Movements of the Mind: A Theory of Attention, Intention and Action 0192866893, 9780192866899

Table of contents :
Cover
Movements of the Mind: A Theory of Attention, Intention and Action
Copyright
Contents
Introduction
0.1 A Biologist’s Perspective
0.2 Central Themes
0.3 The Book’s Parts
0.4 Chapter Summaries
0.5 Acknowledgments
0.6 Family
Claims by Section
PART I: THE STRUCTURE OF ACTION AND ATTENTION
1: The Structure of Acting
1.1 Introduction
1.2 The Selection Problem and the Structure of Acting
1.3 Intentions and Intentional Action
1.4 Control and Automaticity
1.5 The Necessity of Bias for Action
1.6 The Biology of Intention-Based Biasing
1.7 Intention-Based Biasing as Cognitive Integration
1.8 Learning to Act and Shifting Control
1.9 The Agent Must Be in Control in Action
1.10 The Agent’s Being Active
1.11 Taking Stock
Appendix 1.1
Notes
2: Attention and Attending
2.1 Introduction
2.2 Merging the Psychology and Philosophy of Attention
2.3 Attention and the Selection Problem
2.4 Attention as Guide versus Attention as Mechanism
2.5 Perceptual Attending as Mental Action
2.6 Goal-Directed Automatic Attention and Bias
2.7 Attentional Capture and Passive Agency
2.8 How Much Attention Is There in the World of Action?
2.9 Action Is Necessary for Attention
2.10 On Different Lessons from Causal Deviance
2.11 Agentive Control and Guidance Revisited
2.12 Taking Stock
Notes
PART II: INTENTION AS PRACTICAL MEMORY AND REMEMBERING
3: Intention as Practical Memory
3.1 Introduction
3.2 Memory in Action
3.3 Empirical Theories of Working Memory
3.4 Memory at Work
3.5 Vigilance
3.6 Steadfastness and Sustained Attention
3.7 Taking Stock
Notes
4: Intending as Practical Remembering
4.1 Introduction
4.2 The Continuity of Practical Memory
4.3 Practical Fine-Tuning
4.4 Fine-Tuning as Practical Memory at Work
4.5 The Dynamics of Thinking about Action, in Action
4.6 First-Personal Access to Intentional Action
4.7 On Keeping Time with Action
4.8 Taking Stock
Notes
PART III: MOVEMENTS OF THE MIND AS DEPLOYMENTS OF ATTENTION
5: Automatic Bias, Experts and Amateurs
5.1 Introduction
5.2 A Structure for Explaining Bias
5.3 Epistemic Bias Is Necessitated Bias
5.4 Overt Attending as Mental Bias
5.5 Epistemic Virtue in the Eye
5.6 Automatic Bias and the Distribution of Gaze as Good
5.7 Automatic Thinking in Fields of Thought
5.8 The Norms of Attention
5.9 Taking Stock
Note
6: Deducing, Skill and Knowledge
6.1 Introduction
6.2 Deducing with Models
6.3 Formally Deducing and Learning Rules
6.4 Taking and Sensitivity
6.5 Skill and Knowledge
6.6 Knowledgeable Control and Practical Understanding
6.7 Taking Stock
Notes
7: Introspecting Perceptual Experience
7.1 Introduction
7.2 The Need to Carefully Define Introspective Tasks
7.3 Introspecting as Mental Action
7.4 Reliability Conditions for Simple Introspection
7.5 Complex Introspection and Blur
7.6 Introspection and Bad Cases
7.7 Taking Stock
Notes
Epilogue
Bibliography
Index

Citation preview

Movements of the Mind

Movements of the Mind A Theory of Attention, Intention and Action WAYNE WU

Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Wayne Wu 2023 The moral rights of the author have been asserted All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: 2023930905 ISBN 978–0–19–286689–9 DOI: 10.1093/oso/9780192866899.001.0001 Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY Links to third party websites are provided by Oxford in good faith and for information only. Oxford disclaims any responsibility for the materials contained in any third party website referenced in this work.

Contents Introduction Claims by Section

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P A R T I . T HE S T R UC T UR E O F AC T I O N A N D AT T E N TI O N 1. The Structure of Acting Appendix 1.1

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2. Attention and Attending

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P A R T I I . I NT E N T I ON AS PRA C T I C A L M E M OR Y AND R EMEMBERING 3. Intention as Practical Memory 4. Intending as Practical Remembering

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PART III. MOVEMENTS O F T HE MIND AS D E P L O Y MEN T S O F AT T E N T I O N 5. Automatic Bias, Experts and Amateurs

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6. Deducing, Skill and Knowledge

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7. Introspecting Perceptual Experience

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Epilogue Bibliography Index

231 233 255

Introduction This work would not be possible without my wife, Alison Barth, so at its beginning, I want to thank her. In our youth, we had a “public” debate on a train from Oxford to London, neurobiologist versus philosopher. As we pulled into our stop, an older English gentleman sitting across from us leaned over and said, “I agree with her.” That sums up a lot. There was a difficult period after I left science, lost and unsure of what to do. Alison patiently weathered the storm with me. Since then, we have shared the ups and downs of a rich, wonderful life together, raising two daughters who remind me every time I am with them how their strength, intelligence, and beauty reflect their mother’s. So, Alison, thank you for your companionship and your love. This book is inadequate to all that, but it is the best I can produce. I dedicate it to you with all my love.

0.1 A Biologist’s Perspective The title, Movements of the Mind, plays on the default conception in science and philosophy of action as bodily movement. On that view, there are no mental actions. This leaves out much. Pointedly, I focus in what follows on mental movements such as attending, remembering, reasoning, introspecting, and thinking. There are general features of agency seen more sharply by avoiding the complexities of motor control. Focusing on mental actions facilitates explanation. That said, my arguments apply to movements in their basic form, that presupposed in discussions of free, moral, and skilled action. To understand these, we must understand basic agency, an agent’s doing things, intentionally or not, with the body or not. I aspire to a biology of agency, writ large where philosophy plays a part. Such a broad view theory aims to integrate different levels of analysis: a priori argument, computational theories of mental processes, psychophysics, imaging and electrophysiology of neurons, and, though not here, the genetic and molecular. To systematically understand agency as it is lived we must understand it from multiple levels. The link to biology is necessitated when philosophical theories posit psychological processes and causally potent capacities that in us are organically realized. Such theories are enjoined to impose empirical friction, to show

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0001

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that claims generated from the armchair about the living world make contact with the actual world as we live it. Philosophical psychology is replete with causal claims about subjects and their minds derived from thought experiments, dissected by introspection, or informed by folk psychology and ordinary language. Yet rigorous inquiry into specific causal claims is the provenance of empirical science. Philosophical psychology should not theorize about what happens mentally in the complete absence of empirical engagement. The requirement is not that philosophers should do experiments. Rather, where philosophical inquiry postulates causal features of mind, we philosophers should delve into what is known about relevant biology. Well, I have felt obligated to do so. I see engaging in empirical work as a way of keeping my own philosophical reflections honest to the way the world is as we empirically understand it. This is not to say that the engagement is only in one direction. Ultimately, science and philosophy of mind should work together as part of biology, for they share a goal: to understand the world. I hope to provide a detailed outline of what agency as a biological phenomenon is. This is a deeply personal project. I began academic life as an experimental biologist. In college, I gravitated to organic chemistry which describes the movement of molecules that join and alter in principled ways to form other molecules. A course on genetics introduced me to DNA and the central dogma, a chemical transition: DNA to RNA to proteins. Biology conjoined with chemistry promised to explain life through principles of organic interaction and recombination. Inspired, I took every biology and chemistry course I could. Graduate school followed. A professor waxed nostalgic of the old days when he and his colleagues would argue about the mechanisms of life over coffee. Occasionally, someone would leave to start a centrifuge for an experiment then return to continue debating. That sounded like the good life, but the reality of biological research felt different. Centrifuging samples once illuminated important principles (see Meselson and Stahl), but for me, it was one more tedious part of life at the lab bench. It was theory that grabbed me, not bench work. After two unhappy years of experimental tinkering, I dropped out. It was a devastating loss of an identity I had cultivated. Skipping over the lost years, I simply note that I found my way to philosophy. So here we are. This book reflects the distant aspirations of my younger self though I have inverted my prior explanatory orientation. While my younger self believed in working from the bottom up, from molecules to life, this book works initially from the top down, from philosophical reflection on an agent’s doings toward the activity of neurons. Still the same goal remains, the systematic illumination of living things. Accordingly readers will find many empirical details in what follows. They are essential. How else can we achieve a systematic understanding of lived agency? Indeed, ignoring empirical work closes off opportunities

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for new insights as I hope to show by intersecting working memory with intention (Part II, Chapters 3 and 4). I have focused on research at the center of current empirical work and have worked hard to make the details clear and accessible. Please don’t skip them. That said, the empirical work should not obscure the fact that the central level of philosophical analysis concerns an agent who is a subject who perceives, thinks, is consciously aware, loves and hates, is bored or engaged, aims for good and ill. Most of the empirical work I draw on focuses on the algorithmic, psychological, and neural processes that constitute subject-level phenomena so lie at levels below subject-level descriptions. The difficult challenge facing cognitive science is how to bridge these “lower” levels of analyses with the subject level we care about in deciding how to live. We should not kid ourselves that these bridges are simple to construct. The overreliance on folkpsychological vocabulary, and corresponding lack of a technical vocabulary (here’s looking at you, attention), makes building such bridges seem deceptively simple. Yet agency as a subject’s doing things is not explained just because cognitive science sometimes uses subject-level vocabulary in describing basic processes (consider the concept of decision making). Scientists, who I hope will read this book, might respond to Part I by noting there are already detailed theories about action and attention in the empirical literature. Yet despite the subject-level terms that literature deploys, the related empirical studies I adduce explicate the mechanisms underlying the subject attending and acting. The challenge that remains is to deploy empirical accounts of the brain’s doing things to inform understanding an agent’s doing things intentionally or not, skillfully or not, reasonably or not, angrily or not, automatically or not, freely or not, and so on. To do so requires that we properly characterize the ultimate target of explanation: an agent’s acting in the basic sense. This book aims to explicate that subject-level phenomenon, and if successful, to sharply delineate a shared explanandum for cognitive science and philosophy. A model for the bridging project I have in mind is David Marr’s (1982) emphasis on a computational theory which provides a unifying principle to link other empirical levels of analysis. The analysis of action, attention, and intention that I aim for is of that ilk. I hope scientists and philosophers will through this book find common ground.

0.2 Central Themes I argue for four central themes about action. The first is this: Action has a specific psychological structure that results from solving a Selection Problem.

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Parts I and II delineate and detail this structure while Part III shows how the structure illuminates three philosophically significant forms of mental agency: mental bias, reasoning, and introspection. These topics are often investigated without drawing substantively on philosophy of action, yet drawing on the right theory of action advances understanding. Specifically, the structure of action unifies the three phenomena as forms of attention. The structure of action allows us to provide an analysis of automaticity and control motivated by solving a paradox (Section 1.4). These notions are crucial because intentional action is characterized by automaticity and control. Control is at the heart of intentional agency, but automaticity is a feature of all action, indeed necessarily so (Section 1.4). Crucially, we must not infer the absence of agency from the presence of automaticity (cf. talk of reflexes as a contrast to action; Section 1.2). This fallacy results from overly casual, nontechnical use of these notions in the philosophical literature. To understand agency, we must use the notions of control and automaticity technically, notions crucial to understand learning, skill, and bias (Chapters 5 and 6). Here is a challenge to my friends and colleagues: If clear technical notions of central theoretical concepts are given, why not use them? Why persist in drawing on mere folk-psychological conceptions in a philosophical psychology that aims to be serious psychology? Philosophers have doubted that we have got action right. Philosophical discussion has focused on a causal theory of action. Yet the persistent problem of deviant causal chains shows that we have not adequately explained agency in causal terms. I draw a specific lesson from the failure: crucial parts of the psychological picture are missing from the causal theory. Specifically, you can’t get action right if you leave out an essential component: attention. Action theorists have largely ignored attention (check the index of any book on action). Sometimes they mention it, but it cannot be merely mentioned. That yields an incomplete psychology of action. You can’t act in respect of X if you are not attending to it. Attention guides. Lack of attention promotes failed action. So, ignoring an agent’s attention is akin to ignoring her belief or desire in the traditional causal theory. If one fails to discuss central psychological components of action, one will fail to explain action. Attention illuminates action. It is not a mere appendage in action but an essential part.

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Finally, even for those aspects of agency that we have discussed since the beginning, the engagement with biology opens up new avenues for illumination. Drawing on the biology shifts our thinking about intention in the following way: Intention is a type of active memory: practical memory for action. This is, perhaps, the most substantial shift in the theory of agency that I argue for in this book (Part II, Chapters 3 and 4). It is motivated by the biology, specifically by research on working memory, along with a philosophical argument that the coherence and intelligibility of intentional action from the agent’s perspective depends on memory in intention (Section 4.2). Intention reflects an agent’s activeness that regulates the agent’s solving what I call the Selection Problem, the need to settle on a course of action among many. In action, intention constitutes the agent’s remembering (thinking about) what to do as she does it, and in such remembering, the agent’s intending dynamically biases the exercise of her action capabilities as she acts. Indeed, her thinking about what she is doing in her intending to act keeps time with her action through continued practical reasoning. It provides her a distinctive access to her intentional doings.

0.3 The Book’s Parts The book is divided into three parts. Part I establishes the structure of action, explicating its components with emphasis on attention and its interaction with a bias. Here’s a mantra: An agent’s action is her responding in light of how she is taking things, given her biases. Taking things, a term of art, picks out a myriad mental phenomena that serve as action inputs such as her perceptually taking things to be a certain way. Accordingly, an action’s geometry is characterized by three aspects: (1) the agent’s taking things such as her perception or memory of things, (2) the agent’s responding such as her body’s moving or her mental response in applying a concept or encoding a memory, and (3) a bias, a factor that explains the specific coupling which is the causal link between (1) and (2). A bias is the psychological factor that explains the expression of action. This yields the basic structure:

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Figure 0.1 The structure depicts an agent’s acting, each node standing in for a feature of the agent qua subject, say a state, event, process, capacity, etc. Each solid arrow indicates an actual causal link between nodes. Such depictions of action structure will be used throughout the book. In acting in the world, the agent is responding (the output), guided by how she takes things (the input) given her being biased in a certain way. Action’s structure is given in the tripartite form, each node a constituent of action. Here, the agent responds to a stimulus S1. The input’s guiding response is a process that takes time, so the structure depicts a dynamic phenomenon.

Note that the structure is a blunt way of representing a complicated, dynamic phenomenon characteristic of a subject as agent. It is not a depiction of parts within the subject. Rather, it is a structural description that isolates different aspects of the agent’s being active, each analytically pulled out from an amalgam of her exercised capacities that normally blend into her acting. It sketches, coarsely, a dynamical integration of the subject’s different perspectives, say in her intending and perceiving, and her exercised abilities to respond. When the agent acts intentionally—in this book that means acting with an intention—the structure involves intention as a specific type of bias:

Figure 0.2 As in Figure 0.1 but here the agent’s responding to S1 is guided by how she takes S1 given her intending to act in a certain way.

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The amalgam of the three nodes is the agent’s intentionally acting. Crucially, intention and the input are in action, not numerically distinct from it. Other actions, intentional actions without intentions as when one is driven by emotion or needs, slips and flops as well as habitual, skilled, expert, incompetent, moral, immoral, passive, and pathological agency (among others) are explained through and by building on this structure. In particular, the identity of the bias provides a crucial differentiating node, individuating different types of action. Accordingly, the geometry provides a unifying explanatory frame. This book shows how applying it illuminates disparate agentive phenomena. Part I summarizes, elaborates, and integrates many of my published articles, with a greater emphasis on the notion of a bias as well as (hopefully) clearer presentation of my views which (I believe) have remained mainly unchanged in essentials (of course, I might be wrong). This first part identifies action as the solution to a Selection Problem, a problem that must be solved in every action. The Problem arises in light of an action space that identifies the different actions available to an agent at a time and in a context. To act, an action among possible actions must be selected. We can embed the geometry of intentional action in the agent’s action space constituted by action possibilities, each possibility constituted by an input linkable to an output, a possible causal coupling (see Figure 0.3). The structure depicted in Figure 0.3 explains the agent’s guidance and control in intentional action. Control is explicated in terms of intention’s role in solving the Selection Problem. The intention represents an action that is one of the paths in the action space and brings about that action. Agentive control is constituted by the agent’s intention biasing solutions to the Selection Problem, specifically through biasing the input and output capacities to facilitate their coupling. The concept of automaticity is precisely defined in contrast to control to resolve a conceptual paradox in the theory of automaticity. The resolution sets down a technical analysis of these crucial notions, crucial because intentional agency exemplifies a pervasive tug of war between automaticity and control (Section 1.4). Guidance is explained as the function of the input state set by the agent’s bias. The input state informs the output response and in doing so constitutes the agent’s attention in action. While attention in intentional action is set by intention, I argue that attention is a constituent of every action, intentional or not. Attention is always biased (Chapter 5). Part II explores intention as an active memory. It is a practical memory, the agent’s remembering to act (Chapter 3). Intention’s mnemonic activity is partly expressed in how it regulates attentional phenomena in light of the agent’s conception of what is to be done. I argue that empirical work on working memory probes the activeness of intention. As I will put it, where the agent is immanently about to act or is acting, in intending to so act, the agent is being active. While acting, the subject continues to actively remember, that is to think

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Figure 0.3 Intention solves the Selection Problem, given an action space that presents multiple possible actions. The intention solves the Problem by engendering the action it represents, here the action Φ which is constituted by responding (R1) to how the subject takes the stimulus S1. Solid arrows indicate actual causal connections, dotted arrows identify possible ones. The downward solid arrows from intention directed at both input and output nodes identify relations of biasing, explicated in the text as cognitive integration (Section 1.7). Intentional action is an amalgam of (1) the intention, (2) the input taking, and (3) the response guided by the input. This is the triangular structure in darker lines, top portion of the figure. Both input states (indicated as active by black circles) are activated by stimuli in the world (S1 and S2), but only one guides a response. Response R2 is inactive (lighter gray circle), but it could have been coupled to the subject’s input states. Downward gray arrows indicate additional inputs and outputs.

about, what she is doing. Intending is the action of practical remembering, exercised in keeping track of action (Chapter 4). This active remembering involves sustained practical reasoning as the agent acts (Section 4.5) and is the grounds of the agent’s distinctive and privileged non-observational access to what she is intentionally doing (Section 4.6) and how she can keep time with her action (Section 4.7). Let me enter a special plea. The approach to intention will, I think, jar many readers since it will clash with certain philosophical intuitions and frameworks, with how we ordinarily speak about intention, with folk psychology,

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and perhaps with introspection. The plea is that missing from all this has been a biological perspective that should be given at least equal weight, indeed I think more. I hope to show that cognitive science has been doing detailed investigation of intention though not always with use of that concept/term. What that work reveals is the dynamics of an agent’s intending, and when the work is bridged to philosophical concerns, there is remarkable cohesion and illumination. Having established action’s psychological structure, Part III draws on the theory to investigate three specific movements of mind much discussed in the philosophical literature: (a) implicit, better automatic, bias in actions of ethical and epistemic concern, (b) deductive reasoning, and (c) introspecting perceptual experience. While my theory applies to any movement, I choose these three because they identify central topics of philosophical investigation as well as salient features in philosophical practice itself. Notably, each is a distinctive way of attending. I urge readers to work through each of these chapters even if they do not work on the topics covered. Many of the basic themes in Parts I and II are further developed in Part III. First, automatic biases reflect a complex diachronic and synchronic modulation on attention in agency. Experience and learning are common sources of bias critical to understanding the many positive and negative biases of epistemic and ethical concern. What drives much biased behavior is biased attention. Bias often reflects a more or less skilled deployment of attention. This engages normative assessment of attention: when the agent acts in a negatively biased way, they are often attending amateurishly or, worse yet, viciously and incompetently. In isolating historical influences on attention, I provide a new way of understanding the causal structure of automatic biases, including many implicit biases, and this structure provides a map of precise targets for normative assessment (see Figure 5.1). In deductive reasoning, the subject sharpens cognitive attention in moving from premises to conclusion where premises serve as attentional cues for logically relevant contents, leading to increased cognitive focus in drawing logically entailed conclusions. In symbolic logic, a capacity to construct proofs depends on attention to logical form, this inculcated on the basis of developing attentional skills through joint attention with an instructor in light of the norms of reasoning. Hence, deductive action is regulated by rules of inference that, through intention, bias cognitive attention in reasoning. Importantly, rules are not targets of cognitive attention as premises. Instead, they regulate reasoning by setting attention as a bias. We can thereby avoid the regress of rules (Carroll 1895) while providing rules an explicit role in action (Section 6.4). Finally, I close with introspection, a crucial source of data for many philosophical and empirical theories. While the use of introspection is central to philosophy and in arenas that appeal to how things subjectively seem such as the science of

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consciousness or medicine, we have no adequate theory of introspection as a psychological phenomenon. For all we know, introspective deliverances are typically and systematically inaccurate. Whether this is so is an empirical question. Claims about introspective accuracy or inaccuracy should be informed by understanding introspection as action, hence by the biology. There is a philosophical consensus that introspection involves attention but with few details regarding attention’s role. Philosophers often postulate a distinctive type of “internal attention” for which we have no good empirical evidence. The final chapter draws on the theory of attention to explain introspective action. This provides a concrete basis for justifying introspection’s use in specific contexts and for rejecting its deliverances in others, some surprising. There is much work to do to improve our introspective practices, and this begins with understanding intentional introspection as attention.

0.4 Chapter Summaries Let me summarize each chapter. A list of propositions argued for in each section is presented at the end of this introduction and can be read as a detailed summary of the book. Chapter 1 establishes action’s psychological structure as an input guiding an output in solving a necessary challenge facing all agents, the Selection Problem. Where the agent acts on an intention, intention solves the Problem, establishing agentive control. The automaticity of action is defined by resolving a paradox of automaticity and control. Chapter 2 establishes that attention constitutes guidance in action and that every action involves attention. Three basic attentional phenomena are identified: vigilance as a readiness to attend, attention as guiding action, and attending as action. Attention as the activity of guiding output response has explanatory priority. It is guidance in action. Chapter 3 establishes that intention is a type of memory for work and that the literature on working memory reveals the dynamics of intention as the source of agentive control. Drawing on the biology, intention is construed as an agent’s being active, an active memory that works to establish vigilance and maintains steadfastness in action, preventing distraction and slips. Chapter 4 identifies intending as an action of thinking about what one is doing in active remembering. Intending-in-action keeps time with action by updating its content through continued practical reasoning: fine-tuning of intention’s content. This explains the agent’s distinctive, privileged, non-observational access to her action. Chapter 5 explains that many biased behaviors of epistemic and ethical concern are rooted in biased attention set by experience and learning. That negative and

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positive biases in attention are learned places biased attention within a context of normative assessment such as the standards for skill and expertise in a given practice. Negative biases reflect an undesirable amateurism, incompetence, or viciousness. Chapter 6 explains deductive reasoning as the development of the agent’s cognitive attention where premises serve as cues for logically relevant guiding features. As capacities for reasoning are learned, the development of abilities to attend to logically relevant properties is an acquired skill and type of attentional expertise. The exercise of these abilities can be explicitly controlled by the agent’s grasp of inferential rules. Chapter 7 explains introspection of perceptual experience as the distinctive deployment of attention in accessing the conscious mind. Conditions for reliable and unreliable uses of introspective attention in accessing perceptual consciousness are detailed. Salient cases of introspection in philosophy and psychology are shown problematic. Principles for improving introspective practice are presented.

0.5 Acknowledgments I have many intellectual debts. I am grateful to Steve Lippard, Amy Rosenzweig, and the late Vernon Ingram for teaching me, years ago, to be a scientist. My work bears the imprint of their mentorship. Mike Botchan, Barbara Meyer, Don Rio, and Robert Tjian were among my teachers in graduate school and, at the end, tried to help me find my way before my exit from science. I appreciate their efforts. I am grateful to the Howard Hughes Medical Institute for a predoctoral fellowship. My career didn’t pan out the way expected, but I hope that this book shows the fruits of that investment in a budding biologist. The transition from science to philosophy was rough. One of my first philosophy courses was Martin Thomson-Jones’s graduate seminar at Berkeley, taken right after I dropped out of science. Having never studied philosophy as an undergraduate, I was in over my head. Martin read one of the worst seminar papers, written by yours truly, but kindly gave feedback and encouragement over coffee. Edward Cushman, whom I only knew at the time as one of the philosophy grad students, stopped by while I was working in the departmental library to encourage me after I had given an amateurish presentation in Martin’s class. It was a random, deeply appreciated act of kindness. I am sure many of us have felt imposter syndrome or uncertainty whether we belong. Moments of encouragement can make a difference, so I want to thank Martin and Eddie in print for those moments. They aren’t the only ones who helped over the years, but they did so at a sensitive time. There are too many people to list, conversations with whom have shaped the ideas in this book. Many of you are perhaps reading this now. Though you are

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unnamed, I hope you’ll know that I’ve learned from all those conversations and that I look forward to more in the future. There are many relevant works that I do not discuss in detail. To write a shorter book (I know, this isn’t that short . . . ), I focus on selective points of clash and contrast. Regretfully, much is left unsaid. To pick just two topics: on mental action and cognition, there is important work by Peter Carruthers, Chris Peacocke, Lucy O’Brian, Joelle Proust, and Matt Soteriou among others (see also a recent book edited by Michael Brent and Lisa Miracchi Titus 2023) and on attention, work by Imogen Dickie, Carolyn Dicey Jennings, Jonardon Ganeri, Abrol Fairweather and Carlos Montemayor, Chris Mole, Declan Smithies, and Sebastian Watzl among others. I apologize for the lack of sustained engagement and aspire to do so in print in the future. John Searle and Jay Wallace advised my dissertation where many of these ideas began. Hong Yu Wong’s group at many points engaged with the ideas expounded in the following pages, so thanks to him, Chiara Brozzo, Gregor Hochstetter, Krisz Orban, and Katja Samoilova for making Tübingen an intellectual focal point for me. A reading group at the Center for the Philosophy of Science (University of Pittsburgh) provided helpful feedback. Thanks to Juan Pablo Bermúdez, Arnon Cahen, Philipp Hauweis, Paola Hernández-Chávez, Edouard Machery, and Adina Roskies. In London, I worked through the manuscript with Zijian Zhu, Matthew Sweeney, Jonathan Gingerich, Eileen Pfeiffer Flores, Chengying Guan, and Seth Goldwasser in an on-line class during the lockdown. Thanks also to Bill Brewer, David Papineau, Barry Smith, and Matt Soteriou for their help in making London a great place to write a book, and for discussions. I presented the material in various places in the US, UK, and Europe during the pandemic. Thanks to Anita Avramides, Will Davies, Chris Frith, Anil Gomes, Alex Grzankowski, Patrick Haggard, Zoe Jenkins, Mike Martin, Matthew Parrot, Chris Peacocke, Harold Robinson, Jake Quilty-Dunn, Nick Shea, Sebastian Watzl, and Keith Wilson for feedback. Francesca Secco worked through the material with me and organized a class in the University of Oslo that I taught on the book. I am grateful to her and the students for comments. I have benefited greatly from philosophers at the Human Abilities Project, Berlin. Barbara Vetter and Carlotta Pavese had their reading group dissect Chapter 5 and Sanja Dembić and Vanessa Carr and their group worked through an earlier paper on which Chapter 2 is based. Sanja and Vanessa organized an online workshop on my manuscript. I am grateful to the commentators: David Heering, Vanessa Carr, Helen Steward, Sarah Paul, Chandra Shripada, Carlotta Pavese, and Christopher Hill. Thanks to Denis Buehler, Steve Butterfill, Kim Frost, Thor Grünbaum, Aaron Henry, Liz Irvine, Matthias Michel, Myrto Mylopoulos, and Josh Shepherd for comments. Dan Burnston and Mike Roche later weighed in. Aaron Glasser and Malte Hendrickx organized a reading group at Michigan to work through the book, and Gabe Mendlow, Catherine Saint-Croix, Jonathan

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Sarnoff, and Laura Soter gave helpful feedback. Recent discussions with Denis Buehler, Liz Camp, Piera Maurizio, Tom McClelland, Jesse Munton, Susanna Siegel, Sebastian Watzl, and Ella Whiteley on salience helped me bring Chapter 5 into shape. I thank two referees for helpful feedback, especially reader “X” for detailed and generous comments that provided timely encouragement. Years ago, Peter Momtchiloff asked some questions of a young philosopher wandering around the APA, jotted down a few things in his notebook and would ask about my proposals on later crossing paths. This book is tenuously related to those grandiose plans. My thanks to Peter for following up and for supporting this project, to Tara Werger who helped me prepare the manuscript and deal with pesky permissions with an occasional tidbit about the London theater scene, and to Rio Ruskin-Tompkins for a fantastic cover (more on that in a moment).

