process in Person Perception Edward E. Jones and Keith E. Davis Inequality in Social Exchange J. Stacy Adams The Concept of Aggressive Drive: Some Additional Considerations Leonard Berkowitz Author Index—Subject Index

Volume 3 Mathematical Models in Social Psychology Robert P. Abelson The Experimental Analysis of Social Performance Michael Argyle and Adam Kendon A Structural Balance Approach to the Analysis of Communication Effects N. T. Feather Effects of Fear Arousal on Attitude Change: Recent Developments in Theory and Experimental Research Irving L. Janis Communication Processes and the Properties of Language Serge Moscovici The Congruity Principle Revisited: Studies in the Reduction, Induction, and Generalization of Persuasion Percy H. Tannenbaum Author Index—Subject Index

Volume 4 The Theory of Cognitive Dissonance: A Current Perspective Elliot Aronson Attitudes and Attraction Donn Byrne

309

310 Sociolinguistics Susan M. Ervin-Tripp Recognition of Emotion Nico H. Frijda Studies of Status Congruence Edward E. Sampson Exploratory Investigations of Empathy Ezra Stotland The Personal Reference Scale: An Approach to Social Judgment Harry S. Upshaw Author Index—Subject Index

Contents of Other Volumes

Libera-lized View of Secondary Reinforcement Albert J. Lott and Bernice E. Lott Social Influence, Conformity Bias, and the Study of Active Minorities Serge Moscovici and Claude Faucheux A Critical Analysis of Research Utilizing the Prisoner’s Dilemma Paradigm for the Study of Bargaining Charlan Nemeth Structural Representations of Implicit Personality Theory Seymour Rosenberg and Andrea Sedlak Author Index—Subject Index

Volume 5 Media Violence and Aggressive Behavior: A Review of Experimental Research Richard E. Goranson Studies in Leader Legitimacy, Influence, and Innovation Edwin P. Hollander and James W. Julian Experimental Studies of Negro-White Relationships Irwin Katz Findings and Theory in the Study of Fear Communications Howard Leventhal Perceived Freedom Ivan D. Steiner Experimental Studies of Families Nancy E. Waxler and Elliot G. Mishler Why Do Groups Make Riskier Decisions than Individuals? Kenneth L. Dion, Robert S. Baron, and Norman Miller Author Index—Subject Index

Volume 7 Cognitive Algebra: Integration Theory Applied to Social Attribution Norman A. Anderson On Conflicts and Bargaining Erika Apfelbaum Physical Attractiveness Ellen Bersheid and Elaine Walster Compliance, Justification, and Cognitive Change Harold B. Gerard, Edward S. Connolley, and Roland A. Wilhelmy Processes in Delay of Gratification Walter Mischel Helping a Distressed Person: Social, Personality, and Stimulus Determinants Ervin Staub Author Index—Subject Index

Volume 8 Volume 6 Self-Perception Theory Daryl J. Bem Social Norms, Feelings, and Other Factors Affecting Helping and Altruism Leonard Berkowitz The Power of Liking: Consequence of Inter-personal Attitudes Derived from a

Social Support for Nonconformity Vernon L. Allen Group Tasks, Group Interaction Process, and Group Performance Effectiveness: A Review and Proposed Integration J. Richard Hackman and Charles G. Morris The Human Subject in the Psychology Experiment: Fact and Artifact Arie W. Kruglanski

311

Contents of Other Volumes

Emotional Arousal in the Facilitation of Aggression Through Communication Percy H. Tannenbaum and Dolf Zillman The Reluctance to Transmit Bad News Abraham Tesser and Sidney Rosen Objective Self-Awareness Robert A. Wicklund Responses to Uncontrollable Outcomes: An Integration of Reactance Theory and the Learned Helplessness Model Camille B. Wortman and Jack W. Brehm Subject Index

Volume 9 New Directions in Equity Research Elaine Walster, Ellen Berscheid, and G. William Walster Equity Theory Revisited: Comments and Annotated Bibliography J. Stacy Adams and Sara Freedman The Distribution of Rewards and Resources in Groups and Organizations Gerald S. Leventhal Deserving and the Emergence of Forms of Justice Melvin J. Lerner, Dale T. Miller, and John G. Holmes Equity and the Law: The Effect of a Harmdoer’s “Suffering in the Act” on Liking and Assigned Punishment William Austin, Elaine Walster, and Mary Kristine Utne Incremental Exchange Theory: A Formal Model for Progression in Dyadic Social Interaction L. Lowell Huesmann and George Levinger Commentary George C. Homans Subject Index

Volume 10 The Catharsis of Aggression: An Evaluation of a Hypothesis Russell G. Geen and Michael B. Quanty