0.6 Family My wife and I, with our youngest daughter, travelled to London, U.K. to sabbatical in February of 2020. It was not the sabbatical we planned for. Still, there were blessings. The U.K. lockdown had unexpected benefits in providing space and time to write a book in a quiet, subdued London. Our oldest daughter, forced out from college due to the pandemic, came to stay as well. We endured the lockdown together as a family. In thinking about family, let me complete the circle at last but most assuredly not least: to my beloved daughters, Madeleine and Eleanor (Pei and Mei), thank you for making this actual timeline the best possible one. I am also grateful to Madeleine for the image that graces the cover. Providing a glimpse of a quiet London Underground station during the pandemic as a train slips by, her photograph perfectly captures the book’s title and the mood of London during the time in which much of this work was written. Ok, let’s begin.

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Claims by Section 1.1 An agent’s acting intentionally has a psychological structure: an agent responds guided by how she takes things given her intending to act. 1.2 Action as a structured phenomenon arises from a Selection Problem, a necessary challenge facing agents, one set by an action space constituted by paths that link inputs, the agent’s taking things, to outputs, the agent’s capacities for response, where a path implemented is the agent’s acting. 1.3 The agent’s intentionally acting is a solution to the Selection Problem due to her intending to act in the relevant way serving as a bias that explains why the Problem is solved in the way that it is. 1.4 Automaticity and control pervade intentional action and can be rigorously defined: features are controlled by being intended, and those that are not intended are automatic. 1.5 Bias is a necessary feature of action and can be tied to control or automaticity. 1.6 Control in intention is revealed behaviorally, biologically, and computationally in biasing relevant input and output states in accord with the content of the intention. 1.7 Biasing by intention involves the intention cognitively integrating with input states to solve the Selection Problem consistent with the intention’s content. 1.8 In learning to act, acquiring the appropriate biases, a subject comes to directly intend to act in shifting the balance between automaticity and control. 1.9 As theories that identify actions in terms of their causes make the subject qua agent a target of control, not in control, the agent’s causal powers, tied to her intending and to her taking things, must be internal, not external, to her action. 1.10 The mental elements of the agent’s action identify the agent’s being active though short of acting, for her being active partly constitutes her acting. 2.1 There are three salient modes of attention: vigilance, attentional guidance, and attending as action. 2.2 Attention is mental guidance in action, the agent’s taking things informing response. 2.3 Attention as guidance, a necessary part of a solution to the Selection Problem, is present in every action. 2.4 Attention is not a mechanism modulating neural activity; rather it, as a subject-level activity of guiding response, is constituted by specific neural modulations. 2.5 Attending as an action is guided by attention, often with improved access to its target. 2.6 Attention can be both automatic and goal-directed by being sensitive to the agent’s many biases. 2.7 Attentional capture is a form of passive agency but is distinct from the passivity of perceiving, a behavior that is never an action. 2.8 Attention is everywhere, largely automatic, mostly unnoticed. 2.9 When a subject attends to a target, she is acting on it. 2.10 Central cases of causal deviance involve the disruption of attention, hence the absence of appropriate agentive guidance, a necessary feature of action. 2.11 Intention sets the standard for appropriate attentional guidance in intentional action.

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3.1 Intention is practical memory for work, actively regulating and maintaining action-relevant capacities. 3.2 The agent’s remembering is her cognitively attending to mnemonic contents. 3.3 Working memory is memory for action, specifically for the control of attention and its central executive component is the basis of the agent’s intention. 3.4 Empirical investigation of working memory as an executive capacity explicates the activity of intention in setting attention. 3.5 Intention proximal to action is an active memory for work that modulates vigilance, a propensity to attend. 3.6 Intention-in-action keeps the agent steadfast, sustaining attention against distraction and preventing slips. 4.1 Acting on an intention involves the agent’s intending, a simultaneous action of practical reasoning as she acts. 4.2 Practical memory is the basis of the agent’s conception of her action that renders it intelligible to her. 4.3 Intending-in-action is constituted by fine-tuning of practical memory in practical reasoning. 4.4 Fine-tuning is practical memory at work, its dynamics revealed in the dynamics of working memory. 4.5 As the agent acts, keeping track of what she is doing involves the exercise of practical reasoning as part of her developing her intending to act, as she acts. 4.6 Intending-in-action maintains distinctive, authoritative, and non-perceptual access to action through practical reasoning. 4.7 By practical reasoning, the agent keeps time with her action in intending. 5.1 Bias is a critical factor explaining acting well or poorly, and accordingly, attention is a critical factor in such explanations. 5.2 A central source of bias on attention is revealed in the setting of priority, including that set by historical influences. 5.3 Epistemic bias often begins with biased attention. 5.4 Every movement involves a mental guide in attention, so no action is “purely bodily” including overt visual attending. 5.5 Virtuous automatic bias in visual attention can be learned through practice and training as demonstrated in epistemic skill in medicine. 5.6 Gaze is a good whose distribution is automatically biased in ways that can have negative consequences in academic and social settings. 5.7 Perception and cognition operate over a biased field, the set of inputs in an action space, its structure revealed by automatic perceptual and cognitive attention. 5.8 Attention is a target of normative assessment, and the panoply of biases on attention provides a map for such assessments. 6.1 Reasoning is the deployment of skilled cognitive attention. 6.2 Deducing, on semantical accounts, is constituted by sharpening cognitive attention in moving from premises to conclusion where said premises provide cognitive cues for attention. 6.3 Rules typically contribute to control, rather than to guidance, and in this way a reasoner can explicitly invoke rules.

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6.4 Taking premises to support a conclusion is grounded in the acquisition of recognitional capacities through rule-based control during learning that avoids Carroll’s regress of rules. 6.5 Knowing how, understood as what we acquire in learning and practice, involves the acquisition of schemas. 6.6 Learning shapes the agent’s knowledge of how to act, and this knowledge provides a developmentally based bias on the agent’s action, one often coordinated with the agent’s intention to act in the way learned. 7.1 Introspecting is like any action: there are contexts in which it is reliably successful and contexts in which it reliably is not. 7.2 As an action, introspection’s reliability is sensitive to task instructions. 7.3 Introspecting perceptual consciousness is guided by perceptual attention. 7.4 Simple introspection draws solely on perceptual experience as constituting introspective attention, and its reliability is a function of the reliability of the components of perceptual judgment. 7.5 Complex introspection, typically used in philosophy, can be reliable, but it is challenged by multiple sources of noise. 7.6 It is not clear that introspection can adjudicate metaphysical debates about perceptual consciousness.

PART I

THE STRUCTURE OF ACTION AND ATTENTION Action has a psychological structure with attention as a necessary part.

1 The Structure of Acting 1.1 Introduction An agent’s acting intentionally has a psychological structure: an agent responds guided by how she takes things given her intending to act. In this book, an agent’s acting intentionally means an agent’s doing things with an intention. Although I focus on intentional mental agency, my theory applies to all actions: intentional action writ large, unintentional and automatic actions, actions done from emotions, implicitly biased actions, pathologies of agency, passivity behavior, and so on. Acting with an intention has a psychological structure: An agent’s acting intentionally is the agent’s responding in a specific way, guided by how she takes things, given her intending to act. Given her intending to act on a drink, the agent’s visually taking in the glass guides her reach for it (bodily action) or her encoding its location in memory (mental action). Generalizing from intention to bias yields the basic structure of action: An agent’s acting is the agent’s responding in a specific way, guided by how she takes things given her biases. Actions are movements of body and mind, transitions within an action space constituted by the possible actions available to an agent in a context and time. In intentional action, that structure has two salient components: guidance in the agent’s taking things informing her response, and control in the agent’s intending to act. Control sets guidance. This chapter explains these ideas.

1.2 The Selection Problem and the Structure of Acting Action as a structured phenomenon arises from a Selection Problem, a necessary challenge facing agents, one set by an action space constituted by paths that link inputs, the agent’s taking things, to outputs, the agent’s capacities for response, where a path implemented is the agent’s acting.

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0002

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A behavior space describes behaviors available to a system at a time and context. The space is constituted by potential couplings of inputs to outputs, a causal linking of both. A system’s behavior is the instantiation of a specific coupling, a path in the space. Behavior spaces describe physical systems whose behavior is decomposable to input and output mappings where the input explains the output. Action verbs describe such behaviors: plants turn to the light, machines sort widgets, a fax transcribes a message. Since these systems lack mentality, they exhibit mere behaviors. They are not acting in the sense at issue where action can be rational, reasonable, appropriate, skillful, clever, clumsy, moral, or freely done. Behavior spaces of minded systems have input takings that are mental phenomena with intentionality. Taking things is a term of art, referring to subject-level states, intentional mental phenomena such as perception, memory, imagination, and thought. The behavior spaces within which action emerges are thus psychological behavior spaces, specifically action spaces. To be an action space, input-output couplings must meet a certain profile: the intentionality or content of the subject’s taking guides her response. To take a basic form of guidance, one’s visual experience identifies a location to respond to. The experience guides by providing spatial content that informs a movement. Guiding content explains why the response occurs as it does. How content precisely sets response will be relative to the action kind in question, say setting parameters for movement or encoding content in memory. Action spaces are a subset of psychological behavior spaces which are a subset of behavior spaces. In the psychological behavior space, the basic structure of behavior is:

For example, the psychological input might be a perceptual experience of something X in the world, so a typical case will be:

As perceptual experiences are responses to the world, here stimulus X, we have:

There need not be a perceived stimulus, for some behaviors are driven by memory or hallucination. All behaviors begin with a mental input, the subject’s

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taking things: a subject’s perceiving an object’s having a property is the subject’s perceptually taking the object to have a property; a subject’s perceiving an object is the subject’s perceptually taking in the object and so on. To uncover action’s structure, contrast action with reflex. An agent undergoing a reflex evinces behavior but not action. Action admits of the qualifications noted earlier. Reflexes, however, do not express rationality, grasp of reasons, skill, or expertise. They are not targets of normative assessment, are never intentional or done freely. They lie outside the set of actions. I will focus on a class of reflex where a mental input guarantees a response. This excludes spinal cord mediated reflexes since these are not guaranteed in the sense at issue. What is it about reflexes that rules out action? I suggest that it is the necessitation of response. Consider an engineer who programs a reflex in a robot, a system-preserving response to a dastardly danger. The engineer aims to ensure that the response necessarily occurs if danger is detected, yet the system can fail to escape. In response, the engineer rejiggers the system to eliminate such failures. Although a physical system can never be so modally robust as to rule out all possible failures, the engineer’s ideal is to asymptote to a limit where one has a reflex which could not possibly fail, a necessitation that the engineer aspires to but can never attain. It is not attained by biological reflexes. To render salient the necessitation at the core of the contrast to action, focus on reflex at the idealized limit, a pure reflex which eliminates all other behavioral possibilities. The corresponding behavior space is a single path:

Pure reflexes rule out agency by eliminating alternative behavioral possibilities. The subject is purely a patient suffering a change. So, consider any world in which a subject generates a behavior. 1.

If a subject undergoes a pure reflex, this behavior is not her acting.¹

Equivalently: 2.

If a subject’s behavior is her acting, then her behavior is not a pure reflex.

If a behavior is not a pure reflex, it lacks the individuating feature of necessity. If so, another mapping is available, another possibility, most simply (though other mappings are possible):

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Figure 1.1 In a branched behavior space, the input is mapped to two possible responses to it. The input is darker because it is an active response to a stimulus, the responses lighter because they are not yet engaged. Dotted lines indicate possible couplings.

A behavior space that excludes pure reflexes involves additional behavioral possibilities, a branched space. So 3.

If a behavior is not a pure reflex, it occurs within a branched behavior space.

Thus, 4.

If a subject’s behavior is her acting, then it occurs within a branched behavior space.

Every instance of the agent’s acting is an actual input-output coupling among possible couplings. In real life, behavior spaces are highly branched. As shown in Figure 1.2, a common space is a many-many mapping (in cognitive science, see Desimone and Duncan 1995; Miller and Cohen 2001, 167–8; and Appendix 1.1). Branching raises a Selection Problem. To act, the agent must respond to how she takes things. By hypothesis, that link is not a pure reflex so it is one among other possibilities. Thus, branches delineate a space of possible behaviors. The Selection Problem requires selection among possibilities where the solution, the path taken, just is the agent’s acting, the coupling of an input to an output. The Problem underscores that we cannot instantiate all behavioral combinations. For example, an agent can multi-task, say perform two actions at a time, but also perform each action singly. The agent cannot both multi-task and singly-task. One of these options, among all options, must be instantiated to solve the Selection Problem. Otherwise, no action is performed (cf. Buridan Action Spaces and death, Section 1.5). For exposition, I focus on a single path.

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Figure 1.2 The Selection Problem in its many-many version, linking many inputs to many response outputs, these possible couplings defining the action space. In the figure, only two inputs and two outputs are depicted, but typically, there are many more inputs and responses (imagine two very long vertical columns and a mass of connections linking them). The inputs are darker because they are active responses to the stimuli, the responses lighter because they are yet to be activated.

5.

For a behavior to occur within a branched behavior space, that behavior must involve a coupling that is one among others.

If we understand the instantiation of a coupling to be what is meant by “selection” of one among many behavioral paths—where such talk does not suggest an additional action of selecting—then 6.

For a behavior to occur within a branched behavior space, that behavior is a selected path, one that is actualized among other possible paths not taken.

Selection is just the mapping from a Problem to its solution in an action performed. Then, 7.

If a subject’s behavior is her acting, then that behavior is a selected path that is one selected among others.

This provides the structure of an agent’s acting in the worlds we are considering, every possible world where there is non-reflex behavior by subjects. This behavior is constituted by coupling the agent’s taking some X to the agent’s response to X against the background of a branched action space. We have arrived at a necessary structure of action, mental or bodily.

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Figure 1.3 An action is a solution to the Selection Problem. The particular act is categorized as of the type Φ. Φ-ing is the response R1 mapped to how the agent takes the stimulus S1. The input guides the output in that its content explains the production of R1. The action is a process that takes time, the input’s guiding response (dark solid arrow). Both inputs are active but only one guides behavior. Dotted arrows indicate possible paths not taken. Dark circles indicate active nodes, lighter circles inactive nodes.

Let me make a few general comments. First, applied to our world, the Selection Problem captures a structural challenge for all agents. It presents a structure of causal possibility that an agent must navigate to act. Second, a causal action space identifies what the agent can objectively do at a time yet is detached from the agent’s conception of her options. To capture her perspective, we focus on a doxastic action space largely delineated by her beliefs about her options. A human agent typically only knows about a proper subset of the actual causal possibilities. If some of her beliefs are false, the space, or portion of it, based on what she falsely believes differs from the space anchored on what she knows. Reasoning and learning can expand and alter the bounds of doxastic action spaces relative to the causal action space. In addition, among the actions the agent believes available, only some are advisable or obligated. A normative action space delineates the actions an agent ought to do, in the relevant sense of “ought.” Such spaces, defined by relevant normative requirements, might contain only one action as when Martin Luther protested, “Here I stand. I can do no other” (these paragraphs owe much to David Heering). We can gloss the contrast in action spaces by saying that what one ought, or has reason, to do is less than what one can in actuality do. Indeed, the agent might not know that she has such obligations. Decision making occurs within doxastic and normative action spaces, but the nature of action in the basic sense is explicated within the causal action space.

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Figure 1.4 A map of different types of behavior spaces and their relations. The map is coarsely rendered since a single action space might have paths that occupy one region, say a required action (normative space), and other paths that occupy another space, say an action one believes falsely that one can do (doxastic space). Caveats noted, here are key points. Behavior spaces present the things that an agent can do in the broadest sense, so include reflexes. Action spaces present things that the agent can actually do intentionally, so effectively are causal action spaces. Doxastic action spaces present what the agent thinks (or knows) she can do intentionally, some of which might lie outside of behavior space, so she cannot actually do them (e.g. fly by flapping arms) or within the behavior space, but outside of the causal action space because she must learn to do them (e.g. fly an airplane). An action space lying outside of behavior space is an “action” space by courtesy (e.g. some of its paths might be within the causal action space). Some doxastic spaces identify actions that are within behavior space but not within the causal action space, things the agent can do but has not yet learned how to do (Section 1.8). Learning brings a behavior into action space. Normative action spaces identify things the agent ought to or must do, relative to a normative system. This includes things that she has not yet learned how to do (outside of her causal action space) and things that she might not realize that she has to do (outside of her doxastic action space).

There are ways of bypassing specific premises or weakening the conclusion that are sufficient for my purposes in this book. For example, one can enter the argument at premise (4) which identifies a branched structure for action. That premise recapitulates a common idea that, in general, an agent could have done otherwise than she did. On my picture, this requirement is not a condition on free action but on action in the basic sense (cf. Steward 2012a). Alternatively, one can endorse a weaker conclusion: the actions kinds of interest to philosophy and

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science are solutions to Selection Problems even if not all actions are. Of course, I think the argument goes through! Necessarily, all actions are solutions to the Selection Problem.

1.3 Intentions and Intentional Action The agent’s intentionally acting is a solution to the Selection Problem due to her intending to act in the relevant way serving as a bias that explains why the Problem is solved in the way that it is. “Intention” as I use the term refers to the agent’s representing an action as to be done so as to bring about that action. In experiments, intentions are set by task instructions. Philosophers also treat intentions as bound up with practical reasoning. I focus on a basic form of practical reasoning, what I shall call fine-tuning, the breaking down of the intended action to enable an agent to perform the action directly (e.g. instructions in teaching; Section 1.6 and Chapter 4). This allows that, in humans, intentions are also linked to a more substantial form of practical reasoning, namely planning (Bratman 1987). Intentions explain why the agent acts as she does: a specific path is taken because it is what the agent intends to do. The content of the agent’s intention specifies a solution to the Selection Problem. Intention prioritizes one path over others in action space, specifically, the path corresponding to the kind of action intended. Consider a basic case: for visually guided acting, the content of intention sets what visual target, hence visual taking, guides response. The agent’s intention biases input processing and required output response. “Bias” refers to the sources that influence solving the Selection Problem while “biasing” refers to that source’s shifting priorities in action space (Appendix 1.1 describes a classic connectionist implementation of similar ideas for the Stroop effect). Intention is one kind of bias. In visually guided intentional action, intention biases appropriate visual selection, the agent’s visual attunement to a guiding feature of the action’s target. Guiding features inform response, say the spatial contour of an object that guides a grasping movement or the object’s individuating feature that informs categorization (“That’s a bald eagle!”). In light of a broad sense of “why,” guiding features explain why the response occurs in the way that it does. Attunement to guiding features is attention (Chapter 2). Causal theorists of action have long worried about deviant causal chains, counterexamples to proposed sufficient causal conditions for intentional action (Section 2.10). Thus, the murderous nephew driving to kill his uncle is so unnerved by his intention that he drives recklessly and kills a pedestrian who happens to be his uncle (Chisholm 1966). His intention caused the death, but philosophers agree that the killing was not intentional. If so, the standard analysis fails to explain agentive control and guidance.

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What is it for an agent to be in control and to guide her behavior? Control is constituted by how one’s intention biases the inputs and outputs that are coupled when the agent acts as intended while guidance is expressed in the subject’s taking things informing response. The agent’s intention imposes a form on action by representing a solution to the Selection Problem and biasing a relevant coupling between input and output. This coupling instantiates the action intended. The structure gives a geometry of intentional agency, a “triangular” form embedded in a network of behavioral possibilities that defines an action space for an agent:

Figure 1.5 Intention provides a bias in solving the Selection Problem. The action path is chosen because the agent intends to do just that. In that way, intentions “solve the Problem.” The downward arrows from intention directed at both input and output identify relations of biasing, explicated in the text as cognitive integration (Section 1.7). The intentional action is an amalgam of (1) the intention, (2) the input taking of S1, and (3) the response R1 guided by the input (dark solid horizontal arrow). Action is represented in the triangular structure at the top in darker lines. The input taking responding to S2 is active but does not guide behavior (e.g. the subject sees S2 but does not respond). Dotted lines indicate couplings not taken, darker arrows indicate causal processes.

This structure will be used to explain specific types of intentional action: If perception provides the input (S1), we have perceptually guided action; if memory, then mnemonically guided action; if cognition, then cognitively guided action, and so on. Chapter 2 argues that the input that guides action constitutes

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attention, say perceptual, mnemonic, or cognitive attention. Further, different mental phenomena can bias which input and output are coupled including emotion (Section 1.5), memory (Chapters 3 and 4), experience and learning (Chapter 5), and knowledge of rules (Chapter 6) in addition to intention. In the next section, I argue that bias from intention identifies agentive control.

1.4 Control and Automaticity Automaticity and Control pervade intentional action and can be rigorously defined: features are controlled by being intended, and those that are not intended are automatic. In intentionally acting, the agent expresses control. She can intentionally move objects, come to conclusions, and, in general, change the world. At the same time, action exhibits substantial automaticity. Consider recalling events from the previous evening’s soiree. You express control in remembering that specific event rather than last week’s party. Yet much of memory is automatic as when recalling a cutting remark overheard or the foul flavor of the appetizers. These memories just spring to mind (Strawson 2003; Mele 2009). An adequate specification of skilled action and learning requires technical notions of automaticity and control for which no adequate analysis in philosophy or psychology exists (see Moors and de Houwer 2006). Psychologists have abandoned a systematic analysis, opting instead for rough-and-ready lists of attributes of each (Palmeri 2006). Philosophers of action have aimed to explain control, and recent work on skill has highlighted automaticity in action (e.g. Fridland 2017). Yet the concepts as used lead to a paradox. Begin with two truths about action: 1. 2.

Acting intentionally exemplifies agentive control. Acting intentionally is imbued with automaticity.

At the same time, cognitive science affirms a Simple Connection. 3.

Control implies the absence of automaticity and vice versa.

If intentionally acting involves control yet control implies the absence of automaticity, then the second proposition is false. The theory of automaticity and control in agency is inconsistent. Shouldn’t we reject (3)? The claim is central, fixing a specific conception of control by tethering it to automaticity’s absence. Jonathan Cohen notes: “the distinction between controlled and automatic processing is one of the most fundamental and long-standing principles of cognitive psychology” (2017, 3).

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In foundational papers, Walter Schneider and Richard Schiffrin (1977) proposed that automaticity involves the absence of control and that control should be understood in terms of the direction of attention. Yet attention can be automatic, as when it is captured by a salient stimulus (e.g. a flash of light or loud bang; Section 2.5). As time went on, psychologists failed in their attempts to specify sufficient or necessary conditions for control and automaticity. Where one psychologist explicated control (or automaticity) by appeal to feature F, say consciousness or task interference, another would empirically demonstrate that F was also exemplified by automatic (or controlled) processes (see Appendix 1.1). Psychologists consequently shifted to a gradualist rather than a categorial characterization, affirming that automaticity or control can be more or less. This idea led to proliferating lists of features correlated with automaticity and control (Wu 2013a; Moors 2016; Fridland 2017). Yet even gradualists retain the Simple Connection, for they organize correlated features in terms of automatic and controlled processes. Thomas Palmeri (2006) in an encyclopedia entry on automaticity lists 13 correlated pairs of features along the dimensions of automatic or controlled processes. Similarly, the much discussed Type 1 / Type 2 and System 1 / System 2 distinction draws on such a division.² We cannot reject the Simple Connection without rejecting substantial psychological work. An advance in the theory of agentive control can be secured if the three claims can be rendered consistent. They can. The problem is that (3) is generally read as distinguishing kinds of processes, yet given (1) and (2), intentional actions are a counterexample to this interpretation. The solution is then simple: reject dividing processes as automatic or controlled. Rather, automaticity and control in agency are to be explicated in terms of features of processes. Accordingly, a single process can exemplify both automaticity and control. Intentional action certainly does. (1) is explained through the role of intention as providing the agent’s conception of her action. Elizabeth Anscombe’s (1957) conception of an action being intentional under a description highlights how certain features of an agent’s doing something are tied to what she intends to do, to her conception of her action. The features that are the basis of such descriptions are represented by the agent’s intention. In intending to Φ, the agent does something that has the property of being a Φ-ing, say her intentionally pumping water, poisoning the inhabitants of a house, or saving the world. This yields the following implementation of the first proposition, for S’s Φ-ing at a time t (I suppress the temporal variable): S’s Φ-ing exemplifies S’s control in action in respect of Φ-ing iff S is Φ-ing because she intends to Φ. The agent’s doing something describable as Φ is controlled in respect of Φ when she does it because she intended to. Controlled features are intended features.

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We now affirm the Simple Connection without paradox: control implies the absence of automaticity and vice versa. This entails the second proposition in a specific form: S’s Φ-ing exemplifies automaticity in action in respect of Φ-ing iff S’s Φ-ing is not controlled in respect of Φ-ing. All unintended features of action are automatic. Given the finite representational capacity of human minds, most properties of action will be automatic because we cannot represent all these features in intention. Where the feature is automatic at a time (or temporal range), then it is not controlled at that time (or temporal range), and vice versa. This temporal indexing allows us to track transitions in automaticity and control during extended learning: at an earlier time, Φ-ing might be controlled as the agent intends to practice Φ-ing, but with acquired skill, her Φ-ing later becomes automatic as she need not explicitly think about it. Similarly, when the going gets tough, the agent can transition from Φ-ing automatically to doing it deliberately, bringing action under control precisely because she has to think about what to do, fine-tuning her intention. Thus, on a straight road, my driving is automatic as I converse with you, but noticing a car weaving erratically, I update my intention to focus explicitly on my driving relative to it, so driving now becomes controlled (on “fine-tuning” intention, see Chapter 4; on direct intentions and learning, see Section 1.8 and Chapter 6; on acquired skills, see Chapters 5 and 6). Finally, with an eye toward capturing habits, environmentally triggered actions, and pathologies: S’s Φ-ing is passive when every feature is automatic. Passively acting is completely independent of an agent’s intending to do so. Such actions are fully automatic expressions of specific capacities for action. Since the term “passive action” will strike readers as paradoxical, note that it is a technical term in the theory to capture fully automatic action. There are different notions of control used in cognitive science, philosophy, and other domains that are conceptually disconnected from automaticity, and, as such, they are distinct from the agentive notion tied to the three truths noted earlier. Of these, some are psychological or neural notions that might be relevant to a full biological account of human agency so compatible with the analysis I have provided (e.g. forward models in motor control theory, Wolpert and Ghahramani 2000), which have been influential in theories of schizophrenia (Frith, Blakemore, and Wolpert 2000; Cho and Wu 2013). Those concepts are not my primary concern since a philosophy of action that disregards the role of automaticity in agency, as a contrast to control, is incomplete. Automaticity

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pervades agency and is tied to a form of control that must be understood whatever additional notion of control one invokes. The three claims I have rendered consistent allow a truth about intentional agency expressed by Anscombe to intersect a foundational line of thought in empirical psychology. Automaticity emphasizes that even once we have formed a decision, there are further problems to be solved. Consider two actions, one bodily, one mental. When one intends to drink a cup of coffee, this biases an input, one’s visual experience of the drink, and an output, a “reach-and-grasp” response to be coupled to the experience. One’s subsequent reaching and grasping is guided by one’s seeing the target. Still, there are many ways one’s experience of the drink can inform a movement that brings it to one’s mouth. For example, there are many ways to grasp the target, say with one’s whole hand or with thumb and index finger. Each requires different visual information. Even with the same type of grasp, no two instances need be qualitatively the same (see Wu 2008 for a discussion). Similarly, if one wants to figure out how to get to Timbuktu, there are different ways of doing so: recalling a common route, visually imagining oneself taking various options, deducing the best option given a set of constraints, and so forth. Rarely are two deliberations on the same topic the same. That there are one–many relations between intention and execution establishes the necessary automaticity of action. The challenge of action-relevant selection is not discharged just by intending to act. If one intends to drink the coffee, the targeted mug presents many properties only some of which are relevant to guiding an appropriate reach-and-grasp movement. Thus, the color of the mug is not relevant but the location and contours of the handle are. Even then, given different ways to pick up the mug, action-relevant properties can be further subdivided. Intention cannot represent all these properties. The same points can be made regarding deliberation that draws on imagining, deducing, or recalling. The results of deliberation must be at a finer grain than what we intend. For example, in recollection, we intend to figure out how to get to Timbuktu, but the result is remembering a specific way to Timbuktu, perhaps one among many possible routes. If the intention represented the requisite route, there would be no need to deliberate since the result would already be in mind (Strawson 2003). It is because intention begins with an abstract representation of action that further Selection Problems must be solved to engender a concrete action. These further Selection Problems are not to be solved by deliberation or through conceptualization, at least completely (I called such problems nondeliberative problems; Wu 2008). A distance necessarily remains between the abstractness of the action representation in intention and the concrete details of the action performed. This distance entails the necessity of automaticity in the actions of finite minds, for not every property of the action done can be controlled since not every property can be represented in intention. The distance can be

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narrowed by further practical reasoning that fine-tunes the content of the intention to render it more specific. Still, a gap will always remain given limitations on what we can hold in mind. The intention can never specify all relevant details of action at the precise level of determinacy. It is easy to conflate automaticity with reflex where the latter suggests the absence of agency. Yet while reflexes are by definition automatic, indeed passive in the technical sense, automaticity does not imply reflex so does not imply the absence of action. Automatic activities that contribute to action also involve the agent. Consider the bodily action of intentionally reaching for a cup. Reaching requires not just visually experiencing the target, but also selecting its actionrelevant properties among irrelevant properties. These guiding features, here spatial properties of the cup, need not be represented in intention, but the agent must be sensitive to them to guide her bodily response. Is this sensitivity at the subject level attributed to the agent rather than to some part of her? Assume that the subject’s involvement is only at the level of the visual state that reflects the abstractness of the intention’s content, say that of seeing the mug. The agent’s involvement stops at her intention and its abstract content. Yet seeing the mug does not suffice for guiding a precise reach-and-grasp response for one must also be sensitive to—take in—the action-relevant properties such as the mug’s precise location, its shape, and its orientation. If the subject’s involvement is restricted to those features of action in the scope of her intention, then the automatic visual attunement to specific action-relevant properties is not subject involving. The subject would be like the factory manager who never does any work but give orders. Others make things happen. Similarly, detailed guidance in action would be instituted by something that does not involve the agent. The agent can only wait and see if things unfold as she intends. She contributes nothing beyond having an intention. This picture abolishes the agent acting. Rather, the subject acts only if guidance is due to her take on things, even at the level of fine-grained attunement. Not only is the agent in control in intentionally acting, she is also the guide. Let me emphasize that what has been introduced is a technical characterization of automaticity and control, two concepts necessary for an adequate characterization of action. We should stop using central notions non-technically. For empirically oriented philosophers of mind who (should) accept the Simple Connection, the concepts are incoherent given the paradox. My resolution has several advantages beyond securing coherence. It explicates automaticity and control in light of the Selection Problem and stays true to the Simple Connection. Further, it defines control and automaticity sharply at a time and allows for gradualism across time in giving a precise sense to talk of more or less automaticity/control. Gradualism emerges because the analysis allows for the extremes of passivity and of full control and everything in between (I discuss gradualism further when examining learning; Chapters 5 and 6). The analysis

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conceptually unifies philosophical and empirical concerns, so the definitions should be our working account of automaticity and control. If I may: I urge readers to take the analysis on board or do better.³

1.5 The Necessity of Bias for Action Bias is a necessary feature of action and can be tied to control or automaticity. Intentional agency involves a tug of war between automaticity and control rooted in different sources of bias. Action, intentional or not, necessarily involves bias. Consider the Selection Problem in a Buridan Space:

Figure 1.6 A Buridan Action Space where two objects, S1 and S2, are qualitatively identical such that the agent has no basis on object features alone to choose one to act on.

A donkey sees two qualitatively identical bales of hay equidistant from it, one to the left (S1), one to the right (S2). The animal’s action space consists of two inputs and one output, an eating response. So, the donkey can eat the bale on the left or on the right. If the donkey has no intention to act, there is no bias from intention. Absent another bias, the Selection Problem remains unsolved leading to death. Action requires bias. Bias can emanate from other mental phenomena. Consider Rosalind Hursthouse’s discussion of arational actions, actions driven by emotion (Hursthouse 1991). When emotions motivate action, they must solve the Selection Problem. Like intention, they bias an action. That Alex is angry at Sam rather than Chris explains why Alex is focused on Sam in lashing out. Absent an intention to lash out, Alex’s action being an outburst is by definition automatic. Emotion leads to action by occupying the biasing role first identified by appeal to intention:

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Figure 1.7 Emotion such as anger can provide a bias in solving the Selection Problem.

Note that emotion and intention can work together or against each other in biasing solutions to the Selection Problem. Indeed, in typical action, there are a variety of biases, congruent and incongruent (see Chapter 5 on implicit, automatic biases and Figure 5.1). The features of an action generated by emotion are automatic, and a purely emotion-driven action is technically passive. Consider: S’s Φ-ing at T is emotionally responsive in respect of T iff S is Φ-ing at T because she is in an emotional state directed at T. A person can be said to be controlled by his emotion, speaking loosely, when an emotion is the bias in action. Someone lashing out in blind rage is a slave to passion even if emotionally responsive. The struggle between control and automaticity is highlighted when we try to get our emotions under control, say by doing something intentionally, hence with control, to disrupt the force of emotion. Here, one bias attempts to cancel out another (cf. reward bias in Section 5.2). Automatic biases will figure throughout the book as they figure pervasively in lived action.⁴ As noted in the Introduction (Figure 0.1), the most general structure of action is as follows:

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Figure 1.8

An input can also be biased in being randomly activated sufficient to guide a response, say due to noise in the neural basis of the input. This can lead to thoughts that randomly pop into one’s head or the feeling of a cell phone buzzing in one’s pocket when there is no cell phone. Spontaneous activity of the neural basis of thought or perception drives a response. Buridan’s donkey might find itself munching on the left bale precisely because spontaneous neural activity altered the perceptual representation of that bale to drive a response, automatically. Perhaps that bale suddenly seems closer, larger, more delectable, and now the donkey moves. Similarly, I suddenly reach for my cell phone when I hallucinate a tactile buzzing in my pocket. In veridical perception, we speak of a “bottom-up” bias tied to the capture of attention by a salient stimulus. Salience as conceptualized in cognitive science provides a basic bias (Section 2.6). A sudden flash or loud sound grabs one’s attention. Here, attention is pulled automatically, without intention, emotion, or other mental attitudes as bias. The capture of attention by salience is a basic passive movement of mind. We began with bias in control due to intention, but most bias is tied to automaticity and provides the lion’s share of what shapes actions (Chapter 5). Human action spaces are constituted by action capacities that can be expressed in light of an appropriate bias. The agent’s ability to act in a certain way, to Φ, is depicted in an action space articulated by specific input-output paths. Each path identifies what an agent can do. Path expression is the agent’s acting. An action capacity is constituted by action-relevant capacities: psychological inputs, say perceptual or mnemonic capacities, and outputs such as capacities to move the body or to encode a content. The expression of these action-relevant capacities constitute the agent’s being active as part of her action. Each action-relevant capacity is the target of biasing. If the bias is an intention, these capacities are the target of cognitive integration (Section 1.7). Action capacities are individuated as such in light of being potential targets of intention so as potential parts of intentional action.

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There might be agents who do not form intentions and in whom other mental phenomena play a functionally similar role, say those with rudimentary beliefs and desires. There might also be creatures driven largely by emotions or perceptual salience. In relation to human intentional action, their behavior is fully automatic hence passive, driven by passion or the environment. The Selection Problem and the concept of bias delineate many kinds of agency of which the human form, planning agency with its functionally rich form of intention, being one of many. The notion of control is conceptually tied to a specific form of intentional action that humans exemplify, but an expansive theory of agency including the agency of non-human animals will eventually opt for a broader conception of control tied to varieties of executive function. That project is a comparative biology of agency I do not take up here. We can explain passive action, understood technically, as constituted by action capacities which are activated fully independent of intention. Passive actions, like reflexes, are fully automatic, yet are distinct from pure reflexes in being solutions to the Selection Problem. Such passivity includes certain forms of mind wandering and daydreaming but also pathologies of action, such as passivity phenomena in schizophrenia, the hearing of voices or the experience of inserted thoughts (see Cho and Wu 2013 for an overview of mechanisms of auditory hallucination).

1.6 The Biology of Intention-Based Biasing Control in intention is revealed behaviorally, biologically, and computationally in biasing relevant input and output states in accord with the content of the intention. This section provides an empirical check on my a priori argument about the structure of agency. Let us begin with two questions: Control: How does the agent’s intention to act prioritize some inputs relative to others and some outputs relative to others to yield appropriate coupling? Guidance: How does the input inform the production of the response? Guidance can be independent of control since automatic (passive) actions are guided. It is instantiated whenever the subject’s taking things informs her responding to a guiding feature. Guiding features explain why the response is generated as it is. Control implicates a distinctive form of agency where the agent’s intention biases a path by prioritizing specific input and output constituents. In general, biasing prioritizes certain capacities for action. Psychology and neuroscience identify realizers of the action structure identified a priori.

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Agents are inundated with too much information. Empirical theories of perception have long emphasized the need for selection in order to generate coherent behavior in light of information overload (Broadbent 1958). For example, the visual system faces information overload under normal viewing conditions and selection is required to avoid informational meltdown. Yet even without excess information, a Selection Problem remains. Action, not information overload, is the fundamental constraint necessitating selection (Neumann 1987). Buridan’s ass doesn’t face information overload but certainly must overcome a Selection Problem, else death. Consider a common case of intentionally investigating the world: looking around. An exemplary study was conducted by Alfred Yarbus (1967; cf. Greene, Liu, and Wolfe 2012). Yarbus presented his subjects with the same stimulus, I. M. Repin’s painting of a homecoming scene, a man returning after a long absence, surprising his family at meal. He assigned his subjects different tasks: to remember aspects of the painting or make judgments about the story. To do so,

Figure 1.9 The stimulus in Yarbus’ experiment is I. P. Repin’s “Unexpected Visitor” (A). Lines show scan paths of a subject’s saccadic eye movements. Task instructions are as follows: (B) remember the clothes worn by the people; (C) remember the position of people and objects in the room and (D) estimate how long the visitor has been away from the family. Reprinted by permission from Springer Nature: Springer, Eye Movements and Vision by Alfred Yarbus (1967, 174, fig. 109). This figure has been adapted and reprinted from M. F. Land. 2006. “Eye Movements and the Control of Action in Everyday Life.” Progress in Retinal and Eye Research 25, 296–324, with permission from Elsevier.

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subjects had to visually select relevant information, moving their eyes to fixate relevant targets. We have visual inquiry. When these movements were tracked over time, intelligible patterns emerged given the task. When subjects were asked to remember the clothing worn by people in the painting, their fixations centered around those figures; when asked to remember the location of objects in the room, fixations ranged widely to various objects; and when asked to estimate how long the father had been away, fixations focused on faces to estimate emotional response. The eye moved to items needed to guide task performance. Scientist’s set the intentions of cooperative experimental subjects through task instructions. Yarbus’ different instructions modulate intention, leading to altered movements. When intention is set, response (eye movement) shifts given a constant stimulus. This manipulation suggests that the content of the intention—the task instruction that informs the subject’s plan—plays a causal role in generating the observed response. Toggling intention by instruction leads to task-relevant changes in behavior needed to appropriately solve the Selection Problem. Similarly, in mundane life, we are instructed by others or in our own case, “self-instruct” by deciding to act. This sets an intention that leads to action. Such experiments identify a behavioral correlate of the biasing role of intention that we have postulated. Action requires solving the Selection Problem, and in intentional action, the solution must be sensitive to the subject’s intention. Thus, I postulated a causal dependence between the path selected and what the agent intends. Yarbus’ experiment, indeed any behavioral experiment involving task instructions, confirms this, showing how action couplings, here specific eye movements to visible targets, change over time to serve the agent’s intention (for further work on task-relevant eye movement, see especially work from Michael Land, Mary Hayhoe, and co-workers, e.g. Land, Mennie, and Rusted (1999); Hayhoe and Rothkopf (2011); Hayhoe and Ballard (2014). Recall the question concerning control: given the agent’s intention, how are inputs prioritized relative to other inputs and how are outputs prioritized relative to other outputs to explain why a specific coupling arises? The behavioral work shows that intentions yield behavior in conformity to their content. I have explicated this functional role in terms of intentions providing a bias to solve the Selection Problem. Neural biasing in the brain implements intentional biasing by the subject. Consider an illustrative case: We can monitor visual system activity directly during intentional performance of tasks. Leonardo Chelazzi et al. (1998, 2001) examined visual processing in awake behaving monkeys. The animals were trained to perform a delayed match to sample task. In this task, an animal holds the eye at the fixation point. A cue identifying the target (here a flower) briefly

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appears at fixation. The animal must remember the sample during a delay period, a working memory task (Chapter 3). Subsequently, the animal was presented with two test stimuli, at most one of which matched the sample. The subject must either identify the match, making an eye movement to it, or maintain fixation if there is no match.

Figure 1.10 This depicts a delayed match to sample task. The subject maintains fixation while activity from a neuron is recorded. The neuron’s receptive field is identified by the dotted circle. A target (the flower) appears at fixation and the subject must remember it during the delay period, a working memory task. In the last panel, two targets are presented in the neuron’s receptive field, and the animal must report the match by moving the eye to it or, if there is no match, by keeping the eye at fixation. This figure is modified and reproduced from Leonardo Chelazzi et al. 2001. “Responses of Neurons in Macaque Area V4 during Memory-Guided Visual Search.” Cerebral Cortex 11: 761–72, by permission of Oxford University Press.

The test array presents the animal with a Selection Problem similar in structure to a Buridan Space. Solving the Problem depends on the animal’s intention to perform the task and on correlated shifts in visual processing. Chelazzi et al. examined activity in two visual areas: (1) V4, a mid-level area in the ventral visual stream; and (2) in inferotemporal cortex, deeper in the ventral stream where strong neural responses to objects are found. The ventral stream is a necessary part of the neural basis of conscious seeing of object and form (Ungerleider and Mishkin 1982). Lesions in this stream lead to visual agnosias, inabilities to see form, faces, or objects (Farah 2004). Visual neurons respond to specific parts of the visual field. The spatial receptive field for a visual neuron corresponds to specific areas in the visual field relative to fixation in which the neuron is responsive to stimuli. The visual neurons monitored by Chelazzi et al. are tuned toward certain stimuli in that preferred stimuli induced a strong neural response, a high firing rate, the generation of many action potentials (spikes) that carry information. In contrast, non-preferred stimuli generate weaker responses. For our purposes, the signal that carries information is the firing rate of the neuron (there are many neural codes; DeCharms and Zador 2000).

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What does task-relevant neural selection look like? For those not used to thinking about neural activity, think of each neuron as a homunculus deploying a signal. In the Chelazzi et al. experiment, the visual neurons in V4 or inferotemporal cortex are presented with two items in their receptive fields, only one of which might be task relevant. The neurons must signal to other systems regarding the task-relevant stimulus. This reproduces at the level of neural processing the Selection Problem the animal faces in performing the task. How does the neuron respond to the Problem? Consider the following data regarding neural activity which maps the firing rate (spikes/sec) over time with time 0 being when the stimuli are presented.

Figure 1.11 This figure shows the average response of 76 visual neuron under four stimulus conditions. The y-axis gives the number of action potentials (spikes) generated per second while the x-axis is the time relative to stimulus presentation at t = 0 milliseconds (ms). The grey and black vertical bars on the x-axis indicate latency of the eye movement to the target in the one and two target presentations, respectively. The thin solid line shows response to the preferred stimulus (flower) when it is presented alone. The thick solid line shows response when the preferred stimulus is presented with a second, less preferred stimulus (mug; cf. thin dotted line for neural response to just the mug). Note that the neural response is suppressed with two stimuli as the peak of the thick solid line is lower than the peak of the thin solid line. Crucially, following the thick solid line, at about 175 ms, the neural response begins to shift, becoming more like the neural response to just the preferred stimulus alone (thin solid line). The authors note that this is as if the receptive field is “contracting” around the target stimulus. Gray circles around a stimulus in the two stimuli conditions, two rightmost circles, identify it as the correct target. This figure is modified and reproduced from Leonardo Chelazzi et al. 2001. “Responses of Neurons in Macaque Area V4 during Memory-guided Visual Search.” Cerebral Cortex 11: 761–72, by permission of Oxford University Press.

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This neuron responds strongly to the flower, the preferred stimulus (high firing rate, >40 spikes per second; thin solid line; stimuli are illustrative, not the actual stimuli used). When a second object (a cup) is placed in the receptive field, this object suppresses the neuron’s response to the flower as seen in the lower firing rate represented by the thick solid line. Activity is lower than it would be had the preferred stimulus appeared alone (at about 100 milliseconds (ms); dark line). Suppression can be understood as resulting from competition between the two stimuli for the neuron’s limited signaling resources, its spikes. The result is less information regarding what object is in the receptive field. Uncertainty about object identity increases. Let’s start with the task where we present a sample to be remembered. The animal must subsequently report whether the sample is matched in a subsequent test array of two stimuli. If there is a match, the animal must move its eye to the match in report. We present the animal with a flower which it commits to memory. This fine-tunes the animal’s intention, from an intention to report a match to an intention to report a match to this sample. Now, we test the animal by providing it with two possible matches, one of which is the flower (presented at time “0”), and begin monitoring neural activity. Focus on the darkest line in the figure. The presence of the two stimuli, one preferred and one not, leads to neural competition and suppression of the neuron’s response to a lower level than if the flower was presented alone. Given the animal’s intention to report a match to the remembered flower, it must select the flower to guide an eye movement to it while ignoring the distractor mug. Selection for action must be constituted by selection at the neural implementation level. Follow the dark line over time. The suppressed response eventually coincides with what the neuron’s response would be if only the flower was in the receptive field (overlap of thick and thin solid lines just after 200 ms). As Chelazzi et al. note, it is as if the neuron’s receptive field has contracted around the task-relevant object. Its response is seemingly driven only by the task-relevant stimulus as competition is resolved in favor of that target. Similar results are reported in fMRI in humans (e.g. Reddy, Kanwisher, and VanRullen 2009). This selective processing is the neural correlate of biasing in solving the Selection Problem at the psychological level: what the animal intends makes a difference to behavioral and neural selection. Task-relevant selection has been argued for a priori given the Selection Problem. Solving that Problem is recapitulated in behavioral experiments like Yarbus’ that must have a basis in neural processing. Such task-relevant shifting of processing is conceptualized computationally as biased competition (Desimone and Duncan 1995). At the subject level, competition is encapsulated by the Selection Problem. At the neural level, task-relevant competition in the visual system is exemplified by suppression of neural activity when multiple stimuli are

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present. Talk of a neural bias is talk of that which shifts processing to generate response. In the Chelazzi et al. experiment, this neural bias originates from the neural basis of the animal’s intention to match a remembered sample as per the trained task (for related work in humans, see Carlisle et al. 2011). In the Yarbus experiment, bias emerges from the neural basis of remembering (intending) an instructed task. The structure at which we have arrived a priori is, as it must be, connected to the behavior and biology of actual agentive systems (Miller and Cohen 2001; Cisek and Kalaska 2010). Philosophical, psychological (behavioral), neural, and, as discussed in the next section, algorithmic perspectives converge on the Selection Problem. This suggests that we are seeing matters aright.

1.7 Intention-Based Biasing as Cognitive Integration Biasing by intention involves the intention cognitively integrating with inputs to solve the Selection Problem consistent with the intention’s content. I present a hypothesis about human agents in light of the structure of action and the biology: Bias by intention involves cognitive integration with subject-level capacities. The subject-level notion of bias from intention is constituted by neural bias that resolves neural competition in solving the Selection Problem, linking Yarbus’ behavioral results with Chelazzi et al.’s electrophysiological data. While I have elsewhere discussed biased competition as cognitive penetration (Wu 2017b), here, I dissociate integration from penetration. My discussion is relevant to debates about the latter, but I set that issue aside. To mark this, I shift terminology. Integration explicates intention’s causal role in light of primate biology. It is an algorithmic notion founded on informational or content exchange captured as follows: X is in an informational transaction with Y in respect of content C if in its computations, X computes over C as provided by Y. “Information” and “content” are used expansively to allow for different theoretical accounts of what influences computation. A compelling idea is that where the information carried by a signal functions to guide behavior, that signal functions as a representation (Millikan 1984; Dretske 1991). I take the informational/content transactions between subject-level states as founded on the transactions between their neural bases. Accordingly, in explicating intention’s functional role as biasing inputs, say seeing (visual taking),

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I examined task-relevant shifts in visual processing due to shifts in what is intended. An agent’s intention biases inputs such as her visually taking in a target only if the neural basis of the intention stands in an informational transaction with the neural basis of the subject’s visual takings. Accordingly, visual processing shifts to serve the intention. In Chapters 3 and 4, I shall discuss the neural basis of intention by leveraging research on working memory, but here, I give a computational gloss. The Chelazzi et al. experiments point to the ventral stream’s facing a neural version of the Selection Problem, one resolved by its sensitivity to what the subject intends to do. They provide evidence for the type of information transaction I am postulating. To state the point strictly: Cognitive Integration by Intention: If an information processing system that is the basis of the agent’s intention to R contains information regarding the intention’s content and the system that is the basis of the agent’s input states computes over this information to generate a task-relevant subject-level state S rather than Sn, then the intention cognitively integrates with S (for example, in Figure 1.5, S = S1 Sn = S2).

A mouthful, but the basic idea is that if the input system establishes a selective orientation by computing over content from intention, then the input system is integrated with cognition. The interaction is computational. In the Chelazzi et al. experiment, the visual ventral stream responds to information about the target of the subject’s intention, changing its processing to select the intended target and exclude the distractor before contributing to guiding the eye to the former (Milner and Goodale 2006 argue that the ventral stream serves as a pointer for visuomotor computations in the dorsal visual stream that informs visually guided movement). At some point, such selection by the visual system is the basis of the subject’s being in a subject-level visual state (S) tuned to the target rather than to the task-irrelevant distractor (Sn). Cognitive integration of intention with visual takings is built on computational exchange between their neural bases as postulated in biased competition. The resulting shift in action space involves prioritizing action capacities needed to execute the intention. Consequently, a coupling of biased input and output is instantiated, the subject’s responding in light of how she takes things, given her intention to act. The task-relevant shift in neural processing has more recently been modeled in terms of divisive normalization which can be understood as an instance of biased competition (Reynolds and Heeger 2009; Lee and Maunsell 2009). Divisive normalization has been dubbed a canonical neural computation (Carandini and Heeger 2012). John Reynolds and David Heeger provided one version:

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Figure 1.12 The left box presents a fixation point and two stimuli. The stimulus on the right of that box, a vertically oriented contrast patch, is the task target. The response of neurons that respond to orientation are recorded and mapped along the y-axis based on their tuning to vertical orientation in the dark boxes in the figure (these are the bright vertical lines in each dark box). Brighter locations indicate stronger neural response, so greater preference for vertical orientation. Note the output population response that shows prioritization (stronger response) of neurons that respond to the right stimulus after divisive normalization. A more complete description is given in the text. Reprinted from John Reynolds and David Heeger. 2009. “The Normalization Model of Attention.” Neuron 16 (2): 168–85, with permission from Elsevier.