Mere Exposure Albert A. Harrison Moral Internalization: Current Theory and Research Martin L. Hoffman Some Effects of Violent and Nonviolent Movies on the Behavior of Juvenile Delinquents Ross D. Parke, Leonard Berkowitz, Jacques P. Leyens, Stephen G. West, and Richard Sebastian The Intuitive Psychologist and His Shortcomings: Distortions in the Attribution Process Less Ross Normative Influences on Altruism Shalom H. Schwartz A Discussion of the Domain and Methods of Social Psychology: Two Papers by Ron Harre and Barry R. Schlenker Leonard Berkowitz The Ethogenic Approach: Theory and Practice R. Harre On the Ethogenic Approach: Etiquette and Revolution Barry R. Schlenker Automatisms and Autonomies: In Reply to Professor Schlenker R. Harre Subject Index

Volume 11 The Persistence of Experimentally Induced Attitude Change Thomas D. Cook and Brian F. Flay The Contingency Model and the Dynamics of the Leadership Process Fred E. Fiedler An Attributional Theory of Choice Andy Kukla Group-Induced Polarization of Attitudes and Behavior Helmut Lamm and David G. Myers Crowding: Determinants and Effects Janet E. Stockdale

312 Salience: Attention, and Attribution: Top of the Head Phenomena Shelley E. Taylor and Susan T. Fiske Self-Generated Attitude Change Abraham Tesser Subject Index

Volume 12 Part I. Studies in Social Cognition Prototypes in Person Perception Nancy Cantor and Walter Mischel A Cognitive-Attributional Analysis of Stereotyping David L. Hamilton Self-Monitoring Processes Mark Snyder Part II. Social Influences and Social Interaction Architectural Mediation of Residential Density and Control: Crowding and the Regulation of Social Contact Andrew Baum and Stuart Valins A Cultural Ecology of Social Behavior J. W. Berry Experiments on Deviance with Special Reference to Dishonesty David P. Farrington From the Early Window to the Late Night Show: International Trends in the Study of Television’s Impact on Children and Adults John P. Murray and Susan Kippax Effects of Prosocial Television and Film Material on the Behavior of Viewers J. Phillipe Rushton Subject Index

Volume 13 People’s Analyses of the Causes of AbilityLinked Performances John M. Darley and George R. Goethals The Empirical Exploration of Intrinsic Motivational Processes Edward I. Deci and Richard M. Ryan

Contents of Other Volumes

Attribution of Responsibility: From Man the Scientist to Man as Lawyer Frank D. Fincham and Joseph M. Jaspars Toward a Comprehensive Theory of Emotion Howard Leventhal Toward a Theory of Conversion Behavior Serge Moscovici The Role of Information Retrieval and Conditional Inference Processes in Belief Formation and Change Robert S. Wyer, Jr. and Jon Hartwick Index

Volume 14 Verbal and Nonverbal Communication of Deception Miron Zuckerman, Bella M. DePaulo, and Robert Rosenthal Cognitive, Social, and Personality Processes in the Physiological Detection of Deception William M. Waid and Martin T. Orne Dialectic Conceptions in Social Psychology: An Application to Social Penetration and Privacy Regulation Irwin Altman, Anne Vinsel, and Barbara B. Brown Direct Experience and Attitude–Behavior Consistency Russell H. Fazio and Mark P. Zanna Predictability and Human Stress: Toward a Clarification of Evidence and Theory Suzanne M. Miller Perceptual and Judgmental Processes in Social Contexts Arnold Upmeyer Jury Trials: Psychology and Law Charlan Jeanne Nemeth Index

Volume 15 Balance, Agreement, and Positivity in the Cognition of Small Social Structures Walter H. Crockett

313

Contents of Other Volumes

Episode Cognition: Internal Representations of Interaction Routines Joseph P. Forgas The Effects of Aggressive-Pornographic Mass Media Stimuli Neil M. Malamuth and Ed Donnerstein Socialization in Small Groups: Temporal Changes in Individual–Group Relations Richard L. Moreland and John M. Levine Translating Actions into Attitudes: An Identity-Analytic Approach to the Explanation of Social Conduct Barry R. Schlenker Aversive Conditions as Stimuli to Aggression Leonard Berkowitz Index

Volume 16 A Contextualist Theory of Knowledge: Its Implications for Innovation and Reform in Psychological Research William J. McGuire Social Cognition: Some Historical and Theoretical Perspectives Janet Landman and Melvin Manis Paradigmatic Behaviorism: Unified Theory for Social-Personality Psychology Arthur W. Staats Social Psychology from the Standpoint of a Structural Symbolic Interactionism: Toward an Interdisciplinary Social Psychology Sheldon Stryker Toward an Interdisciplinary Social Psychology Carl W. Backman Index

Volume 17 Mental Representations of Self John F. Kihlstrom and Nancy Cantor Theory of the Self: Impasse and Evolution Kenneth J. Gergen