Let’s start with an intuition of how we might expect processing to change when the animal intends to act on a specific object. In this example, the animal maintains fixation while two objects of vertical orientation appear, left and right at L and R. Treat the right stimulus as the task-relevant target designated by a cue to the subject. Prior to the cue, we expect the brain to be faced with a Buridan Action Space in that there is no differentiating the two putative targets. Once one target is identified as task relevant, however, the brain must solve the Selection Problem by withdrawing from one object to deal effectively with the target. The latter should be prioritized. With intuition in hand, consider the computation depicted. The input stimulus drive is a representation of responses among many visual neurons which have receptive fields responsive to space marked along the x-axis (we look at just one dimension for ease). That map depicts the activity of neurons that respond to stimuli at positions L and R. The neurons represented as active have different

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tuning to the stimuli. For some neurons, vertical orientations are preferred, and they fire strongly (note bright center of each vertical line in the stimulus drive map). Other neurons prefer non-vertical orientations, and their response scales according to how different their preferred orientation is to vertical, with response dropping as their preference moves further away from vertical (moving up and down from the center of the vertical lines, noting that lines grow dimmer as one does so, this indicating decreasing response). Thus, the y-axis of the stimulus drive map depicts the varying activity of these neurons that have receptive fields that respond to either L or R. Notice that we have a neural Buridan Space in that there is no differentiating the stimuli based on level of response. Now, the visual system computes over these representations. Note what is effectively suppression, namely dividing neural response by what is also called the normalization pool (Lee and Maunsell 2009), here called the suppressive drive. We can treat normalization as an expression of competition. Second, the input is multiplicatively scaled by a factor ascribed to attention, the attention field. Effectively, this is a spotlight model of attention which I shall question in Chapter 2. Consider instead what information the attention field carries. The animal has been cued to the location of the task-relevant target, namely the right stimulus and thereby knows, forms a specific intention, to act on that target. The attention field thereby carries a signal regarding the task-relevant location. This, I submit, is just the information of where the animal intends to act (see Chapter 3 on sensory working memory). On that assumption, the visual system is computing over a representation of the content of the animal’s spatial intention as signaled in the attention field. In that respect, visual processing is integrated with the subject’s intention since vision computes over this cognitive content (for more discussion, see Wu 2013b, 2017b). The result is withdrawing from the taskirrelevant stimulus on the left in order to prioritize the task-relevant one on the right, as seen in the shift in neural population response (output) indicating a stronger signal for the target on the right (prioritizing) and a weaker response for the target on the left (withdrawal). If the result is the activation of a visual capacity of tuning toward the task-relevant object, the divisive normalization model shows how intention integrates with visual capacities. The behavioral situation described is static since the hypothesized subject is maintaining fixation. The eye, however, moves two to three times a second, so input is constantly changing. As input changes, intentions keep time, so on the postulated model, the attention field (better, the intention field) will dynamically shift. If the agent updates her intention, this involves a basic form of practical reasoning (fine-tuning; Chapter 4). Accordingly, there is a sense in which integration reveals intention as being active in setting attention over time. I return to this idea of being active in Section 1.10. Yarbus’ experiment demonstrated that eye movements are appropriately sensitive to the agent’s intention. Chelazzi et al. demonstrated that visual neural

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processing is sensitive to the content of the agent’s intention. Reynolds and Heeger show how this interaction between intention and vision can be understood as cognitive integration. If this synthesis of a priori, behavioral, neural and computational perspectives is correct, then we return to the sufficient condition: intention cognitively integrates with visual takings because the neural basis of the former institutes a shift in processing in the neural basis of the latter to prioritize the task-relevant target, S rather than Sn. This biological shift constitutes the psychological shift captured in changes in action space that eventuate in the intended action.⁵ In Section 3.5, I will consider an upshot of this shift as the establishment of vigilance, a preparation to attend (on the neural networks supporting this, see Corbetta and Shulman 2002; Corbetta, Patel, and Shulman 2008).

1.8 Learning to Act and Shifting Control In learning to act, acquiring the appropriate biases, a subject comes to directly intend to act in shifting the balance of automaticity and control. Learning plays a central role in my account of automatically biased behavior and deduction in Part III. Typical learning is the result of intentional action. As one learns how to Φ, the balance of automaticity and control in Φ-ing shifts, paralleling a change in cognitive integration. Let X be a guiding feature, what one is attuned to in the input that guides response. Given the structure of action, the agent’s taking(X) guides her response R. Let there be an action Φ-ing on X constituted by coupling the agent’s Taking(X) to response R:

For example, one might reach for a glass of water X guided by one’s seeing it or one might answer a question X by posing it to oneself. When the action is performed, an action capacity is exercised, anchored on the subject’s taking in the guiding feature. The appropriateness of a coupling, Taking(X) ! R, is measured against one’s intention, namely whether it satisfies what is intended. Intention provides the standard of success whereby couplings are assessed. Where the coupling satisfies the intention, R is appropriately informed by one’s taking X. Learning transforms action spaces. At a given time, an agent either has an action capacity constituted by the coupling type, Taking(X) ! R, or she does not. That is, the agent can or cannot intentionally Φ. If she cannot, the coupling is not a possibility in the agent’s causal action space. To Φ, the corresponding ability must be acquired through learning. If Φ-ing is something that the agent can learn

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to do, it is in the agent’s behavior space, a behavior she can in principle perform. Learning moves behavioral capacities into action space, thereby increasing the agent’s action repertoire.

Figure 1.13 A behavior space, or at least a set of potential behavior couplings, moves into the set of causal action spaces when an agent learns how to act in the way at issue. This is depicted by the dot that moves from behavior to action space.

Now, the agent can Φ intentionally. If so, the capacity to Φ can be cognitively integrated with intention. Learning does not cease when one learns to intentionally Φ, for one’s Φ-ing is sensitive to further practice. Changes in one’s ability to Φ are tied to changes in the intentions needed to engage the capacity to Φ precisely because practicing Φ-ing is motivated by the agent’s intention to do so. Practice leads to a change in the agent’s intention vis-à-vis her capacity to Φ. The agent’s intending to Φ is direct if it can cognitively integrate with the capacity to Φ leading to her so acting without the need to fine-tune the intention.

In the normal case, when the agent intends to Φ at the present time, she simply does so. When the intention is not direct, hence indirect, fine-tuning is needed for the intention to integrate with action capacities. That is, the action intended must be broken down into digestible parts, and the content of the intention is correspondingly sharpened, representing how to Φ by doing specific subactions.⁶

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Consider the virtuoso violinist. She intends to play the violin, and she plays. No further thought is required. She does not need to think through the basic steps. Step back in time, however, when she was a child at her first lesson. Then, she intended to play the violin but her intention is indirect for she did not know how to play. More thought was required. Her teacher helped her by describing and demonstrating subactions that, when put together, amount to playing the violin: picking up the bow and instrument, holding the violin under one’s chin, putting the bow on the string, drawing the bow across it, and so forth. This involves sharpening how the agent takes things and how she responds. Some of the subactions the teacher highlights are those that the child can directly intend to do for she knows how: picking up an object, putting something under her chin. If the subactions are too complicated, the teacher breaks things down further to subparts that the student can practically understand and directly do. The teacher’s instruction fine-tunes the student’s intention and also sets appropriate attention. Learning involves joint practical reasoning and joint attention (cf. Section 6.4 on learning symbolic logic). The virtuoso learned through practice and instruction, and in doing so she acquired both a capacity to act and, through intense practice, a skill. The shift from indirect to direct intention correlates with a shift from control to automaticity for certain features of the action. At the beginning, the specification of subactions is part of the agent’s conception of what it is to play the violin. In intending to do those subactions, her intention directly engaged extant action capacities present at the time of learning. Definitionally, these subparts, things she could directly do, were controlled in being explicitly intended. This allowed her to learn something more complicated based on things she already knew how to do. With increase in skill, intended subparts come to be performed automatically, for the agent no longer needs to explicitly intend to do them. The agent simply intends to play, now automatically doing the necessary subactions. In general, if Φ-ing involves doing X, Y, and Z, then early in learning, one’s intending to Φ cannot be direct as one has not yet learned how to Φ. Rather, one must fine-tune the intention to Φ into an intention to Φ by doing X, Y, and Z where the latter are subactions that can be directly done and are explicitly intended. Working memory capacity limitations will constrain such learning, say in the student’s ability to retain complicated instructions (the content of her intention; Chapter 3). After much practice, one need only intend to Φ. Doing X, Y, and Z need not be explicitly intended, so are then, by definition, automatic. No fine-tuning is needed (cf. the hierarchical account of intentions, Elisabeth Pacherie 2008, Mylopoulos and Pacherie 2019; cf. Brozzo 2017).⁷ The balance between control and automaticity changes as skill and expertise are acquired. This exemplifies the gradualist approach noted in Section 1.4. I shall return to this when discussing attentional skill in medicine (Section 5.4) and in symbolic reasoning (Section 6.4).

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1.9 The Agent Must Be in Control in Action As theories that identify actions in terms of their causes make the subject qua agent a target of control, not in control, the agent’s causal powers, tied to her intending and her taking things, must be internal, not external, to her action. The phenomena that constitute guidance and control must be attributed to the agent as it is she who acts. Assume that in intentionally acting, neither control nor guidance is attributed to the subject. That is, her behavior is not guided by how she takes things or controlled by her intending to act. Perhaps these executive powers are attributed to some part of the subject’s brain, an internal mechanism disconnected from her perspective. Thus, her response is guided by something that is not her own taking things or is controlled by something that is not her intending to act, even if the cause is part of her body. In such cases, the subject qua agent is not in control. She is along for the ride, subjected to guidance and control rather than being their source. Every property of the resulting behavior will be automatic, so passive. It is difficult to cleanly divide the subject level from levels below the subject, but clear cases suffice to make the point. A paradigm passive behavior is the spinal cord reflex exemplified when one pulls one’s hand away from a burning hot surface. The body moves, but the agent does not move it, hence does not act. Rather, processes distinct from the agent, though part of her, guide her response. The subject’s spinal cord takes over to guarantee the needed response (recall our engineering ideal; Section 1.2). Yet to secure agency, the subject must be the source of control and guidance, not subject to it as in many reflexes. The problematic causal structure in reflex that removes control from the subject is recapitulated by the standard causal theory of action. That theory conceives of executive features such as control and guidance as external to the agent’s acting. On the causal theory, intentional action is treated as an effect, the target of control and guidance rooted in a source external to the agent’s doing things. This externalist causal perspective emphasizes mental causes numerically distinct from the agent’s movements, bodily or mental, as what makes those movements into bona fide action. For example, a movement of the arm is an intentional action when it is caused by an appropriate belief and desire (Davidson 1980a). Else it is a mere movement (cf. Hornsby 1981 on transitive versus intransitive movements). Yet as in reflex, the agent is rendered a patient, a target of an external influence even if the cause is part of her as the spinal cord is part of her. In both cases, something outside of the agent’s action guides and controls her movement. This eliminates genuine agency. One is made to act. Accordingly, control and guidance must be internal, not external, to action. Davidson noted a similar “fundamental” confusion: “It is a mistake to think that when I close the door of my own free will anyone normally causes me to do it,

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even myself, or that any prior or other action of mine causes me to close the door” (Davidson 1980b, 56). One might substitute “anything,” including spinal cord mechanisms or external mental states, for “anyone.” Davidson noted correctly that we are not the causes of our action. After all, as agents, our doing things is the expression of our distinctive causal power. Yet a theory that reveals an event to be an action because of how it is caused exemplifies the confusion Davidson identified. For if my beliefs and desires cause my action, make me close the door, then we have reinstated the problematic causal structure. The fundamental problem is that something deployed to explain agentive control is distinct from and directed at the very thing that should be the source of control. Control begins within, not outside, of intentional action. Intentional agency constitutively involves control and guidance (Wu 2011a). Accordingly, it is not just that control and guidance must be attributed to the agent. It must be part of the agent’s acting. So, the controlling role of intention in an agent’s action is internal, not external, to her acting. Similarly, the agent’s attunement in how she takes things constitutes her guidance when appropriately coupled to her response. Guidance must also be internal to the agent’s doing things. She is not guided by something external to her doing something. Our triangular motif (Figure 1.5) thus captures the structure of intentional action with the agent’s intention, the source of control, and the agent’s taking things, the basis of guidance, as constituents of action. My focus on the internal structure of action bears affinities to work of Harry Frankfurt (1978) and John Searle (1983). Frankfurt identifies guidance as a target of explanation, and his terminology has been taken up by a number of philosophers. My use of guidance differs from Frankfurt’s for, in Chapter 2, I explain guidance as attention while Frankfurt emphasizes counterfactual features of agency as characteristic of guidance, say the agent’s disposition to compensate for perturbations during action (see Shepherd 2021 for a detailed account). I suggest we restrict “guidance” as a technical notion to attention. Cognitive scientists and philosophers speak of visual or memory guidance in action where this points to visual or mnemonic contents informing response, say explaining why one reaches or recalls as one does. As action theorists need that notion of guidance too, Frankfurt’s terminology courts unnecessary theoretical ambiguity. Empirical work has a term for the phenomenon Frankfurt has in mind by talk of “guidance,” namely control. This is not to deny that scientists sometimes use “guidance” as Frankfurt does. The point is conceptual regimentation to maintain a technical way of speaking. In many of the contexts in question, scientists use “guidance” in an informal way which can be replaced with “control” (e.g. Banich 2009 discussion of executive (control) functions as guiding behavior). Earlier (Section 1.4), I noted different conceptions of control, those analytically tied to automaticity and those not. The counterfactually characterized form of control tied to an ability to deal with perturbation, much discussed by

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philosophers of action since Frankfurt, is, from the agent’s point of view, an automaticity. Such quick responsiveness to change is part of agentive skill, refined by sustained practice, and differences in the ability to respond to perturbations reflect levels of skill, say a professional tennis versus a novice player adjusting to a ball suddenly off course having hit a divot in the court (cf. eye movements in medicine; Section 5.5). The fine-tuned movements an agent makes can certainly involve control at a subpersonal level. A compelling empirical idea is that the motor system predicts the consequences of a commanded movement and compares these predictions with the actual consequences as perceived. This on-line comparison allows the system to make adjustments as the agent moves (see the concepts of forward models and comparators; Wolpert and Ghahramani 2000). Such processing is a control computation executed by the motor system, but this subpersonal phenomenon, a feature of part of the agent, realizes an agentive automaticity in movement. Granted in ordinary speech, we say the tennis player exhibited exquisite control in making sudden adjustments, but this is speaking nontechnically. We could just as well say that the tennis player exhibited exquisite skill or exquisite spontaneity (automaticity). If there is control here, used in a technical sense, it is motor control that realizes agentive automaticity, a subject-level skill exercised exquisitely. The rapid response to perturbation is something the agent need not think about, intend to do, precisely because she is skilled. What this means is that we have (at least) two distinct conceptions of control in cognitive science, one in motor control tied to forward models and comparators for which there is no correlated notion of automaticity, and one in philosophical psychology regarding agentive control and its contrast, automaticity. Irrespective of terminology, Frankfurt leaves out an adequate account of guidance in the sense I will explicate as attention (see related issues regarding causal deviance; Section 2.10). To explain his notion of guidance, Frankfurt discusses a driver who lets his car coast down the hill. The driver guides his action in the Frankfurtian sense even though he does not move his body because the agent would respond appropriately were obstacles to suddenly appear. Yet what is missing is an account of guidance tied to the empirical sense of that notion. The agent is guiding his action in that his response is continually informed by how he is actually taking things: his perception of the speed, direction, and the absence of obstacles on the road. He attends to all these features to inform his response, here keeping his hands lightly pressed on the steering wheel because the parameters he takes in are appropriate to simply coasting. His guidance, as I would say, is not in his being watchful for possible obstacles (cf. vigilance; Section 3.6), but in actually watching how the coasting unfolds. The agent’s taking things actively informs his maintaining his current bodily state during coasting. Guidance is through attention.

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1.10 The Agent’s Being Active The mental elements of the agent’s action identify the agent’s being active though short of acting, for her being active partly constitutes her acting. Critics of the standard story of action argue that it makes the agent disappear. If one’s theory fails to recover action, then the agent is not present. Davidson noted that mental states, not being occurrences, cannot play the right causal role in explaining action, namely as efficient cause, so he appealed to events such as the onslaught of a desire (Davidson 1980a, 12). Helen Steward (2012b, 2016) and Jennifer Hornsby (2004) have argued against the centrality of events in the ontology of action (cf. Steward’s emphasis on processes; see also Stout 1996). To capture action’s constituent structure, the constituents have to be more than static states and mere happenings. This more cannot, however, itself be an action, on pain of regress or circularity. When an intentional action occurs, each of the constituents of the structure are put in motion. I am inclined to treat the expression of action-relevant capacities, say perceptual capacities, as activities of the agent that are not themselves actions. The metaphysics of activity and of processes have been actively discussed in the philosophical literature, so to be clear, my goal is not to build on that work. Rather, I aim to examine the relevant idea of activity biologically to gain a different perspective on action and its constituents. What I draw from the discussion of the metaphysics of action is that states being static are not sufficient to recover the dynamics of agency, and events, construed as concrete particulars (Davidson 1970) or as facts (Kim 1993), also do not evince the requisite dynamics (see Steward 1997 and Bennett 1988 on events). The alternative to these categories is the idea of a process in which an agent partakes (Steward 2012b). My approach is also to take up a third way between events and states, threading the needle by drawing on biology, broadly construed. Action on my account is the amalgam of the active component parts. Conceptually, we began unpacking this amalgam via control (Sections 1.5–1.8). Another part, guidance, will be explicated in Chapter 2 as attention. In the former case, control was grounded biologically in cognitive integration. Accordingly, in the case of intention, amalgamation involves integration, a dynamic process that must modulate action over time. The agent’s intending to act shifts processing to allow for a type of selectivity in how the agent takes things where this integration must be as dynamic as action requires. Chapters 3 and 4 further explore intention’s activity. Let us make an initial start on guidance rooted in the agent’s perceptual taking, say her perceiving the targets of action. The traditional paradigms in philosophy of action are perceptually guided movements. In explicating these, the standard theory appeals to beliefs and desires but is silent on perception. When philosophers speak of an agent drinking a glass of gin (Williams 1981), they

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mention only a desire to drink gin and a belief that drinking that gin will satisfy the desire. So, the agent reaches for the gin. Yet for all that the standard story says, the agent could be doing so with her eyes closed. The complicated but essential phenomenon of perceptual guidance is left out. The agent’s reaching for a glass that she sees involves her seeing it guiding her reach, providing essential spatial content. Her seeing is not a static state for its content, hence its guiding role, changes as the agent moves. The agent’s seeing is not aptly theorized as an event for the issue is not that the experience happens at a place and time, every one of its features fixed to individuate a spatiotemporal particular whose temporal boundaries must be set to understand its causal role. Appeal to events would not illuminate the temporal dynamics of action, for seeing plays a temporally extended guiding role, providing new information to the subject as she completes her intended action. Seeing is active in action. In guiding action, seeing is the agent’s activity of attending (see figures in Section 2.1). That is, the agent’s taking things provides a constant input to inform response. This continual guidance is what I mean to capture by talk of activity. In any event, this section announces a thesis to be unpacked in the next three chapters with emphasis on a biological perspective. The larger question of how to stitch together the biological conception of being active to the metaphysical conception of activity must be left for another time.

1.11 Taking Stock An agent’s acting intentionally has a psychological structure: it is the agent’s responding, guided by her taking things, given her intending to act. The necessity of this structure derives from the contrast between action and reflex and points to the Selection Problem necessarily faced by all agents. Paradigmatically, the agent solves this Problem by acting with an intention, and the action capacities exercised in intentional action are cognitively integrated with the agent’s intending. Control in the agent’s intending to act leads to defining the division between automaticity and control. Agents also guide, and in the next chapter, I explicate guidance in action as the subject’s attention.

Appendix 1.1 A Reflection: Automaticity and Control in Parallel Distributed Processing In cognitive science, a standard paradigm to probe automaticity is the Stroop task: color words are presented in different colors and subjects are tasked with reporting the font color. In the congruent condition, the color word matches the font color, say the word “red”

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printed in red. In the incongruent condition, the color word does not match the font color, so “red” printed in green. Reporting the color of the word is harder in the incongruent condition. A common explanation is that word reading is automatic and must be suppressed to enable naming in the incongruent condition. While one is tasked to report the color the word is printed in, one is strongly inclined to read the printed word. In my terminology, the controlled color-naming action is interfered with by the automaticity of word processing. Jonathan Cohen and co-workers constructed a parallel distributed processing (PDP) network to model Stroop behavior (Cohen, Dunbar, and McClelland 1990). Their network involves nodes constituting the input layer, specifically nodes responding to either colors or to color words, and nodes corresponding to an output layer linked to responses, specifically nodes corresponding to naming colors/words. In their initial implementation, Cohen et al. picked two color word inputs, “red” and “green”, and the two corresponding color inputs (thus, four total input nodes for color words and the colors thereby named). Outputs were production of the words (utterances of “red” or “green”). A single intermediate, hidden, layer was also part of the network. Weights assigned to connections between nodes identify the strength of a given pathway, a bias, and determine speed and performance by the network. Finally, a node representing task was connected to intermediate layers. Notice that this is a computational reflection of the Selection Problem for this version of the Stroop task. Cohen et al. used this network to model the behavioral results observed in standard Stroop tasks. That is, the performance of their network under analogous task conditions in human experiments yielded similar performance. They note: “two processes that use qualitatively identical mechanisms and differ only in their strength can exhibit differences in speed of processing and a pattern of interference effects that make the processes look as though one is automatic and the other is controlled” (334). This poses a challenge to the standard categorization of a process as automatic or controlled, based on the following common inference: For two processes, A and C, “if A is faster than C, and if A interferes with C but C does not interfere with A, then A is automatic and C is controlled” (333). The rule, however, can show that A is automatic in one context relative to C as controlled and that A is controlled in another context where C is automatic. This echoes our paradox about categorizing processes as either automatic or controlled. Indeed, later, they give the following description which fits the perspective argued for in this chapter nicely: given the task of naming the color [that a word is printed in], a person can exercise control by responding “red” to such a stimulus. In the model, this is achieved by activating the color-naming unit in the task layer of the network. This unit sends additional biasing activity to the intermediate units in the color pathway, so that they are more responsive to their inputs. In this way, the model can selectively “attend” to the color dimension and respond accordingly. It is worth emphasizing that the increase in responsivity of the intermediate units is achieved simply by the additional top–down input provided by the task unit . . . it does not require any special or qualitatively distinct apparatus [cf. a spotlight of attention]. The key observation is that attention, and corresponding control of behavior, emerges from the activation of a

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Figure A.1 This figure depicts a connectionist network modeling processing during the Stroop task described in the text. Two types of inputs are possible, one concerning actual colors (red and green), and another concerning color words (“red” and “green”). Outputs are verbal reports expressing “red” and “green” which, depending on the task, can be repetition of the input word or report of the word color. Connections between nodes are assigned weights, in this case, a stronger weight (darker lines) from word input to output. Task representations provide a bias that increases the strength of the appropriate connections. In the standard Stroop task, reporting color is the task representation that must increase the weight of the weaker input color to report connections (left side connections) in order to overcome the strong prepotent weighting linking word to report (right side connections). This figure is redrawn from a figure in Matthew M. Botvinick and Jonathan D. Cohen. 2014. “The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.” Cognitive Science 38 (6): 1249–85. representation of the task to be performed and its influence on units that implement the set of possible mappings from stimuli to responses. (1254; cf. Desimone and Duncan 1995, 194, quoted in Section 2.4) Readers might come back to this quote after reading Section 2.4. See also discussion of the antisaccade task in Section 3.6. Cohen et al. deploy a representation of the task to bias processing. In the geometry of action described in Chapter 1, this task representation corresponds functionally to intention where biasing sets selective processing for task, what I shall argue is attention. They note: “The role of attention in the model is to select one of two competing processes on the basis of the task instructions. For this to occur, one of two task demand specifications must be provided as input to the model: ‘respond to color’ or ‘respond to word’ ” (338). Since attention is not a cause in the sense attributed (Section 2.4), replace “attention” with

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“intention” and “to select” with “to bias” in their quote, and we have the correct answer (see also Miller and Cohen 2001). Automaticity is tied to control via the Simple Connection. The theory of psychological control, as tied to discussion of executive function, is broader than, though includes, the form of control at issue in this book: acting as one intends. For example, an important element in the study of executive control in cognitive science is probed through task switching paradigms. These paradigms require subjects to shift which intention is active in bringing about action (Monsell 2003). Shifting plans is an important part of ordinary agency but task switching presupposes the ability to act as one intended, the basic phenomenon which we are trying to understand. Accordingly, I acknowledge other ways we might theorize about control in agency, but those conversations will be predicated on assuming that the agent can act in the basic sense, say perform a task. This book focuses on clarifying that basic sense. Further layers of control on intention build on basic control. To capture higher-order control, we would have to add to the PDP model in Figure A.1 additional nodes that regulate the task structures, say higher-order goal representations. Consider the Wisconsin Card Sorting Test where subjects have to infer a rule used to sort cards and then, when the experimenter changes the rules without warning or stating the new rule, the subjects have to recognize the change and infer the new rule. This involves inference, monitoring of current behavior, and task switching. The full theory of human agency will have to incorporate actions of this kind and integrate them in the overall theory of control, but these are more complicated cases again built on the basic ability to act with an intention. A conceptual point does arise, for I have defined control relative to intention, so for the actions noted to come out as part of agentive control, there must be a corresponding intention. This is plausible for many cases. In many task-switching paradigms, subjects are told that there will be a task switch in the experiment where the switch is either cued or must be inferred. In this context, it is clear that the agent forms a plan to be receptive to the cue or to recognize signs that the task is switched, say through feedback on performance. Here, an intention regulates task switching. That said, task switching can be automatic. Notably, in cases of expertise, an agent can respond to environmental conditions by delaying one task and switching to another because she knows how best to respond in certain unstable conditions. There need not be an intention to switch, just the expression of the agent’s expertise acquired through learning (Chapters 5 and 6).

Notes 1. Disambiguating “Reflex”: The idea of a pure reflex is a useful fiction. No actual reflexes are pure since all actual reflexes can fail. A pure reflex is an idealization at the engineer’s limit that distills the contrast with action. Hence, pure reflex is a term of art. It is no objection to the first premise to note that there are reflexes that can fail as these are not pure (see my 2018 response to Jennings and Nanay 2016). Note that automaticity is not the basis of the contrast with action even if standard reflexes are, by definition (Section 1.4), fully automatic, hence passive. But actions can also be automatic.

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Often, when we talk loosely about acting on reflex, we really mean acting automatically. I use pure reflex and automatic as technical terms. 2. The Simple Connection and System 1 / System 2 thinking: The paradox undercuts a common way of dividing mental processes, often dubbed System 1 and System 2 thinking (Evans and Stanovich 2013). Paul Boghossian (2014) quotes Daniel Kahneman’s characterization: System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration. (Kahneman 2011, 20–1) Kahneman draws on the Simple Connection. Boghossian suggests that to capture normal cases of inferring, we need something in between, what he calls System 1.5 thinking: “It resembles System 2 thinking in that it is a person-level, conscious, voluntary mental action; it resembles System 1 in that it is quick, relatively automatic and not particularly demanding on the resources of attention” (3). This is just to affirm (1) and (2) in our triad for the mental action of inferring. Reasoning and inference, as intentional actions, exemplify control and automaticity. Thus, the paradox arises for System 1 and System 2 accounts of reasoning. Chapter 6 discusses inference as mental action, specifically as the deployment of attention. 3. Four Objections to the Analysis: Ellen Fridland (Fridland 2017) raises four questions regarding this account. The first is this: How does one motivate choosing intention as the basis for analyzing control rather than consciousness or any of the other features typically connected with control? My answer is that intention’s link to control is fixed by the first proposition: intentional action exemplifies the agent’s control. The paradox and its solution motivate my analysis. Second, Fridland argues that what I intend to do can be automatic, yet on my view, the action must be controlled. This generates a contradiction. Fridland considers a pianist imagining playing a piece in a particular way, so intending to play just like that (as she imagines). On my view, playing like that is controlled. Fridland asserts, however, that the expert plays just like that automatically. Applying the Simple Connection yields a contradiction: playing like that is both automatic and controlled, yet on my view, this cannot be. The argument, however, equivocates on that. The first sense in which the agent plays like that is that the agent plays as imagined in her intention. Yet the concrete action that is her playing like that (second sense) has specific parameters that she did not imagine. It is a concrete phenomenon with many features the agent did not intend. So that way she actually played (second sense), the performance we observe, is an instance of that (type) of way that she intended (first sense). In the argument, “that” has different referents: that way I intend to play, a type of action even if highly specified, and that way I actually play, a concrete instance of the type intended. Even in the most specific intentions of finite minds, the actual action that is performed must always be at a finer (determinate) grain. We

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cannot, after all, imagine every parameter of an action, so even in Fridland’s case, the way the pianist imagines playing (playing like that) can be multiply instantiated. Third, Fridland notes that some intentions are automatic. I agree. Indeed, forming an intention, like forming a belief, is an automatic aspect of an action of settling the mind, the achievement of intentional reasoning. Acknowledging this, however, does not yield explanatory circularity or contradiction, and Fridland does not demonstrate that it does. Finally she notes that “being uncontrolled or uncontrollable is hardly a universal property of automatic processes” (4345). I agree, and again, intentional action illustrates the point. The objection, however, also changes the subject since my analysis explicitly denies that we should divide processes as automatic or controlled. Many processes that have automatic features are also controlled, like skilled action. Indeed, Fridland and I, who are allied on many matters regarding skill, agree on this last point, so that’s a good place to stop. 4. Alief as affective bias: Consider Tamar Gendler’s concept of alief as an explanation of emotion- or affect-driven actions (Gendler 2008b, 2008a). Gendler characterizes aliefs as follows: A paradigmatic alief is a mental state with associatively linked content that is representational, affective and behavioral, and that is activated—consciously or nonconsciously—by features of the subject’s internal or ambient environment. Aliefs may be either occurrent [activated] or dispositional (642) . . . activated alief has three sorts of components: (a) the representation of some object or concept or situation or circumstance, perhaps propositionally, perhaps nonpropositionally, perhaps conceptually, perhaps nonconceptually; (b) the experience of some affective or emotional state; (c) the readying of some motor routine. (643) We can subsume Gendler’s proposal in the structure of action: there is an input state, Gendler’s (a), an output state, Gendler’s (c), and a source of bias, Gendler’s (b). When Gendler speaks of alief as occurrent or activated, this must be activation that is short of action, for Gendler intends alief to explain action. The activation of alief must then be a readiness to act. In the occurrent case, what we have is an emotional state that readies attention and response but does not yet yield coupling (action). Affect biases the action space and on the input side, it establishes a propensity to attend: vigilance (Sections 2.1 and 3.5). Accordingly, aliefs can be understood as a type of arational action through a shared action structure (Figure 1.7). For another conative source that biases action, see also desires in the directed-attention sense (Scanlon 1998, ch. 1; for a different approach in the spirit Gendler’s account, see Brownstein and Madva 2012b, 2012a). 5. A debate about bias and translation in integration: Dan Burnston (2017a) has criticized an earlier presentation of this approach (Wu 2013b). Burnston also appeals to the notion of biasing but as he conceives of it, biasing does not involve transferring content. Rather, cognition changes the probabilities that certain perceptual processes rather than others will be initiated and maintained (see also Burnston 2021; cf. Wu 2008, 1010–11). He reasons that the content of an intention is too general relative to the computational processes it is said to penetrate. So, an intention to grab a cup leaves

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unspecified the movement’s specific parameters (Burnston 2017b). Some related issues have been discussed in terms of the interface problem, roughly how intention engages the correct, finely tuned output capacities (Butterfill and Sinigaglia 2014). Since concepts are not sufficiently fine-grained to specify the precise motor movement parameters instantiated, something else must determine these movements in accordance to the intention. Accordingly, the bias by intention potentiates not a single movement capacity but a set of relevant capacities. The resulting parameters of movement will be automatic, speaking technically. Still, in potentiating a set of motor representations, we can treat this as the motor system operating over the content of the intention by translation (Wu 2013b). To anthropomorphize, the way for the motor system to deal with the content of the intention is by activating the set of specific movement capacities the execution of which would satisfy the intention. As we noted (Section 1.4), there are many such movements, these being necessarily automatic. Given the architecture of the motor system, we can think of this set, for heuristic purposes, as a disjunction of movements within the vocabulary of the motor system such that where the intention speaks of a movement type X, the motor system treats this as concrete movements it can generate: X1, X2, X3 . . . or Xn (the reader can insert their favorite format for motor representations of movement possibilities). We can treat these movements as the motor’s system expression in its representational scheme of the intention’s content. It is in that narrow sense a translation though there need be no mechanism of translation, only an association, likely established by learning and coactivation of conceptual and motor representations during practice, that links conceptual and motor representations. Subsequent machinery needed to generate movement operates over this content to settle on one motor type (cf. Wu 2008 p. 1010ff). This satisfies the condition on cognitive integration. 6. Hornsby on directness and basicness: Jennifer Hornsby’s discusses similar ideas that she ties to an idea of basic action. In her (2013), she discusses intentional activity when we do things “just like that”: Practical reasoning terminates in things that one needs no knowledge of the means to do. And that takes us back to basics. The knowledge a person has at a particular time equips her to be doing whatever she is doing then. So at any time she must be doing something she can then be doing without recourse to further knowledge something she can then be doing directly, ‘just like that’. Thus on-going (intentional) activity will always be of some basic type. (16) In the text, I speak of direct intentions that represent actions that one can directly do and, in Chapter 4, I will discuss intending, one’s thinking in time with one’s action, which I think connects with another idea of Hornsby’s that the agent acting “is at every stage a thinking agent” (16). On actions as activity, see also Hornsby (2012). Helen Steward’s work has also been influential (on actions, activity, and processes; see especially Steward 2012b). I should note that when I speak of being active in action, in Section 1.10, I do not describe an agent’s acting as Hornsby does but rather, for example, describe an aspect of the agent’s taking things when she acts where this taking guides her response. Such guidance is not itself an action. Indeed, it is attention, a necessary component of action (Chapter 2).

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7. Learning through expansion: Consider a case discussed by Katherine Hawley (2003): one way to escape an avalanche (apparently) is to make swimming motions. Thus, a person who knows how to swim knows a way to escape avalanches. Yet she might not be able to do this directly in simply intending to escape the avalanche as snow washes over her. If she were instructed by a knowledgeable friend beside her right before both are swept away that swimming motions are effective in escaping an avalanche, she can intentionally escape by intending to make swimming motions and acting in that way. This is a case where learning to act does not involve breaking the targeted action down into subactions but in recognizing that one action F can be done by doing G where the latter is a way of doing the former. Still, this is a case where I can F directly only by learning that G is a way of F-ing where I can, fortunately, directly G when I intend to. So, learning again fills a gap. I am especially grateful to Katherine Hawley for generously taking the time in the autumn of 2020 to discuss with me issues where our work intersected.

2 Attention and Attending 2.1 Introduction There are three salient modes of attention: vigilance, attentional guidance, and attending as action. A detective has received a tip that a thief well-known to him plans to rob a jewelry store. The detective leaves intending to catch her in the act. As he surveys the possible scene of the crime, we depict his action space focusing on the visual domain, identifying relevant visual capacities exercised in response to each visible stimulus that he sees. For each, we assign inputs tied to his visually taking in (seeing) the stimulus, a potential target for action though most are irrelevant to his intention (cf. visual and cognitive fields of input; Section 5.7). The detective is primed to recognize the thief. As she has not yet appeared, the detective is vigilant for her appearance. Vigilance is a readiness to attend to the intended target (Section 3.5), a type of attentional attunement understood as a general propensity to attend (Wu forthcoming). Hence, before the intended arrest (hopefully) occurs, recognitional capacities that can contribute to attention are activated (biased) in vigilance though not yet exercised. Vigilance marks a shift in the potential for attention (cf. Section 5.8 on attentional character), hence potential for action, prioritizing a potential target that is not actually attended because it is not yet present. Attention engages when the thief appears. While waiting, the detective visually searches the scene, looking for the thief, an action of attending (searching for) involving his looking at various people until the target is found. As the detective walks around, he is guided by selective attention, as psychologists would say, that fixes a path and helps him avoid obstacles. His vigilance pays off as the thief captures his attention, sitting surreptitiously at a café table, large sunglasses and a hat. She gets up and the detective follows her by attending to her, the detective’s vigilance for the thief now transformed into actual attention. Simultaneously, a new form of vigilance is established for an act of larceny. I suspect the thief knows he’s there. We leave the drama to map the story of attention onto the structure of action in three modes: vigilance, attention, and attending. Of these, attention has explanatory priority, but start with the most complex of the phenomena, attending

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0003

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as an action, say looking for something. Looking is conceptualized as overt attention, a direction of the eye toward visual targets. Attending as action involves a coupling of input to output, here a visual experience of a target to an eye movement. The agent moves his eyes toward a target guided by his visual experience of it. He looks. The dynamics of looking were vividly demonstrated in Yarbus’ experiment (Section 1.6). In action space, attending is constituted by a coupling of input and output, so I mark it by circling the coupling, the action of looking:

Figure 2.1 This figure depicts the action of overt visual attending based on an explicit intention to look at a target (S1). The dotted oval identifies the intentional action that has three components: (1) the intention to act, (2) the visual experience of the target, and (3) the movement of the eye to it. The coupling arrow refers to the experience’s guiding the movement. This is a dynamic process. Attending as an action has the structure shared by all actions (Chapter 1).

At the same time, every action involves an input’s guiding response. This guidance is an input-based activity, not an action but a constituent of it. Guidance, better guiding, is a process. So, attention in action is the input’s contribution in guiding a response. Crudely, we mark it by circling the input and the arrow that points to but excludes the output. The arrow emphasizes that this is an ongoing process of guidance that contributes to but does not include response. For example, the input can be a visual experience which provides spatial content to the generation of a reaching movement or to be encoded in memory. The visual experience contributes to but is distinct from these responses.¹

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Figure 2.2 This figure focuses on the visual experience (input) guiding the response but not including the response. The intention has been removed to aid emphasis. Attention occurs when an input, here the subject’s seeing S1, informs the response. Hence, attention includes not just the experience but the arrow that refers to the experience’s guiding. Accordingly, the dotted oval for attention includes input and arrow. Attention, understood technically, is not an action but part of one, in this case, the action identified in Figure 2.1.

The visual experience counts as guiding only if there is something guided. The functional roles of the guide and the guided are distinct. Attention in the sense of that which is guiding response is a component of action, and is present in every action. Finally, given available inputs, some are prioritized before action begins and, indeed, before the stimulus appears, as when the detective is vigilant, ready to attend to the thief not yet seen. To emphasize this preparation to attend, we circle that input action-relevant capacity not yet exercised because the stimulus has not yet appeared. That capacity is prioritized in advance of its serving to guide response. It is biased, but there is as yet no guidance, no perceptual attention, for there is no stimulus to perceive and respond to. Some memory theorist might treat this active orientation as a form of working memory, a holding of a target in mind (see Section 3.5). Figure 2.3 diagrammatically emphasizes that the agent is ready to perceptually guide when the stimulus appears. Possibly, the agent might react more quickly to S1 over S2.² All three attentional phenomena are explicated in relation to the Selection Problem. To track the subtle distinction between attentional guidance in action and attending as action, I refer to the former, reluctantly, as attention and the latter as attending. The term attention is unfortunate since it suggests a state

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Figure 2.3 This figure represents the subject’s vigilance for the target that has not yet appeared. The intention engenders a readiness to attend to a target. The darker circle for the input capacity that would respond to S1 were it to appear indicates that this capacity is active while the capacity for responding to S2 is not. The subject is ready to attend to S1 in a way that they are not with respect to S2. When S1 appears, the subject responds to it (see Figures 2.1 and 2.2). See Section 3.5 for a full discussion of vigilance.

whereas guiding is a process, so I would prefer to refer to it as attending as well, thus courting ambiguity. What we have are attending-in-action (henceforth: attention) and attending-as-action (e.g. looking). Since we are explaining action in general, attention is prior in the order of explanation relative to attending, a type of action. As attention is present in all actions as the basis of guidance, this chapter explains what attention is. That said, I have found that even with the diagrams, my conception of attention is not always clear to readers—or perhaps the diagrams are less helpful than I think. So, let me return to the engineer of Section 1.2 (see also Appendix 1.1 on Cohen et al.’s connectionist model of the Stroop effect as the frame of the circuit I will now describe). Let our engineer design a robot to navigate an environment. The engineer builds a circuit that involves two optical sensors, one to detect gold nuggets, the other to detect silver nuggets. These sensors are connected to a motor system that moves the robot toward the items of interest. Let us focus on one aspect of that motor system, namely a camera that gives the robot a better view of the target. So, the circuit in question is the sensor-to-camera, namely two sensors to one camera control module. To control this circuit, the

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engineer builds in a memory system so that the robot’s tasks for the day can be encoded, say to look for gold or to look for silver today. This system serves as the robot’s “intentions,” the maintenance of its plans (see Chapters 3 and 4). When the plan to collect gold is active, this activates the gold sensor and deactivates the silver sensor, this pattern being reversed when the plan to collect silver is active. We map the three attentional phenomena onto this circuit by analogy (I am not claiming the robot attends). With the sensor for gold activated but before “seeing” any gold, the robot is primed to respond to gold. This is the functional analog of vigilance. When gold is spotted, the sensor now goes into a highly active state which we can conceive of as the robot’s “visual experience” of gold, its “seeing” it. Here’s the crucial bit: when the sensor, “the seeing,” starts to send a signal through the wiring to the camera, take this as the process of the sensor-sending-signal-toprogram-camera-movement. This is the analog of attention. It is the sensor’s activity of guiding. Thus, attention is more than just the sensor being active. It is more than the “visual experience.” It is the visual experience’s playing a functional role of guiding. Finally, consider the whole process of the sensor-guiding-camera movement. This is the equivalent of attending, basically the robot’s “overt visual attention.” Here, we have an “action,” a bit of robotic behavior. The crucial idea, and perhaps the most difficult to discern, is the structure of attention. In us, it starts with analytically isolating our visual experience, our seeing. This is not sufficient for attention. Rather, it is only when seeing is guiding that we have attention. As I shall argue, every action involves attention though not every action is an attending. One last thing before we get to the arguments: my theory of attention is economical. Notice that the engineer built three systems in the robotic agent: (1) an input system, here an optical “visual” system, (2) an output system, here a system of movement of camera and robotic limbs, and (3) a memory system to store plans, effectively intention (cf. Appendix 1.1 on a similar connectionist architecture). Each is an action-relevant capacity. Crucially, the engineer did not build a fourth system, an attention system. As I shall argue, attention comes for free when we have an agent capable of solving a Selection Problem. Talk of attention is just a way of describing the agent’s selectivity on the input side when the input is guiding the response, here the robot’s visual sensory attunement to gold that guides camera movement. The theory of attention I advocate for is in that sense economical. Attention supervenes on agency, namely on solutions to Selection Problems. It does not exist without agency (Section 2.4).

2.2 Merging the Psychology and Philosophy of Attention Attention is mental guidance in action, the agent’s taking things informing response.

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What is attention? Answers to this have typically drawn on folk and empirical psychology, but we must approach these sources with care. Folk-psychological claims provide substantive constraints only if they are evidentially well grounded. Our interest is not in how the folk think about the causal structure of action but about the structure itself. Unfortunately, subjects’ ordinary access to mental processes through introspection is often unreliable (Chapter 7). I draw only on folk claims for which we have good reason to take as accurate. One should not assume that they are. We, the folk, do reliably track attentional phenomena because we have a distinctive form of access to our intentional agency. We can know about our intentional actions without observation (Anscombe 1957). We can intentionally attend to something to act on it, and we can accordingly know what we “pay attention” to, that is, what we are acting on. Privileged access provides an initial justification for affirming William James’ claim that we know what attention is: Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization, concentration, of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others. (James 1890, 403)

The distinctive awareness we have in acting intentionally need not just access the action of attending but can also access action’s source in attention. We can know that we are intentionally scrutinizing the painting given that we see and attend to it. The detective tracking the thief knows who he is attending to and keeping his eye on. This claim does not flow from intuition but from the agent’s distinctive access to intentional action through intention. Accessing action in this way is an action we can explain (Section 4.6). The folk, in this specific case, are reliably accurate. James tied attention to action in the agent’s withdrawal from some things to deal effectively with others. This selective dealing with draws on the mind’s taking possession of one out of several targets. Attention is a selective mental orientation for the sake of guiding action. Drawing on your distinctive access to action, say your intentionally reading this text right now, you can become aware that attention is directed to just this text in order to read it, rather than other objects or trains of thought. You need not observe yourself reading to know this. We can be distinctively aware of our mental attunement toward a target. James’ characterization provides the starting point for reflection on attention in intentional action. On this narrow basis, folk psychology sets the stage for empirical psychology.³ The folk observe attention in the wild, say overt attention in the movement of the eye. Consider when a person is not looking at you during a conversation, a sign that they are not paying attention to you. This happens all the time at parties or

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conferences when your interlocutor seems to be looking for someone else (e.g. someone more important; Section 5.6). You can test this hypothesis: : You’re not listening to me! : (Looking back at you) Yes, I am! : What did I say then? : Um . . . Folk intuitions regarding divided attention or distraction are grounded in our observation of recognizable behavior in others and are validated by empirical work. Accordingly, I do not deny the relevance of some folk judgments about mental activity. What I question are folk claims that are not grounded in firstperson knowledge of intentional action or reliable third-person observation of action yet are offered as a constraint on theorizing about mental processes. Folk claims must be validated. We must also proceed carefully with scientific results. Our concern is the nature of attention. The question of underlying causal mechanisms is one that philosophers have properly ceded to science, but the question of what attention is is not to be dissected by experimental inquiry which focuses on how attention is implemented (realized) in biological substrates. The what-is question is a metaphysical question. The answers to it are not to be simply read off from empirical data. Indeed, in analyzing experimental data on attention, scientists often assume some answer to the metaphysical question. I shall argue that they actually assume James’ answer, hence our—the folk’s—answer. To explain what attention in action is, I draw on David Marr’s theory of explanation for psychological phenomena (Marr 1982, ch. 1). Marr outlined a schema for answering questions of the form “What is X?” An answer is structured by three levels: (a) the computational level that specifies what the phenomenon is for, its function and logic; (b) the algorithmic level that specifies the relevant representations and algorithms for the function, (c) the implementation level that specifies the hardware that realizes the algorithms, say specific circuits in the brain. It is at the computational level that philosophical inquiry most directly engages. James’ characterization provides a computational theory of attention. It links attention to action: attention is a subject’s mentally selecting an object or train of thought rather than others in order to deal effectively with it. Attention is selection for action. This computational account constrains the other explanatory levels, for it is encoded in the central methodological assumption of experimental paradigms

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for attention. To study attention to X, scientists must ensure that subjects are paying attention to X during data collection. Experimenters design tasks such that to perform these correctly, subjects must attend to X. Specifically, the subject must select X to guide response and, in doing so, pays attention to X. Such selection for task performance is the phenomenon James described, as implemented in the lab. The scientist can verify attention by measuring the subject’s task performance, the taking possession of X to deal effectively with it. Experimental strategy assumes an empirical sufficient condition for attention: if a subject S (mentally) selects X in guiding performance of task T, then the subject attends to X. This provides a response to those who aver that we do not know what attention is (see Section 2.4). Let us treat the empirical sufficient condition as derived from a consequence of James’ definition: if S selects X to guide action, then S attends to X. Call this the Jamesian Condition on Attention. It suffices to identify the phenomena studied by the science of attention. We answer empirical skepticism about attention in a deductive argument that fixes an empirical notion of selective attention: 1. If attentional paradigm X is valid, then so is the Jamesian Condition. 2. Attentional paradigm X is valid. 3. So is the Jamesian Condition. Take any task paradigm X, results from which you treat as relevant to understanding attention, so affirming (2). In cognitive science, this form is typically referred to as “top-down” attention (I shall deal with automatic attention in Sections 2.6 and 2.7). Now for conditional proof regarding (1), assume that paradigm X is valid as a probe of attention. Any such paradigm will involve performance of a task where instructions specify a target to which the subject must respond in a particular way. Measurements are taken of task performance that depends on selection of the target, usually perceptual selection but often memory. The construction of these tasks is not arbitrary. A subject who fails to attend ruins the experiment. To study attention, experimenters must control how subjects attend. The Methodological Setting of Attention: Define a task such that proper performance has as a necessary condition the subject’s (mental) selection of a defined target to act on in a measurable way.

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When the subject selects that target to perform the task, the subject is attending to the target. This is verified via measurement. Since the task is an experimental construction, it is not that the link to attention conceptually flows from that specific task. The task’s validity is not due to a conceptual link between that task and attention. Instances of the empirical sufficient condition are not conceptual truths. Rather, the validity of any given experimental task as tied to attention is justified by the folk conception of attention as the mind’s taking possession of a target to deal with (act on). The commonality across all tasks, extant and new ones, is that they are constructed under the methodological setting of attention as selection which guides appropriate response. Validation of specific tasks as probing attention draws on the general perspective expressed by the Jamesian Condition. Hence, (1) (cf. my earlier attempt to establish (1) in Wu 2014, 84ff). Recall the distinction between attention as input and attending as action. What is mainly at issue in cognitive science is attention, namely the selectivity that guides behavior. This need not be the act of attending. Thus, in tracking eye movements, scientists often are not in the first instance interested in the coupling of neural processing to eye movement, but in the selective neural processing itself (this depends on the area of the brain being investigated). Accordingly, they might focus on imaging data restricted to the cortical visual system, the neural basis of visual inputs in action space (recall the work of Chelazzi et al.; Section 1.6). Their assumption is that the observed activity is causally implicated in the guidance of behavior, a hypothesis that must be separately tested. Since task performance in most experiments is an intentional activity, scientists are probing selective guidance, a task-relevant input informing response, say selective visual processing. This structure is imposed by task instructions that subjects intend to follow. The empirical sufficient condition is an instance of James’ account of attention which is derived from folk knowledge of intentional action. This is a good result: the science of attention, given its methodology, is directly tackling attention as we the folk know it. The Jamesian Condition suffices for my aims, but I note the bold hypothesis and equate selection of X for action and attention to X so as to provide a computational account of attention. I shall dub this the Jamesian account. It was developed in psychology by Alan Allport and Odmar Neumann (Allport 1987; Neumann 1987). I consider in Section 2.9 worries about action as necessary for attention, but for now, it is a strong argument for the selection for action account that it informs the experimental science of attention through the empirical, hence Jamesian, sufficient condition. Scientists design an experimentally tractable analog of selectively dealing with things, and the resulting data is used to theorize about the nature of attentional guidance, including its implementation in neural activity (Section 1.6). The extant work in cognitive science, anchored on the empirical sufficient condition, can then be organized by it into a unified answer to the

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question “What is attention?” (Wu 2014, chs. 1–2; see also Wu forthcoming a). The Jamesian answer is roughly this: For all targets X, subjects S, and actions A: S attends to X = subject S mentally selects X in guiding action A. For our purposes, mental selection can be explicated in a task (action), so visual attention in search tasks (Section 2.1), or auditory attention in dichotic listening (Section 2.5), or mnemonic (cognitive) attention in matching to sample or recalling symbol sequences (Sections 1.6, 3.2–3.3). Attention, especially visual attention, is well understood precisely because we have many details that can be unified in a Marrian explanation. In the Selection Problem, selection expresses James’ idea that the mind selectively guides one’s dealings. As selection is tied to guidance (Wu 2016), a better gloss is this: Attention is the subject’s mental guidance of response. In a slogan: attention is guidance in action. When an input, the subject’s taking X rather than other targets, guides response, the mind thereby takes possession of X rather than others in order to deal with it. Guidance, selection, and coupling are explicated within the structure of action. We thus merge Jamesian and empirical conceptions of attention with the structure. That is, we link the psychological theory of attention to the metaphysical theory of action yielding a metaphysical theory of attention in action. The confluence of well-grounded folk psychology, empirical psychology, neuroscience, and the philosophy of action provides strong grounds for treating selection for action as the right theory of attention (see Wu forthcoming a). Here, as with control and automaticity, I would urge readers to take up the theory. There is a tendency in invocations of attention by philosophers to acknowledge different philosophical theories but then assume neutrality or draw liberally from folk psychology. This hinders investigation by cutting the theorist off from detailed work on attention in cognitive science writ large. I have given a deductive argument that enjoins those who take the science of attention seriously to accept one direction of James’ account, the sufficient condition that will suffice for understanding attention’s role in all actions of empirical and philosophical interest. Even if one disputes the conditional premise of my argument and wants to stay neutral on the metaphysics of attention, the common structure of the standard paradigms in the science of attention that conforms to the Jamesian idea should provide a basic anchor. The Jamesian Condition and its empirical echoes are common and clear. There is no reason to ignore them.