A Perceptual-Motor Theory of Emotion Howard Leventhal Equity and Social Change in Human Relationships Charles G. McClintock, Roderick M. Kramer, and Linda J. Keil A New Look at Dissonance Theory Joel Cooper and Russell H. Fazio Cognitive Theories of Persuasion Alice H. Eagly and Shelly Chaiken Helping Behavior and Altruism: An Empirical and Conceptual Overview John F. Dovidio Index

Volume 18 A Typological Approach to Marital Interaction: Recent Theory and Research Mary Anne Fitzpatrick Groups in Exotic Environments Albert A. Harrison and Mary M. Connors Balance Theory, the Jordan Paradigm, and the Wiest Tetrahedon Chester A. Insko The Social Relations Model David A. Kenny and Lawrence La Voie Coalition Bargaining S. S. Komorita When Belief Creates Reality Mark Snyder Index

Volume 19 Distraction–Conflict Theory: Progress and Problems Robert S. Baron Recent Research on Selective Exposure to Information Dieter Frey The Role of Threat to Self-Esteem and Perceived Control in Recipient Reaction to Help: Theory Development and Empirical Validation Arie Nadler and Jeffrey D. Fisher

314 The Elaboration Likelihood Model of Persuasion Richard E. Petty and John T. Cacioppo Natural Experiments on the Effects of Mass Media Violence on Fatal Aggression: Strengths and Weaknesses of a New Approach David P. Phillips Paradigms and Groups Ivan D. Steiner Social Categorization: Implications for Creation and Reduction of Intergroup Bias David A. Wilder Index

Volume 20 Attitudes, Traits, and Actions: Dispositional Prediction of Behavior in Personality and Social Psychology Icek Ajzen Prosocial Motivation: Is It Ever Truly Altruistic? C. Daniel Batson Dimensions of Group Process: Amount and Structure of Vocal Interaction James M. Dabbs, Jr. and R. Barry Ruback The Dynamics of Opinion Formation Harold B. Gerard and Ruben Orive Positive Affect, Cognitive Processes, and Social Behavior Alice M. Isen Between Hope and Fear: The Psychology of Risk Lola L. Lopes Toward an Integration of Cognitive and Motivational Perspectives on Social Inference: A Biased Hypothesis-Testing Model Tom Pyszczynski and Jeff Greenberg Index

Volume 21 Introduction Leonard Berkowitz Part I. The Self as Known Narrative and the Self as Relationship Kenneth J. Gergen and Mary M. Gergen

Contents of Other Volumes

Self and Others: Studies in Social Personality and Autobiography Seymour Rosenberg Content and Process in the Experience of Self William J. McGuire and Claire V. McGuire Information Processing and the Study of the Self John F. Kihlstrom, Nancy Cantor, Jeanne Sumi Albright, Beverly R. Chew, Stanley B. Klein, and Paula M. Niedenthal Part II. Self-Motives Toward a Self-Evaluation Maintenance Model of Social Behavior Abraham Tesser The Self: A Dialectical Approach Carl W. Backman The Psychology of Self-Affirmation: Sustaining the Integrity of the Self Claude M. Steele A Model of Behavioral Self-Regulation: Translating Intention into Action Michael F. Scheier and Charles S. Carver Index

Volume 22 On the Construction of the Anger Experience: Aversive Events and Negative Priming in the Formation of Feelings Leonard Berkowitz and Karen Heimer Social Psychophysiology: A New Look John T. Cacioppo, Richard E. Petty, and Louis G. Tassinary Self-Discrepancy Theory: What Patterns of Self-Beliefs Cause People to Suffer? E. Tory Higgins Minding Matters: The Consequences of Mindlessness-Mindfulness Ellen J. Langer The Tradeoffs of Social Control and Innovation in Groups and Organizations Charlan Jeanne Nemeth and Barry M. Staw Confession, Inhibition, and Disease James W. Pennebaker

315

Contents of Other Volumes

A Sociocognitive Model of Attitude Structure and Function Anthony R. Pratkanis and Anthony G. Greenwald Introspection, Attitude Change, and Attitude–Behavior Consistency: The Disruptive Effects of Explaining Why We Feel the Way We Do Timothy D. Wilson, Dana S. Dunn, Dolores Kraft, and Douglas J. Lisle Index

Volume 23 A Continuum of Impression Formation, from Category-Based to Individuating Processes: Influences of Information and Motivation on Attention and Interpretation Susan T. Fiske and Steven L. Neuberg Multiple Processes by Which Attitudes Guide Behavior: The MODE Model as an Integrative Framework Russell H. Fazio PEAT: An Integrative Model of Attribution Processes John W. Medcof Reading People’s Minds: A Transformation Rule Model for Predicting Others’ Thoughts and Feelings Rachel Karniol Self-Attention and Behavior: A Review and Theoretical Update Frederick X. Gibbons Counterfactual Thinking and Social Perception: Thinking about What Might Have Been Dale T. Miller, William Turnbull, and Cathy McFarland Index

Volume 24 The Role of Self-Interest in Social and Political Attitudes David O. Sears and Carolyn L. Funk

A Terror Management Theory of Social Behavior: The Psychological Functions of Self-Esteem and Cultural Worldviews Sheldon Solomon, Jeff Greenberg, and Tom Pyszczynski Mood and Persuasion: Affective States Influence the Processing of Persuasive Communications Norbert Schwarz, Herbert Bless, and Gerd Bohner A Focus Theory of Normative Conduct: A Theoretical Refinement and Reevaluation of the Role of Norms in Human Behavior Robert B. Cialdini, Carl A. Kallgren, and Raymond R. Reno The Effects of Interaction Goals on Person Perception James L. Hilton and John M. Darley Studying Social Interaction with the Rochester Interaction Record Harry T. Reis and Ladd Wheeler Subjective Construal, Social Inference, and Human Misunderstanding Dale W. Griffin and Lee Ross Index

Volume 25 Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries Shalom H. Schwartz Motivational Foundations of Behavioral Confirmation Mark Snyder A Relational Model of Authority in Groups Tom R. Tyler and E. Allan Lind You Can’t Always Think What You Want: Problems in the Suppression of Unwanted Thoughts Daniel M. Wegner Affect in Social Judgments and Decisions: A Multiprocess Model Joseph Paul Forgas The Social Psychology of Stanley Milgram Thomas Blass

316 The Impact of Accountability on Judgment and Choice: Toward a Social Contingency Model Philip E. Tetlock Index

Volume 26 Attitudes Toward High Achievers and Reactions to Their Fall: Theory and Research Concerning Tall Poppies N. T. Feather Evolutionary Social Psychology: From Sexual Selection to Social Cognition Douglas T. Kenrick Judgment in a Social Context: Biases, Short-comings, and the Logic of Conversation Norbert Schwarz A Phase Model of Transitions: Cognitive and Motivational Consequences Diane N. Ruble Multiple-Audience Problems, Tactical Communication, and Social Interaction: A Relational-Regulation Perspective John H. Fleming From Social Inequality to Personal Entitlement: The Role of Social Comparisons, Legitimacy Appraisals, and Group Membership Brenda Major Mental Representations of Social Groups: Advances in Understanding Stereotypes and Stereotyping Charles Stangor and James E. Lange Index

Volume 27 Inferences of Responsibility and Social Motivation Bernard Weiner Information Processing in Social Contexts: Implications for Social Memory and Judgment Robert S. Wyer, Jr. and Deborah H. Gruenfeld

Contents of Other Volumes

The Interactive Roles of Stability and Level of Self-Esteem: Research and Theory Michael H. Kernis and Stephanie B. Waschull Gender Differences in Perceiving Internal State: Toward a His-and-Hers Model of Perceptual Cue Use Tomi-Ann Roberts and James W. Pennebaker On the Role of Encoding Processes in Stereotype Maintenance William von Hippel, Denise Sekaquaptewa, and Patrick Vargas Psychological Barriers to Dispute Resolution Lee Ross and Andrew Ward Index

Volume 28 The Biopsychosocial Model of Arousal Regulation Jim Blascovich and Joe Tomaka Outcome Biases in Social Perception: Implications for Dispositional Inference, Attitude Change, Stereotyping, and Social Behavior Scott T. Allison, Diane M. Mackie, and David M. Messick Principles of Judging Valence: What Makes Events Positive or Negative? C. Miguel Brendl and E. Tory Higgins Pluralistic Ignorance and the Perpetuation of Social Norms by Unwitting Actors Deborah A. Prentice and Dale T. Miller People as Flexible Interpreters: Evidence and Issues from Spontaneous Trait Inference James S. Uleman, Leonard S. Newman, and Gordon B. Moskowitz Social Perception, Social Stereotypes, and Teacher Expectations: Accuracy and the Quest for the Powerful Self-Fulfilling Prophecy Lee Jussim Jacquelynne Eccles, and Stephanie Madon Nonverbal Behavior and Nonverbal Communication: What do Conversational Hand Gestures Tell Us? Robert M. Krauss, Yihsiu Chen, and Purnima Chawla Index

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Volume 29

Volume 31

Counterfactual Thinking: The Intersection of Affect and Function Neal J. Roese and James M. Olson Terror Management Theory of Self-Esteem and Cultural Worldviews: Empirical Assessments and Conceptual Refinements Jeff Greenberg, Sheldon Solomon, and Tom Pyszczynski The Flexible Correction Model: The Role of Naı¨ve Theories of Bias in Bias Correction Duane T. Wegener and Richard E. Petty Self-Evaluation: To Thine Own Self Be Good, to Thine Own Self Be Sure, to Thine Own Self Be True, and to Thine Own Self Be Better Constantine Sedikides and Michael J. Strube Toward a Hierarchical Model of Intrinsic and Extrinsic Motivation Robert J. Vallerand Index