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2.3 Attention and the Selection Problem Attention as guidance, a necessary part of a solution to the Selection Problem, is present in every action. The mind takes possession of things to deal effectively with them in solving the Selection Problem. Attention emerges in such coupling where input informs response. For perceptually guided actions, the relevant input is the subject’s perceptually taking things to be a certain way, say her visually experiencing that an object has a certain shape, color, or location. In light of how she perceives things, the subject responds. Her perceiving guides her response. Guidance is a process tied to attention: Attention is mental guidance in solving the Selection Problem. More precisely, where M(X) refers to the relevant M(ental)-input (e.g. a perceptual or memory input) that is about X: Subject S M-attends to X = subject S’s M(X) guides S’s response in solving the Selection Problem.

Figure 2.4 Attention is involved in any action as a solution to the Selection Problem, for in each action, an input guides response. Applying the Jamesian Condition entails that the mind’s selection of a target, here S1, to deal effectively with, here producing response R1, constitutes a type of attention: selection for action. The gray circle, as in Figure 2.2, identifies attention in action.

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The subject’s exemplifying M(X) is the subject’s mentally taking possession of the relevant target of action in an M way (mnemonic, perceptual and so on). Every action involves attention. We can argue for the necessity of attention in action. The Jamesian Condition allows us to locate attention in solving the Selection Problem. Consider the manymany version where an action is performed as depicted in Figure 2.4. Applying the Jamesian Condition, we see that the subject mentally selects S1 to deal with, say make an eye movement to better focus on S1. Accordingly, the agent withdraws from S2 and other putative targets. This yields the input being part of the process of guiding response but excluding other inputs. The subject is thereby attending to S1. Every action as a solution to a Selection Problem involves attention.

2.4 Attention as Guide versus Attention as Mechanism Attention is not a mechanism modulating neural activity; rather it, as a subject-level activity of guiding response, is constituted by specific neural modulations. Attention has a causal impact by guiding response. That is its primary causal role, part of an agent’s acting. Readers with a passing familiarity with the empirical literature on attention, however, will have come across the metaphor of visual attention as a spotlight. On this common conception, attention is like a tool that one, or one’s brain, can deploy, highlighting a target. In the science of attention, attention is said to modulate psychological and neural processing. That is, attention is a causal factor that scientists appeal to in order to explain other biological phenomena. I disagree, for attention is not a cause or explanans as the spotlight metaphor implies. Attention is an explanandum, what we are trying to explain. We must push back on this causal conception of attention because it distorts our understanding of attention at multiple levels of analysis, including philosophical. In neuroscience and psychology, attention is taken to cause the neural modulations and behavioral signatures associated with attention: amplification of neural signals, remapping of receptive fields, decrease in reaction time, or increased accuracy. We have already considered receptive field contraction (Chelazzi et al. 1998, 2001), so consider another common neural signature: gain modulation. For example, in multiplicative gain, a neural signal in the spikes generated by a neuron to a stimulus is amplified, increased by a multiplicative factor when the subject attends to that stimulus (recall the attention field in divisive normalization; Section 1.7). In both receptive field contraction and gain modulation, attention is taken to be the mechanism that induces these responses. Attention so conceived

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is not the subject’s paying attention since the subject does not modulate neural response. More than likely, while the neural modulation occurs, the subject is attending to the external world. So, we have an ambiguity in “attention.” In neuroscience it means a mechanism, whereas in descriptions of action it means the subject’s selective guidance of behavior. There is no consensus on what mechanism attention is (Allport 1993). The failure to identify a specific mechanism or process has led to widespread skepticism about the very idea of attention. Russell Poldrack has recently averred: “I don’t think we know what ‘attention’ is. It’s a concept that’s so broad and overused as to be meaningless. There’s [sic.] lots of things people in psychology and neuroscience study that they call ‘attention’ that are clearly different things” (quoted in Goldhill 2018; cf. Hommel et al. 2019). Strikingly similar thoughts were expressed over 100 years earlier by Karl Groos (quoted in Mole 2011): “To the question, ‘What is Attention?’, there is not only no generally recognized answer but the different attempts at a solution even diverge in the most disturbing manner.” The century-old problem remains unsolved. The solution is to demonstrate that there is a single characterization of attention accepted (typically implicitly) by all scientists of attention who treat standard experimental paradigms as valid, namely that expressed by the Jamesian Condition. The deductive argument showed that empirical methodology is built around that condition as a normative principle justifying experimental design (Section 2.2). Further, the condition’s role as an organizing principle in a Marrian explanation reinforces its centrality. The condition imposes a specific interpretation of relevant brain data since that data is collected under a specific empirical sufficient condition for a task, an instance of the Jamesian Condition. Consider changes in gain (neural signal intensity). There is nothing just in the increase in a neuron’s response that entails attention. We can increase its response by manipulating the stimulus responded to, say by increasing stimulus brightness. On its own, amplification does not entail attention. Rather, changes in brain activity are tied to attention precisely because the data is gathered while the subject is performing an instructed task, that is while the subject is attending in the Jamesian sense. The empirical sufficient condition constrains the collection of neural data and provides a framework to interpret it. This leads to a natural explanation: relevant neural data collected under the empirical sufficient condition identify the putative realization or implementation of the agent’s guiding her response in that task. So, the observed neural modulations are not caused by attention. They realize or constitute the agent’s attention. This point is part of biased competition, arguably the most widely endorsed empirical account of attention. Biased competition does not conceive of attention as the cause of neural modulations observed in experimental tasks. Robert Desimone and John Duncan explicitly disavowed such a spotlight (causal) conception:

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In Appendix 1.1, I describe a connectionist model by Jonathan Cohen et al. (1990) that mirrors the Selection Problem. On the place of attention in the model, they later noted: “The key observation is that attention, and corresponding control of behavior, emerges from the activation of a representation of the task to be performed [cf. intention] and its influence on units that implement the set of possible mappings from stimuli to responses” (Botvinick and Cohen 2014, 1254). Attention arises when competition is resolved. This inverts the traditional direction of explanation in the science of attention. Attention doesn’t cause the resolution of competition. Rather, it is explained by the resolution of such competition, this resolution constituting the agent’s (subject-level) attention. We move away from a horizontal causal conception where attention is a causal spotlight:

Instead, we have:

Resolution of neural competition is necessary for solving the Selection Problem (cf. the Buridan Action Space; Section 1.5), a solution due to a bias such as intention that sets attention. This model eliminates the spotlight mechanism without denying that the agent attends in guiding response such as moving the eyes (Section 2.1). For much of cognitive science, attention is the explanans to which biased competition contributes an explanation. What results from the resolution of neural competition is the mind’s taking possession of one out of many targets to deal effectively with. If anything functions like a spotlight, it is the relevant bias such as intention. After all, neural processing must shift in a selective way precisely because the subject aims to perform a specific action. Attention as we experience it is a cause not in the sense of a spotlight mechanism influencing

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the brain but as what guides an agent’s response in action (contrast this with the positions of Sebastian Watzl (2017) and Carolyn Dicey Jennings (2020) who characterize attention as a cause of psychological or neural modulation). In the face of a century’s disagreement about what attention is, we have an anchor accepted in the science of attention provided by the Jamesian Condition. The antidote to confusion and disagreement is to stick to what we actually agree on. What remains is attention as an important target of explanation for cognitive science. Science, in studying attention, investigates what is involved in the agent’s selectively taking something in order to respond to it. That is the basic sense of “attention.” We have mapped attention to the Selection Problem, and this leads to an economical conception of attention. What it is for a subject to visually attend to a target, or in general to M-attend to a target for psychological mode M, is for the subject’s visual taking of the target to inform her response. That visual input constitutes visual attention when it plays the functional role of guiding response. More generally, the M-psychological input constitutes M-attention (visual, auditory, cognitive) when the input guides response. This means that there is no need to engineer an additional capacity for attention once the system’s behaviors involve the subject’s taking a target in order to guide response. We have attention just when the subject’s taking things guides. In our diagrams in Section 2.1, nothing further need be drawn to identify attention (recall the engineer; Section 2.1). The key point for attention theorists is that attention is not a tool, something we manipulate like we move our eyes in overt looking. Rather, we invoke attention to describe a selective orientation in action. Two questions remain: whether the Jamesian Account that identifies attention with guidance in action is viable and whether we should court ambiguity and claim that attention might be other things too. I do not discuss the second topic in this book, though I am skeptical of there being good arguments for the common identification of attention as a limited resource akin to energy (Kahneman 1973; see Wu forthcoming a). I will return to the first issue in Section 2.9.

2.5 Perceptual Attending as Mental Action Attending as an action is guided by attention, often with improved access to its target. Attending is something we can do intentionally, strategically, effectively, impolitely, and so on. This book investigates different ways of attending (Chapters 4–7), but in this section, I focus on visual attending as an action, hence as having the basic structure: an input guides an output where that input is attention. To understand the structure of visual attending, consider an automatic (passive) form: attentional capture. The sign of mundane attentional capture is

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automatic orienting, the movement of the body to bring a sensory organ to an optimal position relative to the stimulus, say fixating on a visible stimulus by shifting one’s eyes, turning one’s head to track a sound or haptically exploring an object carefully with one’s fingertips. This is overt perceptual attending. Attentional capture is an automatic action of attending, so it occurs without an intention to so attend (Section 2.7). In this case, coupling of a perceptual experience to guide a movement of the sense organ is fully automatic, passive in the technical sense. That said, overt attending is often intentional, and when it is, we speak of looking or listening in the visual and auditory domains respectively (Crowther 2009b, 2009a). Covert attending necessarily exemplifies coupling with a covert response. Consider the premotor theory of attention, a prominent theory of visual spatial attention (Rizzolatti, Riggio, and Sheliga 1994). While the theory is problematic as a general account of covert attention (Smith and Schenk 2012; Wu 2014, sect. 2.6), it identifies one type of response in some forms of attending: preparation of eye movement. Visual spatial covert attending can involve the preparation of eye movement toward the attended location. In such cases, the covert form is modeled on the overt form. Overt visual spatial capture of attention involves the visual taking of a stimulus guiding an overt eye movement toward it while the covert form involves that visual taking guiding the preparation of an eye movement toward the stimulus. In both, we have an input coupled to a motor output, one explicit and overt, the other preparatory and covert. Motor preparation is one possible covert response. Another involves improved subjective access to the target of attention. If we think of the input state in representational terms, then improved subjective access involves a change in the input’s representational content, an initial taking as input that changes to an improved taking as output. This would be a special case of coupling in that the response is not the exercise of a distinct capacity such as a movement. Rather, the content of the input informs its own change over time, a self-coupling (see work by Marisa Carrasco on output changes in perceptual sensitivity, reviewed in Carrasco 2011, 2013). The response is a modification of the input. How the subject perceptually takes things changes when she is covertly attending to a specific target. Covert perceptual attending can be phenomenologically noticeable. Consider the cocktail party effect. At a party, one is deep in conversation and suddenly hears one’s name uttered elsewhere in the room. This captures auditory attention. One can then intentionally shift attention covertly, for reasons of politeness, to a different conversation. This switching between auditory streams has been extensively studied in the laboratory using a dichotic listening task where subjects hear two distinct verbal streams, usually one in each ear, and are tasked with parroting one of the two (Cherry 1953; Moray 1959). Many early experiments probed what was accessible in the unattended auditory channel. Note that the empirical sufficient condition secures that auditory attention is directed at the targeted

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channel through task instructions. By monitoring the accuracy of the parroting, the experimenter concludes that the subject is attending appropriately. Introspection suggests that when we switch from verbal source A to B and back, there is a difference in how the sources strike us. When attended, the words in A are clear while we do not, in some sense, hear B with the same auditory acuity, a fact that can lead to significant social awkwardness. This is reversed when we voluntarily switch to B. Extensive behavioral studies in the 1950s and 1960s bore this out (reviewed in Wu 2014, ch. 1; see also Pashler 1998, ch. 1). These gave rise to one of the first major debates about attention: is sensory selection early or late in processing? For example, subjects show a decrement in their ability to identify the words voiced in the unattended channel though they access basic features such as the voice’s pitch. Attending involves changes in sensory phenomenology and accessibility rooted in shifts in sensory processing. Covert auditory attention as experienced at a party provides a familiar case where we can change auditory attunement at will, flipping back and forth between auditory streams and sharpening our access to attended sounds at the expense of others. Action capacities are constituted by action-relevant capacities which can be cognitively integrated with intention (Section 1.7). Integrated capacities are prioritized so as to alter how the Selection Problem is solved. Where one intends to covertly attend to a specific target, one’s taking it is altered, such as increasing auditory acuity in respect of the attended voice rather than others. The agent’s intention integrates with her initial auditory awareness of the world such that this awareness sharpens. Similar points arise with covert visual attending (Carrasco, Ling, and Read 2004; Nanay 2010; Stazicker 2011; see Carrasco 2011, 2013 for reviews).

2.6 Goal-Directed Automatic Attention and Bias Attention can be both automatic and goal-directed by being sensitive to the agent’s many biases. For attention to exemplify automaticity is for it to be independent of one’s intentional control. I reproduce the earlier definitions (Section 1.4): S’s Φ-ing exemplifies S’s control of her action in respect of Φ-ing iff S is Φ-ing because she intends to Φ. S’s Φ-ing exemplifies automaticity in respect of being a Φ-ing iff S’s Φ-ing is not controlled in respect of Φ-ing. When one looks at the patterns of overt attention in Yarbus’ experiment (Figure 1.9), one is struck by the complexity of the automatic eye movements but also by their intelligibility. Bare appeal to automaticity leaves out the

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intelligibility of patterns in visual search which are clearly sensitive to the agent’s intention. At the same time, those eye movements are not controlled since the intention does not represent any specific pattern of movement. We have automatic movement that is not controlled yet sensitive to the agent’s goals. How so? Consider salience. In cognitive science, salience is explicated in terms of a salience map (Itti and Koch 2000, 2001), an algorithmic postulate that explains the allocation of bottom-up or stimulus-driven attention (for a recent discussion of philosophical notions, see the essays in Archer 2022 and Wu 2011 for an early analysis of phenomenal salience). Such attention is driven by the stimulus array and independent of the subject’s intentions and goals. As defined by Laurent Itti and Christof Koch (2000) (cf. Koch and Ullman 1985), a visual salience map is the product of integrating separate visual feature maps, say maps for visual contrast and color. Salience depends on a contrast in the magnitudes along feature dimensions and the computation assigns a salience value for every represented location on the salience map. The algorithm operates on a winner-takes-all principle where overt attention is allocated to the location of highest magnitude in salience, and in the case of overt capture, the output will be an eye movement toward that location. Note that the salience map is featureless. It represents locations in the visual field as salient relative to others without comment on their specific features. We can speak of the feature and object silence of the salience map. With a salience map before you, placing bets that the eye will move to the location with the highest value on the map is a good bet, but any bets about what object or feature is driving salience would be a guess. That information is lost in constructing the map. Salience-driven attention is automatic, but the automatic eye movements in Yarbus’ task are clearly sensitive to the agent’s intention. The salience map, which ignores task information, is not sufficient to explain goal-sensitive, automatic attention. A priority map integrates salience as well as additional factors to provide an ordering of the behavioral relevance of visual locations (Fecteau and Munoz 2006; Serences and Yantis 2006; Todd and Manaligod 2018). Like the salience map, the priority map is an algorithmic postulate that explains visual spatial attention. The priority map is a function of the “synchronic” factors of current task demands, goals, values, intentions, emotional state, knowledge, and so forth, as well as “diachronic” factors such as learning and past experience. Physical salience is then just one of many inputs in the computation of a priority map. All the inputs to the priority map are biases that shape the agent’s attentional potential or character (see also Figure 5.1 and Chapter 5). Note that if the priority map is computed by the visual system in part over the content of intention, then the resulting visual selectivity is the result of cognitive integration. Like the salience map, the priority map is feature and object silent. Yet we act on more than locations. Neither map is sufficient to explain the attention that targets features and objects. For example, even if attention were initially spatially

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directed, object and feature attunement must also be in place to guide action. Further processing is required. That said, the key lesson from the salience and priority map literature is that there are many sources of bias and that different representational capacities compete for functional prioritization in the control of behavior. For this reason, my own view is that biased competition is the more fundamental idea in that attentional phenomena necessarily reflect biases that solve the Selection Problem (cf. Watzl 2017 on priority; and for comments, see Wu 2019). The technical notions of salience and priority maps are just algorithmic postulates to explain instances of the general phenomenon of bias for action. The intelligibility of automatic biases, as in the Yarbus experiment, will be explained, in many cases, by the agent’s past experiences, learning, and skills (Chapter 5).

2.7 Attentional Capture and Passive Agency Attentional capture is a form of passive agency but is distinct from the passivity of perceiving, a behavior that is never an action. Attentional capture is a passive action, the fully automatic exercise of an action capacity. Recall the distinction between action capacities and action-relevant capacities: action is the expression of an action capacity, a coupling of input and output, these components corresponding to action-relevant capacities (Section 1.5). Perceptual capacities can be action-relevant capacities if they are potential inputs for action. Covert perceptual attending as an action begins with the exercise of a perceptual capacity, for one must first perceive in order to perceptually attend. In covert visual attending, the coupling of a visual capacity to a response is often exemplified by the sharpening of one’s visual experience of the stimulus. The passive and active forms of covert attending can exercise the same action capacity. In the former, this exercise is fully automatic, in the latter, it can be intentional. In passive covert attention, the input and output capacities are the same, say one’s input representation becoming sharper as output such as in the cocktail party effect. What then separates passive covert attending, an action, from perceiving, the latter never an action? Consider the case of zoning out where over time, a perceptual experience is extended. Assume that this is possible independent of attention. A capacity for perceiving a stimulus is then uniformly exercised over time, say one’s visual experience uniformly perpetuating because one is awake, eyes open over that time. Here an input is mapped to an output in the sense of self-mapping: the input is maintained over time just because one has zoned out with eyes open. Is this not a path in

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an action space? If so, merely perceiving is a passive action. Yet perceiving is not an action. Perceiving is a type of behavior, so it can be mapped to a behavior space, but not all behavior spaces are or can be action spaces (Section 1.2). There are passive behaviors, and we noted that the family of reflexes mark one type. Perceiving is another. In the case of perceiving, the reason to block its entry to being in an action space is that unlike attending, perceiving is not something one can do intentionally. It does not constitute an action capacity. What is fundamental to perceiving, say one’s seeing the wall, is that one is perceptually aware of the wall. What is necessary for one’s perceiving the wall is world dependence, not intention dependence. I cannot intend to perceive though I can intend to have a look. Intending to look is intending to attend by directing my eyes. In intending to have a look, to overtly attend, I must be able to perceive the world, but perceiving is not something that is ever up to me as something intentionally done. It is the world, not the subject, that is in control of one’s perceiving, so long as the subject’s perceptual system is in order. At best, the subject’s doing something can influence perceiving, say ensuring that one is alert, eyes open and properly directed. We influence perception in attending. In contrast, action, as a subject-level kind, has its essence in its being the expression of a capacity that can reflect the agent’s will through intention. So simply seeing is not within our agentive power, something we exercise. In contrast, opening one’s eyes, looking, and visually attending are. While the world induces the exercise of our ability to perceive, we can modulate that exercise by intending to look or listen. What is within our power is to bring about the perceptual changes associated with ways of attending, whether it is a movement of a sense organ or a covert alteration of perceptual access. Only then do we cross not just a behavior space, but an action space.

2.8 How Much Attention Is There in the World of Action? Attention is everywhere, largely automatic, mostly unnoticed. There is more attention in the world than one realizes. This is a consequence of guidance in action. Let me bring this out with bodily movement. Consider reaching for an apple deeply inset within tree branches (Allport 1987, 396–7). As one reaches for it, one’s seeing the apple guides one’s behavior. At the same time, one automatically avoids touching the branches, leaves, and other potential hindrances as one reaches. Avoidance is not something that one always intends to do, but one does it. One’s reaching toward that apple is controlled even as one’s reaching so as to avoid some of the twigs and leaves is automatic. Action is pervasively automatic.

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Yet the movement that avoids obstacles is guided by one’s visually taking them and their features in. Thus, we have attention not just to the apple but to obstacles. Isn’t this too much attention? The guidance in action theory seems to entail more attention than we are aware of. Indeed, the objection continues, the theory misclassifies informational guidance that is distinct from attention. Of course, there is informational processing relevant to generating action, much of which does not involve the subject’s taking things. Yet the objection must show specifically that there is subject-level guidance of action that is not attention. Here, folkpsychological intuitions bear the burden of making the case, emphasizing that the folk are inclined to deny that one is attending to the twigs and leaves even if one’s seeing them guides one’s movement. We should not draw on such folk data in theorizing about how attention works. I allowed that we are authoritative on certain attentional matters (Section 2.2). In Chapter 4, I will argue that our authoritative access to action draws on our intention as a form of practical memory so, effectively, our access is explained by direct access to the controlled features of action, those features we intend to bring about. This predicts that we will not have direct access to the automatic elements of attention since those features are not, by definition, represented in intention. Accordingly, automatic aspects of attention are beyond our immediate access. The account predicts that the folk will miss automatic aspects of action so will largely be unaware of the pervasiveness of automatic attention. Others, of course, can note one’s automatic attention (e.g. noticing that one’s interlocutor at a reception keeps looking around the room; also bias and gaze; Sections 5.4 and 5.6). For automatic attention, one is not in a position to reliably render judgments about it from the inside. The objection effectively mistakes controlled attention as the only form of attention. The distinction between automaticity and control entails that automatic features are a pervasive aspect of our lives, and thankfully so. We should not expect that we are aware of all those features, at least directly aware of them in the way that we can be of controlled features of action (Sections 4.5 and 4.6). Yarbus’ experiment brings out the pervasiveness of automatic attention that flies below cognitive access. Saccadic eye movements happen one to three times a second in normal viewing, and overt attention of this sort is empirically understood to be set by prior covert attention. This means that covert attention also happens one to three times a second. Yet we are barely aware of our eye movements, certainly that they happen at this frequency, let alone their pattern. A large chunk of automatic overt attention, 57 to 172 thousand eye movements a day, assuming only eight hours of sleep, and their accompanying covert forms of attention are unnoticed by subjects. That is, we are largely unaware of automatic attention. Accordingly, we have good reason to question whether the folk are authoritative in respect of automatic as opposed to controlled attention. Attention is largely automatic and unnoticed. For pervasive subject-level automatic selection of action, I suggest that a well-supported theory is better placed to inform us of attention’s presence.

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2.9 Action Is Necessary for Attention When a subject attends to a target, she is acting on it. In integrating work at different levels of analysis, I have drawn on a sufficient condition for attention: the subject’s selecting a target to deal with. The condition suffices for most of the work to be done in this book. The Jamesian Account, however, also holds that in attending to a target, the subject is acting on it. Many find this commitment obviously incorrect for it seems that there are forms of attention that can occur without any guidance of response. One reason why this objection gets a purchase is that we immediately think of attention without movement. These are, however, still cases of guidance in action, the exercise of an action capacity that one could express intentionally. Indeed, once it is clear what acting comes to, namely input-output coupling in an action space, then it seems that guidance is present in every movement, even the subtlest movements of the mind. Covert perceptual attending provides a framework for thinking about proposed counterexamples as in fact instantiating the structure that we have uncovered. Indeed, one can imagine other cases where the input informs the output in a simple way. Thus, one might be able to hold a visual image in mind over a period of time, perhaps not long, but more than just an instant. Here, the input is mapped to the output over time, but we still have a mental action, where the maintaining of that image is intentionally done (for example, in working memory, Chapter 3). Intuitions drive the resistance to the Jamesian Account for it feels that there are many cases of attention without action. Take a case recently posed to me: attending to a work of art just to enjoy it. One is just attending. But avoid intuition and think through what is going on (cf. Section 1.9 on Frankfurt’s driving case). Presumably one is looking at the painting and not zoning out. Now looking is itself an act of attending, even if one is holding one’s eye fixed on a part of the canvas of interest, though one is typically doing more than that. At a minimum, one’s enjoyment derives from heightening one’s visual experience of the target of attention, enjoyment keyed to improved access to the painting, its colors, textures, or brush work. Even in such cases, properly thought through, we find the agent’s movements of mind and body. In general, my response to putative counterexamples that attempt to show attention without agency is to ask whether the form of attention at issue can be part of intentional action, and then to uncover the structure of such expressions of intentional agency, revealing its component action-relevant capacities. Putative counterexamples will then be revealed to have the relevant structure, often unnoticed when we focus on the automatic forms but brought to light once we recognize that action has its own internal architecture.⁴ No doubt, a metaphysics of attention is subject to continuous assessment, but in respect of a view that coheres

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most fully with the science of attention, with well-grounded folk-psychological pronouncements, and integrates with philosophical concerns, the guidance in action view defended in this chapter seems to me the best view as things stand. It is the view that should be our starting hypothesis in thinking philosophically and empirically about attention and its relation to other aspects of mind.

2.10 On Different Lessons from Causal Deviance Central cases of causal deviance involve the disruption of attention, hence the absence of appropriate agentive guidance, a necessary feature of action. Deviant causal chains present counterexamples to standard causal accounts of action. A standard scenario concerns an agent’s having an intention to Φ which leads to an emotional state that so agitates the agent that she is said to Φ. Roderick Chisholm’s imagined nephew intends to drive over to his uncle’s house to do him in and inherit the man’s fortune, but the intention so agitates him that he drives recklessly and hits a pedestrian who happens to be his uncle. Claim: the nephew’s killing of the uncle is caused by the intention but is not an intentional action. The agent’s intention causes the action but not in the right way. Thus, the standard account does not adequately explain guidance and control which goes missing in deviance. Unfortunately, no solution to deviance is widely accepted. The standard response to causal deviance is to explicate causation in the right way. One can, however, extract an additional important lesson. A causal theory of action should reveal to us the psychology of agency, what the psychological components of agency are. These components give us the agent’s perspective on and in action. Earlier, I noted that the same theories undone by causal deviance largely ignored perception. If a lesson of deviance is that causal analyses have failed to recover agency, then given the absence of perception, a natural hypothesis is that some of the defects uncovered by the classic counterexamples are in the first instance a defect of theoretical omission. That is, problems arise when a theory explains a phenomenon without all the necessary parts. Separate two distinct questions regarding intentional action: 1. When is causation in intentional action non-deviant, i.e. “in the right way”? 2. What are the psychological components of intentional action? Notice that (2) is a question about psychology while (1) is a question about causation. Further, we can answer (2) without having an answer to (1) though arguably, it is hard to answer (1) correctly without having the right answer to (2). Philosophy of action emanating from Davidson’s work focused on (1) (see my discussion of Frankfurt’s driving case, Section 1.9). I am focusing on (2).