Affect and Information Processing Robert S. Wyer, Jr., Gerald L. Clore, and Linda M. Isbell Linguistic Intergroup Bias: Stereotype Perpetuation through Language Anne Maass Relationships from the Past in the Present: Significant-Other Representations and Transference in Interpersonal Life Serena Chen and Susan M. Anderson The Puzzle of Continuing Group Inequality: Piecing Together Psychological, Social, and Cultural Forces in Social Dominance Theory Felicia Pratto Attitude Representation Theory Charles G. Lord and Mark R. Lepper Discontinuity Theory: Cognitive and Social Searches for Rationality and Normality— May Lead to Madness Philip G. Zimbardo Index

Volume 30 Promotion and Prevention: Regulatory Focus as a Motivational Principle E. Tory Higgins The Other “Authoritarian Personality” Bob Altemeyer Person Preception Comes of Age: The Salience and Significance of Age in Social Adjustments Joann M. Montepare and Leslie A. Zebrowitz On the Perception of Social Consensus Joachim Krueger Prejudice and Stereotyping in Everyday Communication Janet B. Ruscher Situated Optimism: Specific Outcome Expectancies and Self-Regulation David A. Armor and Shelley E. Taylor Index

Volume 32 The Nature and Function of Self-Esteem: Sociometer Theory Mark R. Leary and Roy F. Baumeister Temperature and Aggression Craig A. Anderson, Kathryn B. Anderson, Nancy Dorr, Kristina M. DeNeve, and Mindy Flanagan The Importance of Being Selective: Weighing the Role of Attribute Importance in Attitudinal Judgment Joop van der Pligt, Nanne K. de Vries, Antony S. R. Manstead, and Frank van Harreveld Toward a History of Social Behavior: Judgmental Accuracy from Thin Slices of the Behavioral Stream Nalini Amabady, Frank J. Bernieri, and Jennifer A. Richeson Attractiveness, Attraction, and Sexual Selection: Evolutionary Perspectives on

318 the Form and Function of Physical Attractiveness Dianne S. Berry Index

Volume 33 The Perception–Behavior Expressway: Automatic Effects of Social Perception on Social Behavior Ap Dijksterhuis and John A. Bargh A Dual-Process Cognitive-Motivational Theory of Ideology and Prejudice John Duckitt Ambivalent Sexism Peter Glick and Susan T. Fiske Videotaped Confessions: Is Guilt in the Eye of the Camera? G. Daniel Lassiter, Andrew L. Geers, Patrick J. Munhall, Ian M. Handley, and Melissa J. Beers Effort Determination of Cardiovascular Response: An Integrative Analysis with Applications in Social Psychology Rex A. Wright and Leslie D. Kirby Index

Volume 34 Uncertainty Management by Means of Fairness Judgments Kees van den Bos and E. Allan Lind Cognition in Persuasion: An Analysis of Information Processing in Response to Persuasive Communications Dolores Albarracin Narrative-Based Representations of Social Knowledge: Their Construction and Use in Comprehension, Memory, and Judgment Robert S. Wyer, Jr., Rashmi Adaval and Stanley J. Colcombe Reflexion and Reflection: A Social Cognitive Neuroscience Approach to Attributional Inference Matthew D. Lieberman, Ruth Gaunt, Daniel T. Gilbert, and Yaacov Trope

Contents of Other Volumes

Antecedents and Consequences of Attributions to Discrimination: Theoretical and Empirical Advances Brenda Major, Wendy J. Quinton, and Shannon K. McCoy A Theory of Goal Systems Arie W. Kruglanski, James Y. Shah, Ayelet Fishbach, Ron Friedman, Woo Young Chun, and David Sleeth-Keppler Contending with Group Image: The Psychology of Stereotype and Social Identity Threat Claude M. Steele, Steven J. Spencer, and Joshua Aronson Index

Volume 35 Social Identity and Leadership Processes in Groups Michael A. Hogg and Daan van Knippenberg The Attachment Behavioral System in Adulthood: Activation, Psychodynamics, and Interpersonal Processes Mario Mikulincer and Phillip R. Shaver Stereotypes and Behavioral Confirmation: From Interpersonal to Intergroup Perspectives Olivier Klein and Mark Snyder Motivational Bases of Information Processing and Strategy in Conflict and Negotiation Carsten K. W. De Dreu and Peter J. Carnevale Regulatory Mode: Locomotion and Assessment as Distinct Orientations E. Tory Higgins, Arie W. Kruglanski, and Antonio Pierro Affective Forecasting Timothy D. Wilson and Daniel T. Gilbert Index

Volume 36 Aversive Racism John F. Dovidio and Samuel L. Gaertner

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Socially Situated Cognition: Cognition in its Social Context Eliot R. Smith and G€ un R. Semin Social Axioms: A Model for Social Beliefs in Multicultural Perspective Kwok Leung and Michael Harris Bond Violent Video Games: Specific Effects of Violent Content on Aggressive Thoughts and Behavior Craig A. Anderson, Nicholas L. Carnagey, Mindy Flanagan, Arlin J. Benjamin, Jr., Janie Eubanks, and Jeffery C. Valentine Survival and Change in Judgments: A Model of Activation and Comparison Dolores Albarracı´n, Harry M. Wallace, and Laura R. Glasman The Implicit Volition Model: On the Preconscious Regulation of Temporarily Adopted Goals Gordon B. Moskowitz, Peizhong Li, and Elizabeth R. Kirk Index

Volume 37 Accuracy in Social Perception: Criticisms, Controversies, Criteria, Components, and Cognitive Processes Lee Jussim Over Thirty Years Later: A Contemporary Look at Symbolic Racism David O. Sears and P. J. Henry Managing Group Behavior: The Interplay Between Procedural Justice, Sense of Self, and Cooperation David De Cremer and Tom R. Tyler So Right it’s Wrong: Groupthink and the Ubiquitous Nature of Polarized Group Decision Making Robert S. Baron An Integrative Theory of Intergroup Contact Rupert Brown and Miles Hewstone Says Who?: Epistemic Authority Effects in Social Judgment Arie W. Kruglanski, Amiram Raviv, Daniel Bar-Tal, Alona Raviv, Keren Sharvit,

Shmuel Ellis, Ruth Bar, Antonio Pierro, and Lucia Mannetti Index

Volume 38 Exploring the Latent Structure of StrengthRelated Attitude Attributes Penny S. Visser, George Y. Bizer, and Jon A. Krosnick Implementation Intentions and Goal Achievement: A Meta-Analysis of Effects and Processes Peter M. Gollwitzer and Paschal Sheeran Interracial Interactions: A Relational Approach J. Nicole Shelton and Jennifer A. Richeson The Psychology of Self-Defense: Self-Affirmation Theory David K. Sherman and Geoffrey L. Cohen Intergroup Beliefs: Investigations from the Social Side Charles Stangor and Scott P. Leary A Multicomponent Conceptualization of Authenticity: Theory and Research Michael H. Kernis and Brian M. Goldman Index

Volume 39 Culture and the Structure of Personal Experience: Insider and Outsider Phenomenologies of the Self and Social World Dov Cohen, Etsuko Hoshino-Browne, and Angela K.-y. Leung Uncertainty–Identity Theory Michael A. Hogg Metacognitive Experiences and the Intricacies of Setting People Straight: Implications for Debiasing and Public Information Campaigns Norbert Schwarz, Lawrence J. Sanna, Ian Skurnik, and Carolyn Yoon Multiple Social Categorization Richard J. Crisp and Miles Hewstone

320 On the Parameters of Human Judgment Arie W. Kruglanski, Antonio Pierro, Lucia Mannetti, Hans-Peter Erb, and Woo Young Chun Panglossian Ideology in the Service of System Justification: How Complementary Stereotypes Help Us to Rationalize Inequality Aaron C. Kay, John T. Jost, Anesu N. Mandisodza, Steven J. Sherman, John V. Petrocelli, and Amy L. Johnson Feeling the Anguish of Others: A Theory of Vicarious Dissonance Joel Cooper and Michael A. Hogg Index

Volume 40 The Commitment-Insurance System: Self-Esteem and the Regulation of Connection in Close Relationships Sandra L. Murray and John G. Holmes Warmth and Competence as Universal Dimensions of Social Perception: The Stereotype Content Model and the BIAS Map Amy J. C. Cuddy, Susan T. Fiske, and Peter Glick A Reciprocal Influence Model of Social Power: Emerging Principles and Lines of Inquiry Dacher Keltner, Gerben A. Van Kleef, Serena Chen, and Michael W. Kraus Psychological Aspects of Retributive Justice Kevin M. Carlsmith and John M. Darley Majority Versus Minority Influence, Message Processing and Attitude Change: The Source-Context-Elaboration Model Robin Martin and Miles Hewstone Index

Volume 41 The Introspection Illusion Emily Pronin Persuasion: Insights from the Self-Validation Hypothesis Pablo Brin˜ol and Richard E. Petty

Contents of Other Volumes

Action-Based Model of Dissonance: A Review, Integration, and Expansion of Conceptions of Cognitive Conflict Eddie Harmon-Jones, David M. Amodio, and Cindy Harmon-Jones Affect as a Psychological Primitive Lisa Feldman Barrett and Eliza Bliss-Moreau Human Mimicry Tanya L. Chartrand and Rick van Baaren Ostracism: A Temporal Need-Threat Model Kipling D. Williams Index