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Ignoring attention is like ignoring intention, belief, or desire in a theory of action. Silence on attention just is failure to theorize about a central part of action. It is to leave out guidance. Consequently, one cannot tell the complete causal story of action. Interestingly, many well-known cases of deviance leverage this gap, the loss of guidance. When guidance goes missing, so does action. Correlatively, certain cases of causal deviance are settled when attention, guidance, is put back in its rightful place. Cases where emotion generates deviance are cases where emotion disrupts guidance: the agent is not guided by how she takes things. In the famous case, the nephew accidentally kills his uncle. He might feel dissatisfied in not having done the deed by guiding it. What would that involve? “I wish I recognized that it was him,” the nephew might mutter when arrested. “I would have looked him in the eye and mowed him down.” The nephew recognizes that what he wanted but failed to do was to kill his uncle by aiming for him qua target of his homicidal intention (consider fine-tuning intention, Section 4.3). The emotion does the disruptive work, abrogating ordinary guidance to trigger an uncontrolled movement that by chance yields the right result (for an implementation of similar ideas applied to Chisholm’s case in an artificial intelligence (AI) architecture, see Bello and Bridewell 2020). If we restore guidance, we restore agency, here an intentional homicide. Arguably, answers to (1) will depend on (2). Attention is part of “causation in the right way” in action, for when it is left out, guidance disappears. A causal theory of action must explain what constitutes the agent’s control and guidance in action. These are psychological topics. As the causal theory fails to explain control and guidance as aspects of agents, what is needed is an explanation of those psychological activities qua aspects of the agent’s mind, not just a specification of the right sort of causal relation. Of course, an adequate understanding of causation will be part of the overall theory of the world including a theory of agency, but our first task as philosophical psychologists is to understand the agentive psychological capacities for control and guidance. Davidson’s mistake was not (just) that he failed to get causation right. He failed to get the psychology right. Since attention was never in view, the problem highlighted by causal deviance, a loss of control and guidance, holds in non-deviant cases. Mere appeal to intention, belief and desire will always fail to recover agentive guidance since attention remains left out.⁵

2.11 Agentive Control and Guidance Revisited Intention sets the standard for appropriate attentional guidance in intentional action. Agentive guidance and control are located in two different functional roles mapped onto action’s structure. Agency begins with an action space that poses the Selection Problem, a space constituted by the inputs and outputs available at

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that time where the inputs are the agent’s taking things while the outputs include responses. The space presents a set of action possibilities available for the agent at that time and context. When the agent acts, action is the selective expression of one of those paths, intentional or not. An action space is defined by available action capacities, those constituted by action-relevant capacities that can be jointly exercised when the agent acts. Action capacities are built up from action-relevant capacities that can be cognitively integrated with an intention. The basic idea of integration is that the link between input and output can be biased by the agent’s intention which represents a solution to the Selection Problem. When the represented input-output link is brought about, an action with the intended property occurs. The agent responds in light of how she takes things given her intention to so act. The exercise of this cognitively integrated action capacity is the locus of agentive control as expressed in action. Coupling involves guidance, the agent’s taking things informing an appropriate response, that which is necessary for the fulfillment of the intention. Specifically, the response must be informed by an input which provides appropriate guiding content that is sensitive to the constantly changing context that alters with action. This content is a reason why the response occurs with the shape that it has. The most basic form of guidance is parameter setting, the provision of appropriate contents in the agent’s taking in specific guiding features that set the parameters of the response. A simple example would be in perceptual guidance where the response must be programmed by appropriate perceptual contents. Thus, one’s reaching for a mug requires visual sensitivity to location, shape, orientation, and size as well as relational properties such as egocentric spatial relations that alter as the agent moves. This is an intelligible notion of guidance, but one that applies to a variety of systems including heat seeking missiles and reflexes. What separates agentive guidance is that it involves the coupling between action-relevant capacities attributed to the subject. Guidance is also exemplified in what, from the perspective of parameter setting, might seem like an arbitrary connection. Here’s an example. Say the word “bug” to yourself in inner speech. Now, inner speak that word as many times as you wish over the next minute or so, where each time you do so, snap your fingers twice. This mapping is arbitrary, yet we have an intentional action. Indeed, arbitrary mappings happen all the time in psychology experiments. The intuitive notion of a reason why covers both parameter setting and arbitrary mappings. The latter, of course, are not actually arbitrary, for your producing two snaps on saying “bug” in inner speech is exactly as you intended. The relevant sense of guidance is tied to agentive control, namely the agent’s intending to act in a certain way in light of a targeted feature which becomes a guiding feature because of the agent’s intention. Intention sets the standard for what is required in a coupling if the agent is to act as intended.

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2.12 Taking Stock Every theory of attention should explain what visual attention is as a subject-level phenomenon, something about an agent. On the guidance in action theory, begin with a subject’s seeing X. To see X is not to attend to X, but when one’s seeing X guides a response, then the subject’s visual attention to X just is her seeing X guiding response. It is critical to note that the set of possible responses is very wide and is not restricted to bodily movements. A common form of visual attention is covert, involving a change in the visual awareness of X. Another familiar form is visual attention as part of visually attending as action. Here, the response is an eye movement to X. Critically, visual attention is not an additional component that must be added to an agentive system that is capable of responding in light of how the subject is taking things. The taking, when it guides the agent’s responding, is the subject’s attention. Moreover, attention is not a subpersonal mechanism though in us, it is implemented by a variety of mechanisms that are studied in the science of attention. To speak of attention as a mechanism is to invite ambiguity and confusion. Rather, as I argued, the Jamesian Condition provides the common ground across all domains. It is the methodological and conceptual anchor for speaking about attention as being of the subject, and it unifies all the various empirical sufficient conditions that guide the experimental paradigms in the science of attention. It identifies a specific aspect of mind that cognitive science aims to explain. The guidance in action account provides the best account of attention given that it unifies a number of theoretical and empirical perspectives. A complete understanding of agency’s expression in all of its forms must pay attention to attention. Otherwise, we would be attempting to understand a mental phenomenon while ignoring one of its central components. A complete understanding would be an impossible task. We ought not make our task Sisyphean.

Notes 1. On visual integration with output capacities: I expect that a full account of input guidance of response at computational and neural levels will show that informational integration of the sort exemplified in the link from intention to action-relevant capacities as discussed in Section 1.7 is also operative in guidance, say visual integration with motor capacities in the generation of visually guided movement. Talk of integration was a substitute for talking about informational penetration, the latter taken by Jerry Fodor (1983) to be antithetical to his notion of a module. Here I predict that informational integration is likely to be a pervasive phenomenon, key to understanding the neural substrates of action. Accordingly, the distinctions noted in the text identify functional-explanatory units that, when examined at an ontological level, might be

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revealed as an amalgam of informationally penetrated and penetrating units. Such an ontological amalgam is consistent with the explanatory force of correlated constructs at a higher level of abstraction. Consider the circuitry of the robot to be described at the end of this section. 2. Changes on vigilance with respect to Wu (2014) Attention: The present account of vigilance differs from my discussion in Wu (2014, 93–5). There I took vigilance as supervening on selection for action, on attention and attending, a function of how the agent selects a target for action over time. Here I treat vigilance as a potential for selection for action. I do agree with my earlier discussion that hypervigilance can be construed as a manner of attending (see p. 95 on the example of the gazelle). 3. Distraction and attention: James goes on: “ . . . and is a condition which has a real opposite in the confused, dazed, scatterbrained state which in French is called distraction and Zerstreutheit in German.” The contrast to distraction is potentially misleading since distraction involves the deflection of attention to task-irrelevant couplings, so is itself an attentional phenomenon. To clarify, James is emphasizing intentionally attending. It is this form for which we plausibly have authoritative self-knowledge in having knowledge of intentional action. The opposite of intentionally attending is distraction, attention in opposition to what one intends. I discuss distraction further in Section 3.6 as the bane of steadfastness in action. 4. Philosophical criticisms of selection for action: Various criticisms have been raised against my account by Carolyn Dicey Jennings and Bence Nanay (2016); Sebastian Watzl (2017); Kranti Saran (2018); Henry Taylor (2017); Peter Fazekas and Nanay (2020); Carolyn Dicey Jennings (2020). I’ve addressed some of these criticisms elsewhere and hope that this chapter indicates how I would answer others not addressed by me in print (Wu 2018, 2019). Denis Buehler (2018a) has written the most comprehensive critique informed by engagement with the bulk of my published work and for which I am grateful. I will briefly address some issues he raised. Many of Buehler’s counterexamples are claimed by him to be “plausible,” “intuitive,” and “obvious.” As such claims concern automatic attention and are not rooted in empirical work or authoritative access to action, I dispute their accuracy in reflecting biological reality for reasons given in Sections 2.2 and 2.8. In general, overreliance on such counterexamples has led to too quick dismissals of my view, dismissals that are not adequately grounded. Buehler’s central criticism is that my view both overgeneralizes and undergeneralizes attention. Focusing on attentional capture and undergeneralization, Buehler asserts that I have “to identify psychological episodes that are present in each case of attention capture, episodes for which the attended-to-stimulus is selected, and that are plausibly actions” (142). Given how he argues by running through cases and showing that for a given kind of response (output), it is not found in every case of attentional capture, Buehler seems to demand that a single type of response be found in every case of attentional capture (“Is it intuitively plausible that in each case of attention capture, one of these kinds of psychological episode occurs?”; 143). Yet I hold to no such restriction and cannot see why there is any reason to do so. Further, I do not leave the issue to intuitive plausibility. What responses are generated in automatic attention for a type of creature seems to me an empirical matter.

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Against the overgeneration charge, I note that Buehler points to a number of cases where there is selection for action but on his view, intuitively, there is no attention. I have questioned folk intuitions that we draw on in such responses as providing us reliable and accurate access to mental processing (cf. Section 2.8 on the frequency of automatic attention), and I do so in individual cases of appeal to unvarnished intuition. I cannot cover all the cases raised though I think the materials in this chapter provide the basis of a response to all of them. One general challenge is to demand that appeals to intuition be buttressed by understanding the actual biology (again, see Section 2.8 for how to do so for automatic attention). Let me reiterate a point in the text. Automatic attention is often below our subjective radar precisely because it is automatic. It is not compelling to point out that the folk do not recognize such selection as attention. The folk do not recognize covert attention before eye movement as attention either. It was science that provided evidence of such programming. I have given reason to discount folk claims about automatic attention unless they are grounded in observation, say in experiments. Mere intuition or plausibility does not suffice. Accordingly, I deny that my account overgeneralizes. 5. Getting causation versus getting psychology right: The topics of causation in the right way and of the psychology of agency set two different questions. Joshua Shepherd’s recent work addresses the question about causal non-deviance for which he provides the following: Non-deviant causation. Given an agent J, a plan-state P, a comprehensive set of circumstances C, and a token circumstance T which is a member of C, J’s behavior in service of P is non-deviantly caused in T, relative to C, if and only if (a) J’s behavior in T reaches a level of content-approximation L (to the content of P, or to some part of P) that is within a sufficiently normal range for J, relative to J’s behavior across C, (b) J’s behavior in T is produced by way of causal pathways that, when taken, lead, with sufficient frequency, to J’s reaching L across C, and (c) P is among the causal influences of J’s behavior. (Shepherd 2021, 34) This interesting proposal addresses a different question than I do. One way to see this is that Shepherd’s proposal can be adapted to apply to non-agentive systems that are executing preset instructions set by a programmer and where we can think of proper functioning as involving non-deviant causation. If one wants instead to understand what it is for an agent to guide or control her actions via her psychological activities, the above is not the answer needed. If anything, it relies on a prior specification of what the causal components are, here plans or intentions. This is not a criticism, for Shepherd and I are addressing different questions. Shepherd interprets me as using empirical work to try to solve causal deviance (37). He is right to be puzzled by such an approach, but it is not mine. My emphasis, instead, is to identify the psychological constituents of action to explain guidance and control as agentive aspects of mind needed to understand what actions as movements of mind and of body are. The empirical work enriches that picture and is not without causal and mechanistic insights, even with regard to cases of deviance like the homicidal nephew. My primary aim is not to spell out causation in the right way though, as I noted, identifying the complete set of necessary psychological components will inform that

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project. Identifying how the components of action fit together can speak to specific cases of deviance. That is, not all failures are due to a missing causal feature that holds across all actions. Rather, sometimes, the causal defect is that a causal component is missing. As I have argued, attention fills the specific gap highlighted in many cases of causal deviance that involve the loss of guidance. The difference in explanatory aspirations between my discussion above and the attempts to discover causation in the right way must be kept in mind (cf. Shepherd’s dilemma for me; 38). Whatever we think about the causal question as a problem for the theory of action, the question about guidance and control, a question about an agent and her perspective, is fundamental. Control and guidance as aspects of the mind’s movements cannot be understood without understanding the relation between intention and attention.

PART II

INTENTION AS PRACTICAL M E M O R Y A N D RE M E M B E R I N G Intention, in control, is both an agent’s being active and acting.

3 Intention as Practical Memory 3.1 Introduction Intention is practical memory for work, actively regulating and maintaining action-relevant capacities. When an agent acts with an intention, her intention is integrated into action (Section 1.7). Such actions are fundamentally cognitive in having at their core the agent’s conception of what she is doing as the basis of her control. Intention’s influence in action is fundamentally dynamic, changing and adapting over time. To capture this, I speak of an agent’s being active in intending. In this and the next chapter, I develop this conception focusing on intentions that are poised to generate action, sometimes called proximal intentions (Mele 1992), and those that are engaged in action, sometimes called intentions-in-action (Searle 1983). I set aside distal intentions which are often effectively dormant as they aim to generate action in the distant future. Philosophical investigation of intention has focused on its two functions: (1) its role in practical reasoning and (2) its role in the production of the action it represents. As I shall argue, when intention engages action, both roles are interwoven in a form of active remembering, a practical form of memory for work. In this chapter, I emphasize intention’s causal contribution to the agent’s action, arguing that intention dynamically prepares for and controls action. Subsequently, in the next chapter, I argue that in extended action, intention-inaction keeps up with action by fine-tuning, a form of practical reasoning. In doing so, the agent is thinking about her action, remembering what to do as she does it. The claim that intention is a form of memory might seem odd to philosophers. Recent philosophical debates about intention’s relation to other mental kinds has focused on whether intentions are beliefs (Marušić and Schwenkler 2018) or are reducible to beliefs and desires (Sinhababu 2012), not whether they are a type of memory (but see Soteriou 2013). Yet establishing intention is similar to establishing episodic, semantic, or working memory. All result from an experience where the experience’s contents are taken up and persist over time, capable of influencing behavior. Specifically, intention is often set by a decision, maintaining its content to yield the action decided upon and thereby maintaining the motivational force of practical reflection. In retaining decision’s content to serve future behavior, intention has the functional role of memory for work. In Section 4.2, I will argue that the notion of forgetting applies to neglected intentions thereby highlighting intention’s mnemonic function (cf. Section 3.6 on Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0004

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“goal neglect”). Here, I note how different approaches to the mind open up new avenues of investigation. Notably, in cognitive science, distal and prior intentions are characterized as prospective memory (Einstein and McDaniel 2005; see also Grünbaum and Søren 2020). To intend to act is to have a prospective memory to do so. Accordingly, a lapsed intention is a form of forgetting, an amnesia. The gloss memory for work points to the empirical construct of working memory. Working memory was introduced into psychology to theorize about retaining plans and intentions in controlling behavior. Accordingly, the dynamics of working memory, construed as an active memory, reflects the dynamics of intention. George Miller, Eugene Galanter, and Karl Pribram wrote in their Plans and the Structure of Behavior a passage worth quoting in full, marking one of the first uses of the term “working memory”: The parts of a Plan that is being executed have special access to consciousness and special ways of being remembered that are necessary for coordinating parts of different Plans and for coordinating with the Plans of other people. When we have decided to execute some particular Plan, it is probably put into some special state of place where it can be remembered while it is being executed. Particularly if it is a transient, temporary kind of Plan that will be used today and never again, we need some special place to store it. The special place may be on a sheet of paper. Or (who knows?) it may be somewhere in the frontal lobes of the brain. Without committing ourselves to any specific machinery, therefore, we should like to speak of the memory we use for the execution of our Plans as a kind of quick access, “working memory.” There may be several Plans, or several parts of a single Plan, all stored in working memory at the same time. In particular, when one Plan is interrupted by the requirements of some other Plan, we must be able to remember the interrupted Plan in order to resume its execution when the opportunity arises. When a Plan has been transferred into the working memory we recognize the special status of its incompleted parts by calling them “intentions.” (Miller, Galanter, and Pribram 1960, 65; my emphasis)

To get a sense of the thesis I will argue for, I would rewrite the passage without the storage metaphor: when we remember a prior Plan to execute, we recall and actively remember it (cf. transfering it into working memory), and this Plan, being not yet fulfilled, is an intention to act. Crucially, the passage frames the vast empirical investigation of working memory within the context of the role of intention (Plans) in controlling and structuring behavior. That cognitive science treats prior intentions as prospective memory and that working memory was introduced to play an executive function in action should make us open to a mnemonic account of intention as a substantive position (please revisit my “special plea” in the Introduction, Section 0.3). I aim to show that this position is correct. I shall argue that given a functional equivalence between executive control in working memory and intention, we can explain two aspects of agency:

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vigilance as a readiness to attend and steadfastness as a capacity to avoid distraction and stay on task. These features are best explained by treating intentions as an activeness of mind in remembering that biases solutions to the Selection Problem.

3.2 Memory in Action The agent’s remembering is her cognitively attending to mnemonic contents. Apply the structure of action from Chapter 1 to explicate mnemonic actions. Consider remembering what we perceive, as when keeping in mind a phone number heard. In psychology, tasks of remembering sequences are simple span tasks using working memory. Such remembering has three phases: A) Encoding: When on perceiving something, we come to remember it after the item is no longer perceptible; B) Maintaining: We hold on to what is encoded over time; and C) Accessing: What is maintained now guides response. Working memory theorists talk of refreshing or rehearsing memoranda, say when one repeats a sequence of numbers in one’s head. Intentional remembering involves a flow of mnemonic content, a path in action space. When remembering something perceived, the first movement is a coupling of perceiving to retention of content when one intends to remember what is perceived, so mapping perceptual experience to memory (P → M) to perform an action Φ in response to it:

Figure 3.1 Encoding memory.

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This solves a Selection Problem, for multiple items perceived can be remembered and one selectively remembers the targeted item as opposed to responding in other ways. One’s perceiving guides remembering by transferring perceptual content to memory. Here, remembering is guided by perceptual attention: I remember what I perceptually attend to. Second, we can actively maintain items in memory over time. Maintenance is cognitively attending to the memoranda across time, actively holding it in mind in short-term memory, M, so mapping memory to itself (M → M):

Figure 3.2 Maintaining memory.

This is analogous to covert perceptual attention on a single target over time (P → P) and exemplifies an action capacity that we can intentionally express. Finally, memory M is put to work guiding response R:

Figure 3.3 Recalling memory.

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The last two steps, maintenance and recall, involve cognitive attention, a mnemonic taking that guides response. That is, the input is a cognitive memory state. Accordingly, just as we introduced a distinction between attention as input and attending as action (Section 2.1), we distinguish memory as input and remembering as action. Memory is cognitive attention and remembering is cognitive attending. So, one perceives an item, remembering it to respond to it later. During maintenance one keeps the item accessible so that, when the time comes, one responds to it (recall delayed match to sample; Section 1.6). Otherwise, one forgets and action is frustrated. In working memory experiments, the maintenance period is often called the delay period. Consider the mundane case where one hears a phone number and memorizes it as one rushes to dial it. There is a delay between encoding and response. A memory conduit merges all three elements over time:

Thus:

The memory conduit is a common path in action space. A different conduit begins with non-perceptual takings, as when one recalls something from long-term memory (MLT), say a past event or a fact, hence episodic and semantic remembering respectively:

Long-term memory, in being recalled, is made active as short-term memory (MST), part of cognitively attending (see Nelson Cowan’s model of working memory; Figure 3.5). Finally, the relevant response can be the manipulation of what is kept in mind, modifying memory (say adding two retained numbers). We can represent this as:

This last mnemonic movement of mind will be exemplified in fine-tuning intention-in-action; discussed in Chapter 4. All actions of remembering exemplify the necessary structure of action (Chapter 1), and these memory conduits involve attention (Chapter 2).

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3.3 Empirical Theories of Working Memory Working memory is memory for action, specifically for the control of attention and its central executive component is the basis of the agent’s intention. I argue that empirical research on working memory reveals the dynamics of intention as memory for work, explicating the agent’s expression of control in intention, a way that she is active. Note that when I speak of working memory, I refer to processes below the subject level, processes constituted by the regulation of memory stores. The corresponding subject-level phenomenon is given in descriptions of performance in working memory tasks, so remembering for action as given in the memory conduit. This section maps working memory (processes, architecture, etc.) as the implementation of an agent’s memory guided action. After Miller, Galanter, and Pribram’s discussion of working memory as holding plans for the execution of behavior, psychologists were inspired to investigate immediate (short-term) memory and its capacity limits. Measuring capacity limits became a central focus of the working memory literature. Working memory does exhibit capacity limitations, and current estimates of working memory capacity (WMC) converge on three to four chunks (Cowan 2012b). A chunk can be understood as a mnemonic grouping that allows for efficient storage, for example the grouping of a sequence of letters, “C,” “I,” and “A” into a familiar American acronym: “CIA.” Chunking allows the efficient use of slots, here reducing the need for three slots to one (three individual letters to one acronym). Working memory is variously defined. Nelson Cowan (2017, 1159ff) highlighted eight characterizations: Working memory stores “information about goals and subgoals needed to carry out ecologically useful actions.” (Miller, Galanter, and Pribram 1960) Working memory is a “multicomponent system that holds information temporarily and mediates its use in ongoing mental activities.” (Baddeley and Hitch 1974) Working memory is a “part of the mind that can be used to keep track of recent actions and their consequences in order to allow sequences of behaviors to remain effective over time.” (attributed to the animal behavior literature) Working memory is “a combination of temporary storage and the processing that acts upon it, with a limited capacity for the sum of storage and processing activities.” (Daneman and Carpenter 1980)

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Working memory is “the ensemble of components of the mind that hold a limited amount of information temporarily in a heightened state of availability for use in ongoing information processing.” (Cowan 2012a, 45) Working memory is “the use of cue and data-structure formation in long-term memory that allows the information related to an activity to be retrieved relatively easily after a delay.” (Ericsson and Kintsch 1995) Working memory is “the use of attention to preserve information about goals and sub-goals for ongoing processing and to inhibit distractions from those goals.” (Engle 2002) Working memory is “the mental mechanisms that are needed to carry out a complex span task.” (Unsworth and Engle 2007)

This list covers salient positions from over 60 years of research (see also Just and Carpenter 1992; Towse and Hitch 1995; Anderson, Reder, and Lebiere 1996; and Barrouillet and Camos 2014). Each expresses a common idea: working memory maintains information for the guidance of behavior. Even the glosses that do not mention action, say Meredyth Daneman and Ruth Carpenter’s, look to behavior. They elaborate: “In reading comprehension, the reader must store pragmatic, semantic, and syntactic information from the preceding text [in working memory] and use it in disambiguating, parsing and integrating the subsequent text” (1980, 450; my emphasis). Klaus Oberauer notes: “There is a broad consensus on what the term working memory refers to: The mechanisms and processes that hold the mental representations currently most needed for an ongoing cognitive task available for processing” (2019, 223). The information retained can also be used for non-cognitive tasks such as dialing a phone number. The reach of working memory function is quite broad. Randall Engle emphasized working memory’s role in “reading and listening comprehension, language comprehension, ability to follow directions, vocabulary learning, note taking, writing, reasoning, bridge playing, and learning to write computer programs” (Engle 2002, 19). Similarly, D’Esposito notes that working memory “seems necessary for many cognitive abilities, such as reasoning, language comprehension, planning and spatial processing” (D’Esposito 2007, 761). Adele Diamond concurs: Doing any math in your head requires WM [working memory], as does mentally reordering items (such as reorganizing a to-do list), translating instructions into action plans, incorporating new information into your thinking or action plans (updating), considering alternatives, and mentally relating information to derive a general principle or to see relations between items or ideas. Reasoning would not be possible without WM. WM is critical to our ability to see connections between seemingly unrelated things and to pull apart elements from an integrated whole,

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and hence to creativity because creativity involves disassembling and recombining elements in new ways. WM also enables us to bring conceptual knowledge and not just perceptual input to bear on our decisions, and to consider our remembered past and future hopes in making plans and decisions. (Diamond 2013, 143)

Yet while storage (memory) plays a role in all these activities, mere maintenance of memoranda seems too impoverished to fully account for the general executive roles at issue. Attention must shift in complicated ways. Logie and Baddeley similarly noted the expansive reach of working memory: “[Working memory] comprises those functional components of cognition that allow humans to comprehend and mentally represent their immediate environment, to retain information about their immediate past experience, to support the acquisition of new knowledge, to solve problems, and to formulate, relate, and act on current goals” (Baddeley and Logie 1999, 28–9). Miyake and Shah agree, noting that “working memory is not for ‘memorizing’ per se, but, rather, it is in the service of complex cognitive activities, such as language processing, visuospatial thinking, reasoning and problem solving, and decision making” (Miyake and Shah 1999, 446; see also Unsworth 2016, 3). Lawrence Rips’ (1994) well-known psychological theory of deduction postulates that the premises of deductive inferences are manipulated in working memory (on deduction as action; see Chapter 6). In light of this, Nelson Cowan worried that: “allowed to blossom, [such expansive accounts] can include so much of cognition that, taken at face value, the definition may admit all active, complex thought processes as WM [working memory]” (1162). Working memory has a pervasive influence, but that is because it underwrites intention being active. What is at issue in the expansive conception of working memory is the role of maintained plans to regulate intentional behavior across all the noted cognitive domains, and indeed across action. As I suspect all of these authors would agree, the central executive of working memory regulates attention in keeping processing on task-relevant memoranda. This echoes the functional structure we have identified for intention in intentional action. The complexity and breadth of working memory influence suggests a need for the regulation of mnemonic content, the function of a central executive. This function is crucial to explaining working memory performance. Baddeley and Hitch (1974) articulated arguably the most influential model of working memory. Three features in their model are notable: 1. Working memory is a multi-component system including memory stores and an executive component. 2. Working memory includes multiple stores, originally, the visuospatial sketchpad and the phonological loop. 3. Working memory involves not just storage but also processing. It is active.