Volume 42 Mental Representations of Social Values Gregory R. Maio An Interpersonal Approach to Emotion in Social Decision Making: The Emotions as Social Information Model Gerben A. Van Kleef, Carsten K. W. De Dreu, and Antony S. R. Manstead On Passion for Life Activities: The Dualistic Model of Passion Robert J. Vallerand Good News! Capitalizing on Positive Events in an Interpersonal Context Shelly L. Gable and Harry T. Reis Indirect Prime-to-Behavior Effects: The Role of Perceptions of the Self, Others, and Situations in Connecting Primed Constructs to Social Behavior Dirk Smeesters, S. Christian Wheeler, and Aaron C. Kay Mental Construal and the Emergence of Assimilation and Contrast Effects: The Inclusion/Exclusion Model Herbert Bless and Norbert Schwarz Index

Volume 43 The Planning Fallacy: Cognitive, Motivational, and Social Origins Roger Buehler, Dale Griffin, and Johanna Peetz Optimal Distinctiveness Theory: A Framework for Social Identity, Social Cognition, and Intergroup Relations Geoffrey J. Leonardelli, Cynthia L. Pickett, and Marilynn B. Brewer

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Psychological License: When it is Needed and How it Functions Dale T. Miller and Daniel A. Effron Beyond Productivity Loss in Brainstorming Groups: The Evolution of a Question Wolfgang Stroebe, Bernard A. Nijstad, and Eric F. Rietzschel Evaluative Conditioning: The “How” Question Christopher R. Jones, Michael A. Olson, and Russell H. Fazio Flexibility and Consistency in Evaluative Responding: The Function of Construal Level Alison Ledgerwood, Yaacov Trope, and Nira Liberman Index

Volume 44 Psychosocial Resources: Functions, Origins, and Links to Mental and Physical Health Shelley E. Taylor and Joelle I. Broffman The Associative–Propositional Evaluation Model: Theory, Evidence, and Open Questions Bertram Gawronski and Galen V. Bodenhausen The Dynamics of Acculturation: An Intergroup Perspective Rupert Brown and Hanna Zagefka Visual Perspective in Mental Imagery: A Representational Tool that Functions in Judgment, Emotion, and Self-Insight Lisa K. Libby and Richard P. Eibach The Dunning–Kruger Effect: On Being Ignorant of One’s Own Ignorance David Dunning Time to Give Up the Dogmas of Attribution: An Alternative Theory of Behavior Explanation Bertram F. Malle Index

Volume 45 Stereotypes and Shifting Standards: Forming, Communicating, and Translating Person Impressions Monica Biernat Color-in-Context Theory Andrew J. Elliot and Markus A. Maier Implicit Theories Shape Intergroup Relations Priyanka B. Carr, Aneeta Rattan, and Carol S. Dweck Reactions to Vanguards: Advances in Backlash Theory Laurie A. Rudman, Corinne A. Moss-Racusin, Peter Glick, and Julie E. Phelan Consequences of Self-image and Compassionate Goals Jennifer Crocker and Amy Canevello Adult Attachment Orientations, Stress, and Romantic Relationships Jeffry A. Simpson and W. Steven Rholes Index

Volume 46 Danger, Disease, and the Nature of Prejudice(s) Mark Schaller and Steven L. Neuberg Biosocial Construction of Sex Differences and Similarities in Behavior Wendy Wood and Alice H. Eagly The Imagined Contact Hypothesis Richard J. Crisp and Rhiannon N. Turner Making Heroes: The Construction of Courage, Competence, and Virtue George R. Goethals and Scott T. Allison The Effects of Past Behavior on Future Goal-Directed Activity Robert S. Wyer Jr., Alison Jing Xu, and Hao Shen Effectiveness in Humans and Other Animals: A Common Basis for Well-being and Welfare Becca Franks and E. Tory Higgins Index

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Volume 47

Volume 49

Positive Emotions Broaden and Build Barbara L. Fredrickson Moral Foundations Theory: The Pragmatic Validity of Moral Pluralism Jesse Graham, Jonathan Haidt, Sena Koleva, Matt Motyl, Ravi Iyer, Sean P. Wojcik, and Peter H. Ditto Culture and Analytic Versus Holistic Cognition: Toward Multilevel Analyses of Cultural Influences Yuri Miyamoto Message Position, Information Processing, and Persuasion: The Discrepancy Motives Model Jason K. Clark and Duane T. Wegener Implicit Attitudes and Beliefs Adapt to Situations: A Decade of Research on the Malleability of Implicit Prejudice, Stereotypes, and the Self-Concept Nilanjana Dasgupta Index

The I3 Model: Metatheory, Theory, and Evidence Eli J. Finkel Immanent Justice Reasoning: Theory, Research, and Current Directions Mitchell J. Callan, Robbie M. Sutton, Annelie J. Harvey, and Rael J. Dawtry The Fading Affect Bias: Its History, Its Implications, and Its Future John J. Skowronski, W. Richard Walker, Dawn X. Henderson, and Gary D. Bond Threat and Defense: From Anxiety to Approach Eva Jonas, Ian McGregor, Johannes Klackl, Dmitrij Agroskin, Immo Fritsche, Colin Holbrook, Kyle Nash, Travis Proulx, and Markus Quirin Mood and Processing Effort: The Mood-Congruent Expectancies Approach Rene Ziegler Index

Volume 48 On Sense-Making Reactions and Public Inhibition of Benign Social Motives: An Appraisal Model of Prosocial Behavior Kees Van den Bos and E. Allan Lind The Case For and Against PerspectiveTaking Jacquie Vorauer Changing Places: A Dual Judgment Model of Empathy Gaps in Emotional Perspective Taking Leaf Van Boven, George Loewenstein, David Dunning, and Loran F. Nordgren Social Self-Analysis: Constructing, Protecting, and Enhancing the Self Mark D. Alicke, Ethan Zell, and Corey L. Guenther A Three-Tier Hierarchy of Self-Potency: Individual Self, Relational Self, Collective Self Constantine Sedikides, Lowell Gaertner, Michelle A. Luke, Erin M. O’Mara, and Jochen E. Gebauer Index

Volume 50 Recent Research on Free Will: Conceptualizations, Beliefs, and Processes Roy F. Baumeister and Andrew E. Monroe The Intuitive Traditionalist: How Biases for Existence and Longevity Promote the Status Quo Scott Eidelman and Christian S. Crandall Social Psychology and the Fight Against AIDS: An Information–Motivation– Behavioral Skills Model for the Prediction and Promotion of Health Behavior Change William A. Fisher, Jeffrey D. Fisher, and Paul A. Shuper Communal and Agentic Content in Social Cognition: A Dual Perspective Model Andrea E. Abele and Bogdan Wojciszke Motivation Resulting from Completed and Missing Actions Ayelet Fishbach, Minjung Koo, and Stacey R. Finkelstein Index

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Volume 51

Volume 53

Why Do Humans Form Long-Term Mateships? An Evolutionary Game-Theoretic Model Daniel Conroy-Beam, Cari D. Goetz, and David M. Buss The Why and How of Defending Belief in a Just World Carolyn L. Hafer and Alicia N. Rubel Positive Versus Negative Valence: Asymmetries in Attitude Formation and Generalization as Fundamental Individual Differences Russell H. Fazio, Evava S. Pietri, Matthew D. Rocklage, and Natalie J. Shook We’ll Always Have Paris: The Hedonic Payoff from Experiential and Material Investments Thomas Gilovich and Amit Kumar To Nostalgize: Mixing Memory with Affect and Desire Constantine Sedikides, Tim Wildschut, Clay Routledge, Jamie Arndt, Erica G. Hepper, and Xinyue Zhou Index

Pair-Bonded Relationships and Romantic Alternatives: Toward an Integration of Evolutionary and Relationship Science Perspectives Kristina M. Durante, Paul W. Eastwick, Eli J. Finkel, Steven W. Gangestad, and Jeffry A. Simpson The Behavioral Immune System: Implications for Social Cognition, Social Interaction, and Social Influence Damian R. Murray and Mark Schaller Self-Protective yet Self-Defeating: The Paradox of Low Self-Esteem People’s Self-Disclosures Joanne V. Wood and Amanda L. Forest Social Surrogates, Social Motivations, and Everyday Activities: The Case for a Strong, Subtle, and Sneaky Social Self Shira Gabriel, Jennifer Valenti, and Ariana F. Young Spatial Agency Bias: Representing People in Space Caterina Suitner and Anne Maass Index

Volume 52 Thirty Years of Terror Management Theory: From Genesis to Revelation Tom Pyszczynski, Sheldon Solomon, and Jeff Greenberg A Biosocial Model of Affective Decision Making: Implications for Dissonance, Motivation, and Culture Shinobu Kitayama and Steven Tompson Detecting and Experiencing Prejudice: New Answers to Old Questions Manuela Barreto and Naomi Ellemers The Motivated Gatekeeper of Our Minds: New Directions in Need for Closure Theory and Research Arne Roets, Arie W. Kruglanski, Malgorzata Kossowska, AntonioPierro,and Ying-yi Hong The ABC of Ambivalence: Affective, Behavioral, and Cognitive Consequences of Attitudinal Conflict Frenk van Harreveld, Hannah U. Nohlen, and Iris K. Schneider Index

Volume 54 Strategic Thinking N. Halevy Strength Model of Self-Regulation as Limited Resource: Assessment, Controversies, Update R.F. Baumeister and K.D. Vohs Dominance and Prestige: Dual Strategies for Navigating Social Hierarchies J.K. Maner and C.R. Case Understanding Resilience: From Negative Life Events to Everyday Stressors M.D. Seery and W.J. Quinton Highlighting the Contextual Nature of Interpersonal Relationships J.K. McNulty Index