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Figure 3.4 The updated Baddeley-Hitch multi-component model of working memory. Short-term memory (middle row) is separated from long-term memory (bottom row in shaded rectangle). Fundamentally, working memory involves a central executive that regulates memory stores. Long-term memories enter working memory by having content transferred into the short-term stores. Reprinted from Alan Baddeley. 2010. “Working Memory.” Current Biology 20 (4): R138, with permission from Elsevier.

Early work focusing on the phonological loop divided it into two functional parts, a phonological store for storage and an articulatory rehearsal mechanism for processing such as subvocal articulation that maintains and refreshes representations, typically when the limited capacity of the phonological store is taxed. Working memory is a fundamentally active phenomenon. A powerful central executive links working memory to complex cognitive behaviors. Strikingly, Baddeley and Hitch placed it as a component within working memory. Executive regulation, however, is applicable beyond memory. Every working memory theorist accepts that the central executive directs attention, yet attention’s scope includes more than short-term memory. If attention can draw on other memory systems, such as semantic and episodic memory, then the central executive acts on working memory as one of several possible targets. This leaves two options: either the central executive is external to working memory taken as one of many targets of the executive, or the central executive is a component of working memory but the function of working memory is broader than manipulating short-term stores. The purview of working memory covers all cognitive phenomena, indeed all action. Either way, the central executive broadly regulates behavior.

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The power attributed to the central executive concerned Baddeley (1996): the central executive component of working memory is a poorly specified and very powerful system that could be criticized as little more than a homunculus . . . initial specification of the central executive was so vague as to serve as little more than a ragbag into which could be stuffed all the complex strategy selection, planning, and retrieval checking that clearly goes on when subjects perform even the apparently simple digit span task. (5–6)

To organize a research program on the central executive, Baddeley highlighted four of its functions, tied to common experimental paradigms at the time (1996): 1. Coordinating performance during dual task cases. 2. Coordinating search strategies during random generation of words from long-term memory 3. Controlling attention to one stimulus and suppressing distraction from another 4. Manipulating and maintaining items in long-term memory as needed in working memory tasks (e.g. span tasks). What is distinctive about working memory function is control through the central executive. Baddeley emphasized that the executive system is not itself a memory store: In the interests of simplicity and tractability, we assumed that the executive was purely a system for attentional control, and did not itself have any storage capacity. It was able to focus attention against potentially distracting irrelevant information, to switch attention between two or more stimulus sources or actions, to divide attention in order to perform two concomitant tasks, and finally to interface with LTM [long-term memory]. (Baddeley 2002, 247–8)

Yet in working memory tasks, the executive system must function in accordance with a representation of that task, namely with what the subject intends to do. If the central executive does not store this plan, another controller must direct its executive activity according to a plan. The homunculus regress returns. This regress is blocked if the central executive stores the plans that structure behavior. In doing so, we explicitly connect Baddeley and Hitch’s model to the Miller, Pribram, and Gallanter conception of working memory as retaining the agent’s plans. One function consistently tied to the central executive is that it regulates attention. In Nelson Cowan’s (1995) model, which is sometimes categorized as a state-based model as opposed to Baddeley and Hitch’s systems-based model

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(Larocque, Lewis-Peacock, and Postle 2014), the working memory store is divided between stored items in the focus of attention and those in activated working memory, both derived from activated long-term memory. Many state-based approaches treat short-term memory as modifications of long-term memory rather than as distinct from it in the sense of each constituting a separate working memory system.

Figure 3.5 The Cowan model of working memory, drawn to emphasize a parallel to solving the Selection Problem at the subject level.

I have redrawn Nelson Cowan’s model to show commonality with solving a Selection Problem and correlated modulation of attention: vigilance corresponds to active memory while attention is what guides behavior (note Cowan would take attention to be what causes the focus, but I have rejected that approach to attention; Section 2.4). Similar models have been proposed by others. For example, Klaus Oberauer’s (2002, 2009) similar state-based approach modified Cowan’s account, conceiving of working memory as involving (a) activated long-term memory, (b) a region of direct access, and (c) a focus of attention with a capacity limit of one chunk. Given the algorithmic-functional diagrams of the Baddeley and Hitch and the Cowan models as well as the central executive component as a controller of attention, a familiar structure emerges: an executive capacity that influences the selective flow of task-relevant content to inform response. This parallels the functional role we have attributed to intention and suggests that working memory function provides the algorithmic image of the structure of action in the memory conduit. Accordingly, we can reconceptualize the empirical work on working memory as probing the use of short-term memory to guide response in concrete

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tasks given a retained plan, an intention. The aforementioned models of working memory are information processing depictions of the substratum of intentional mnemonic action. They describe the implementation of a path in action space, a memory conduit, in light of the agent’s intending to act. My proposal is this: The dynamics of working memory underwrite the dynamics of intention in respect of the control of action, specifically the control of attention.

3.4 Memory at Work Empirical investigation of working memory as an executive capacity explicates the activity of intention in setting attention. All working memory theorists agree that working memory controls attention. They also emphasize its limited storage capacity, a conception reinforced by emphasis on chunking and simple span tasks (for a methodological review of span tasks, see Conway et al. 2005). It is complex span, however, that is the preferred measure of working memory capacity (Daneman and Carpenter 1980). Consider Marilyn Turner and Randall Engle’s operation span task where subjects are presented with arithmetic statements such as (3*3) + 2 = 10 to evaluate for correctness (incorrect here!) as well as presented with a word to maintain in memory, a simple span task interleaved (Turner and Engle 1989). So, subjects might receive the following sequence: (1) “(3 * 3) +2 = 10? Dog”; (2) “(2 * 4) – 3 = 5? Car”; (3) etc. After a sequence of such math-word pairings, subjects are asked to recall the sequence of words: dog, car . . . Performance on complex span is better correlated than simple span with measures of intelligence to which working memory is postulated to contribute (Section 3.3). Some theorists treat complex span tasks as working memory tasks and simple span tasks as merely short-term memory tasks (Conway et al. 2005). Yet simple span seems the pure measure of storage. Complex span introduces a dual task situation the coordination of which is, as Baddeley (1996) noted, one of the roles of the central executive. Thus, complex span requires regulating dividing attention and response. Andrew Conway et al. comment that there is “a clear distinction between the traditional concept of short-term memory capacity (STMC) and WMC. STMC is thought to reflect primarily domain-specific storage, whereas WMC is thought to reflect primarily domain-general executive attention” (Conway et al. 2005, 771; cf. Diamond 2013, 147). Accordingly, the preferred probe of working memory capacity is a measure of the capacity to control attention. Engle elaborates this as follows: Differences in WMC reflected differences in ability to control endogenous attention—the ability to maintain attention on critical tasks and to avoid having

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attention captured by either internally generated thoughts (“gee, what’s for lunch?”) or externally generated events (“ooh, pretty butterfly”) that lead to thoughts that compete with performance on the task. (Engle 2018, 191)

Further, he notes the tasks used to measure WMC [e.g. complex span] largely reflect an ability to maintain information in the maelstrom of divergent thought. Tasks measuring fluid intelligence largely reflect the converse of that—the ability to think of something that may be important at the moment, but when it shortly proves to be unimportant or wrong, to disengage or unbind that information and to functionally forget it. These two constructs are therefore to a great extent contradictory, so why are they so strongly correlated, in the range of .6 to .8 at the construct level? The answer is that both abilities rely on the ability to control attention to do the mental work necessary to either maintain information or to disengage from information. (Engle 2018, 192)

The selection and withdrawal capacities are not contradictory but reflect different movements of attention in action, with intention controlling single or multiple tasks. Baddeley similarly remarked: “the central executive clearly reflects a system concerned with the attentional control of behavior, with subsequent developments almost certainly depending on parallel developments in the study of attention and the control of action” (Baddeley 1992, 559). Note Baddeley’s linking working memory with the control of attention and action. From this perspective, it is not that storage is irrelevant. We are dealing with memory after all. Rather, it is the control of attention that explains the use of relevant memoranda probed in working memory experiments, especially complex span. We can treat working memory capacity as correlating with level of control as we have defined the latter. Working memory capacity is computed as a function of performance (e.g. a function of hit rate (correct detection) and false alarm (incorrect “detection”); see Pashler’s K (1988) and Cowan’s K (2001)). My suggestion is that such capacity measures also reflect differential control in complex span. How many items a subject is measured to retain correlates with a capacity for active control. In models of working memory, the control of attention is attributed to the central executive. In the theory of action, it is attributed to intention. We can align the structure of intentional action to the Baddeley/Hitch and Cowan models. Every working memory task fits the memory conduit:

Consider the maintenance phase (M → M) where one intentionally keeps something in mind such as a phone number. We can represent this as follows in an action structure with the arrow indicating maintenance over time:

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Figure 3.6

By the Cowan and Baddeley/Hitch models of working memory, we have a parallel subpersonal structure.

Figure 3.7

The parallels are clear. In working memory, an executive function sets mnemonic selection of relevant memoranda as intention would set cognitive attention in the memory conduit. Aligning traditional models of working memory with the structure of action in the Selection Problem, we uncover the functional convergence between intention and the central executive. These are not, I propose, redundant systems at the same level of analysis. Rather, working memory provides the algorithmic basis of mnemonic action in the interaction of subpersonal systems. The storage systems realize subject-level inputs at different stages of the subject level of agency in the mnemonic conduit while the central executive realizes the structuring role of the agent’s intention in setting appropriate (cognitive) attention. In discussing the biasing role of intention in setting attention, I drew on the biased competition model to explicate task-relevant modulation of attention. My proposed link between intention and the central executive in working memory is

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presaged by that account. Desimone and Duncan hypothesized that in top-down attention, “the top-down selection templates for both locations and objects [taskrelevant targets] are probably derived from neural circuits mediating working memory [my emphasis], perhaps especially in prefrontal cortex” (Desimone and Duncan 1995, 217). The content of working memory at issue is the task instruction, but the task as instructed is the content of the agent’s intention. This link would not have surprised Miller, Pribram, and Galanter. We should not ignore the alignment of a philosophically derived structure of action, the psychological theory of working memory, and the underlying neuroscience. This illuminates intention. Given the alignment of the role of the subject’s intention and the function of the central executive, there is a straightforward empirical prediction: attempts to manipulate one will affect the other and vice versa. The basic case can be seen in task instructions during experiments. To set a subject’s intention, one provides instructions regarding a task to be remembered throughout the experiment. Setting intention in this way is placing the plan in working memory to guide performance. If the plan is forgotten, this ruins the experiment. Similarly, if one wishes a subject to retain items in working memory, one instructs the subject to do so in performing a task. Instructions set the contents of working memory and of intention by identifying memoranda as task relevant. Similar convergence is exemplified in the effects of intention and the central executive in setting attention. I have already noted that in the biased competition model, task-relevant information is taken to be retained in working memory and this biases competition to set attention. As another example, consider distraction by intention. The theory of action identifies intention as setting attention while the theory of working memory identifies the central executive as doing the same. Typically, we have many plans, and in intending to act in different ways, we exhibit an orientation to attend (vigilance; next section). How to juggle multiple intentions, especially when they can conflict, is a key feature of coherent agency, something probed in complex span. Yet as imperfect agents, our plans sometimes get muddled. For example, while we act according to one intention, another intention might inappropriately insert itself, setting task-irrelevant attention. This would be a form of intention induced distraction. Correlatively, each intention must differ in its propensity to induce the expression of action-relevant capacities. There is a large literature on working memory setting task-irrelevant attention given multiple intentions or plans. The basic phenomenon is that an intention to act later, at t₂, can render the agent more likely to respond to the later targets which, given experimental connivance, appear while the agent performs a distinct task at t₁ in the present, a current task controlled by a different intention. In some cases, the influence of the intention for the later t₂ task can aid the current t₁ task, as observed in the cuing study I shall note. In others, intention leads to distraction as in the visual search studies I shall subsequently discuss.

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An early, illustrative study by Paul Downing (2000) had subjects perform two visual tasks while maintaining fixation: (1) the primary task: to discriminate the orientation of a bracket (up/down) in the periphery, left or right of a fixation point; and (2) the delayed task: retain a face (the sample) to judge of a later test face whether they match (cf. the Chelazzi et al. experiment; Section 1.6). These instructions set two intentions to perform distinct tasks at different times: an intention for the primary discrimination task performed at t₁, another for the delayed secondary face matching task performed later at t₂. The period in which the agent does the primary task is also the delay period for the secondary face task (in the experiment, the delay period was about 3.5 seconds; Downing (2000), fig. 1, p. 468). To intend to act on the secondary face target during performance of another task is to maintain that target. The key manipulation occurred during the delay period while the subject performed the primary task. Before presentation of the primary task target (the bracket), two faces were flashed for 187 ms, each at one of the two potential bracket locations, left and right of fixation. When one of these faces was visually identical to the face kept in memory, bracket discrimination at that location was significantly faster than when a different face was presented. The flashed face, while irrelevant for the primary task, became a spatial cue that grabbed attention to the target location, leading to reduced reaction time in the primary task when that was also the location of the bracket target. These facilitation effects are similar to those seen for spatial cueing of visual spatial attention (Posner 1980). Apparently, the face held in working memory biased the subject to be ready to attend to faces for the secondary task (vigilance) but was then triggered by an unexpected presentation of the face during the primary task. Attention was consequently captured by the face even though it was irrelevant to the primary task. The agent’s sensitivity for a future target misfired though to her benefit (cf. Soto and Humphreys 2008; Han and Kim 2009). Downing and Dodds (2004) later investigated whether such effects could occur in visual search. While they did not find an effect (see also Houtkamp and Roelfsema 2006; Woodman and Luck 2007), later work did uncover working memory induced distraction. Consider a subject who first performs a visual search while holding an intended target for a later task. If that remembered target appears as a distractor in the visual search, reaction time is slowed as if the remembered item pulls attention to it (e.g. Soto et al. 2005 and Olivers et al. 2006; see Olivers et al. 2011 for a review of other studies). Olivers and Eimer (2011) observed that the distraction effect on search scaled with how immanently the memoranda in working memory were likely to be needed for guiding performance in a subsequent task (table 2; p. 1556): “Memory-related distractor interference was twice as strong when the stored information could be directly applicable to the next visual display . . . compared to when it would not be immediately required” (1556). Further, if the item held in memory was first deployed in a task before doing a

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visual search, thereby discharging the need to keep it in mind, the search was not disrupted when the remembered item was subsequently used as a distractor. Both sets of studies provide evidence that items held in working memory for later tasks, items the agent intends to act on, can set attention. The previous argument identifies intention as a type of memory via its algorithmic underpinnings in the working memory construct. Consider an alternative that holds that intention depends on but is nevertheless distinct from working memory. One might opt for this alternative because intention has world-to-mind direction of fit while memories have mind-to-world direction of fit as seems to be true of episodic, semantic, and even procedural memory (I’m grateful to Helen Steward for discussion here). Such memories are successful if they match the world, say correctly represent a past event, a proposition (fact), or a way of doing things. If decisive, this position rules out cognitivism about intentions as beliefs from the beginning. As a counterpoint to the philosophical appeal to direction of fit is psychology’s identification of intention as prospective memory. That is, the conception of intention as memory is a starting point for a growing area of memory research (McDaniel and Einstein 2007). Further, not all forms of memory recognized in psychology admit of a distinction in direction of fit, say the many forms of priming. The philosopher might respond that the psychology of memory is incorrect, its tent too big, insensitive to direction of fit. In turn, the psychologist could respond that different types of memory exhibit different directions of fit, and that the philosopher should be sensitive to work in science. My stance in doing philosophical psychology is to engage cognitive science openly, so I set the issue regarding direction of fit aside as contentious. Consider instead how one would recall what one has planned to do. This relies on memory, and it seems that it relies on intention. To recall my plan in order to inform you of them, say to facilitate joint action, I report my prior intention. Yet assume that there is a form of memory that is the repository of my plan, a memory that is distinct from intention even if the latter depends on it. To properly recall my plan, I must tap into the memory and not the intention distinct from it. On this view, to tap instead into my intention is to, unknowingly it seems, fail to properly recall what I aim to do, for my recollection has missed its proper target, the related memory. This needlessly postulates mistake and redundancy, for intention is the primary repository of prior plans, and its content is what is appropriate to recall to others in joint planning. I recall my plan by reporting my intention. The argument for linking working memory to intention is not deductive but abductive, based on a convergence in their functional role. The advantages of this are substantial: the theoretical unification of two large bodies of work in philosophy and psychology. Further explanatory advantages are explored in the rest of this and the next chapter.

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As the empirical conception of memory is largely indifferent to issues of direction of fit, my bridging intention to working memory allows the proposal to accommodate different philosophical accounts of what intentions are. On many accounts, intentions have a world-to-mind direction of fit. Their function is to get the world to fit the mind, i.e. to change the world according to the agent’s plan. On others, intentions are beliefs so have mind-to-world direction of fit. Their function is to get the mind to fit the world. In my account, either type of state can occupy the mnemonic role of setting attention in maintaining a plan over time. Via their content, intentions bias attention. The central idea of this section is that the functional role of the central executive of working memory and of intention align. I have suggested that the central executive as part of a mnemonic system is part of the realization of intention as a subject-level attitude. In particular, the control functions of the central executive and of intention are tied to what I have called cognitive integration that leads to biasing of action-relevant capacities to solve the Selection Problem. Intention’s regulation of attention is a dynamic process to ensure that during the course of action, the agent’s attention is appropriately directed to guide the intended action. My reason for treating intention as the agent’s being active is informed by the way cognitive scientists conceive of working memory as active memory. The graded measure of working memory capacity is correlated with the exercise of central executive function, namely control continually deployed in complex span tasks. Notably, working memory capacity can differ between individuals with the same intention. The next two sections uncover action-relevant differences at the subject level which reflect different levels of being active across individuals who have the same intention to act.

3.5 Vigilance Intention proximal to action is an active memory for work that modulates vigilance, a propensity to attend. My interest in this section is with proximal intentions geared toward immanent action. Such intentions maintain a readiness to act by biasing the agent’s capacities for attention. This is to establish action-relevant vigilance, a propensity to attend to task-relevant targets. Vigilance’s expression is attention (recall the detective in Section 2.1), but to be vigilant regarding X is not yet to attend to X. The distraction experiments discussed in the previous section exploit this propensity for attention, triggering attention at the wrong time, with mixed consequences (Section 3.4). The agent, in intending to act immanently, is active in maintaining vigilance. To support this, I link vigilance to active maintenance in working memory (what follows draws on and modifies my discussion in Wu 2014).

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“Vigilance,” like “attention,” is a fraught term in cognitive science. Oken et al. (2006) note three empirical conceptions of “vigilance”: (a) sustaining attention over time; (b) attention to potential dangers, including hyper-vigilance; and (c) “arousal level on the sleep-wake spectrum without any mention of cognition or behavioral responsiveness” (1885). I focus on the first two notions since they are conceptually connected to attention in action.¹ Vigilance as I characterize it corresponds best to Oken et al.’s second conception. Their gloss, however, characterizes it as actual attention. I relax this in two ways. First, vigilance is a readiness to attend, not actual attention. This hews to the sense of “vigilance” as demonstrated by our detective (Section 2.1) or by the sentry for the not-yet-appeared enemy. Accordingly, vigilance is not a form of sustained perceptual attention, for it is not yet perceptual attention. Second, vigilance is not limited to threats. One can be vigilant, on the lookout, for a loved one (cf. Murray 2017; Murray and Vargas 2018 who discuss vigilance in regards to morally relevant features). Vigilance is often conceptualized as preparatory attention (Stokes 2011; Battistoni, Stein, and Peelen 2017). A common neural signature of preparatory attention is an increase of activity in areas needed to process the target to be attended to in the delay before it appears. Relatedly, in working memory contexts, one also observes delay period activity in relevant sensory areas that are conceptualized as the neural basis of maintenance of task-relevant memoranda. The neural activity during a delay tied to vigilance (preparatory attention) intersects with that tied to working memory maintenance which supports cognitive attention. In the working memory literature, initial focus was on delay period activity observed in prefrontal cortex, but Thomas Christophel et al. (2017) note that delay period activity is seen across most cortical areas, from fine-grained visual representations in early visual cortex to more abstract representations in prefrontal cortex. Accordingly, they advocate a shift in focus from asking where working memory can be observed in the brain to how a range of specialized brain areas together transform sensory information into a delayed behavioral response . . . all regions of neo-cortex have the capability to briefly retain their specialized representations in the service of upcoming task demands. Persistent activity in most, if not all, cortical regions can exert control over [better, “can guide”] future behavior. (111)

I will return to this broad view of the neural substrate of working memory maintenance (see Lee, Kravitz, and Baker 2013 for an experimental demonstration of how neural substrates of maintenance shift with task). A notable development is the sensory recruitment or sensory reactivation account of working memory maintenance (D’Esposito and Postle 2015; see also Carruthers

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2015). John Jonides et al. (2005) suggested that “the same brain regions that are involved in encoding information are those that are maintained in a temporarily active state while that information is retained for short intervals. That is, these same areas mediate working memory” (2) and that sensory cortices provide the “first site of working memory storage” (3). Bradley Postle notes “the same systems and representations that are engaged in the perception of information can also contribute to its short-term retention” (Postle 2016, 153). The relevant activation will shift with changes in task demands (Serences 2016). Neural decoding of working memory content, that is the extraction of information regarding memoranda in neural activity, has found correlates of visual working memory maintenance as early as primary visual cortex, V1 (Harrison and Tong 2009; Serences et al. 2009). In some studies, visual working memory content is decodable from delay period activity in task-relevant visual regions (e.g. visual area MT for motion) even as the memoranda are not decodable from high-level areas, such as association cortices that integrate information (Riggall and Postle 2012). Accordingly, the sensory reactivation account of visual working memory has emphasized activity in visual areas. Does sensory reactivation theory hold that all working memory maintenance is realized by early (or mid-level) sensory areas? This is a strong claim (see Carruthers 2015). Consider an assumption true of any memory maintenance system: for any system functioning as the memory store, eliminating its contents induces amnesia. This is definitional, and if amnesia does not follow, the system is not the memory store since redundant memoranda must exist as a buffer against amnesia. Given this, the strong sensory recruitment account should entail that if early sensory areas are the working memory store, disrupting sensory activity during the delay period should induce amnesia. Yaoda Xu (2017) has argued against the strong claim, focusing on the commitment to early sensory areas as “essential” to working memory maintenance. “Essential” here presumably means a necessary condition for working memory maintenance. Xu suggests that in natural perception, the continuous influx of sensory information would constantly disrupt sensory encoding of memoranda. Early sensory areas would not provide a stable substrate for maintenance since they would be constantly responding to incoming stimuli. If stored items are like a mark in the neural “sand” made by a passing sensory wave, continued perception yields continuous waves of information washing over the first mark. Accordingly, if early sensory areas maintain working memory content, disruption of processing therein should erase memory, namely induce amnesia. At the very least, such modulation should have strong effects on mnemonic behavior. Against this, Xu (2017) cites a set of studies that suggest either no effect or small effects on behavior when delay period activity in early sensory areas is disrupted (cf. Leavitt, Mendoza-Halliday, and Martinez-Trujillo 2017). There are, of course, possible responses (e.g. Rademaker, Chunharas, and Serences 2019 on multiplexing, maintaining multiple representations in a single

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neural circuit). It is not clear, however, how many theorists hold the strong view of sensory recruitment though, admittedly, proponents of the view have not been clear in their formulations (Lorenc and Sreenivasan 2021; Teng and Postle 2021). A weaker sensory recruitment view emphasizes that for some tasks, early visual areas serve as the basis of maintenance but, for others, maintenance is supported by distinct areas (cf. Christophel et al. 2017). Thus, the V1 decoding results noted earlier only show that, for some tasks, V1 can be called upon to maintain working memory contents. It does not follow that all visual working memory maintenance relies on V1 or other early visual areas. I suspect that, when pushed, most working memory theorists will endorse an ecumenical position echoing Christophel et al. (2017): working memory maintenance leverages whatever brain regions best aid subsequent behavior (see discussion in Iamshchinina et al. 2021). A weaker sensory recruitment account is consistent with the absence of amnesia after disruption of activity in early sensory areas, say when visual noise floods the visual system or when electrical activity in targeted regions is disrupted (say, by use of transcranial magnetic stimulation (TMS)). Where behavioral effects are observed during such manipulations, they are often small, far from the total failure of memory guided activity that would accompany amnesia (see review by Xu 2017; cf. the interesting reanalysis by Polina Iamshchinina et al. 2021). Consider a suggestive experiment pointing to the dynamics of maintenance. Timo van Kerkoerle et al. (2017) examined activity across cortical layers in macaque monkey V1 during a working memory task that required fine-grained spatial resolution, namely tracing a briefly seen curve from memory. Behavior was correlated with activity in V1 after the stimulus disappeared. Since the time scales involved in the experiment were in the time frame of iconic memory, van Kerkoerle et al. used masking to show that the form of memory called upon in their task was working memory (iconic memory is highly susceptible to masking). Interestingly, the mask disrupted V1 activity in neurons sensitive to the earlier target, but this activity was quickly restored within 200 ms after the offset of the mask, presumably by areas after V1 that must reflect a remembered stimulus to be acted on. Given this disruption, one of the two animals showed a performance decrement in tracing accuracy of about 10% with the second animal showing a similar trend. We can read the reestablished delay period activity in V1 as the reintegration of a redundant memorandum with V1 processing drawing on mnemonic content contained elsewhere. Activity associated with vigilance as preparatory attention intersects with that associated with working memory maintenance as both occur during a delay prior to response. This yields two interpretations of delay period activity in sensory areas (cf. Stokes 2011). First, given that amnesia is rarely (ever?) induced when disrupting delay period activity in sensory areas, if such activity serves as part of the neural basis of the working memory store, its mnemonic content is likely redundant since its elimination or disruption fails to yield amnesia. Distinct maintenance of memoranda elsewhere remains to support subsequent behavior

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even when correlated early sensory areas are disrupted. Second, preparatory activity supports vigilance as established by the subject’s intention. When the subject intends to act in the near future on a specific target, the agent is being active in maintaining a readiness to act. This readiness has its neural basis in delay period activity in early sensory cortex, or indeed in whatever area will be called upon to engage attention in action. Readiness in elevated activity facilitates intended response when the stimulus appears by disposing the subject to appropriately attend (Peelen and Kastner 2011). Disrupting readiness then hinders but does not demolish performance, as observed in a number of experiments. The interpretation of delayed period activity in early sensory areas teeters between interpreting that activity as the basis of vigilance or as the basis of working memory maintenance. Yet on either view, delay period activity is linked to the subject’s intention to act on remembered targets, namely to task. After all, the selective activation is not random but represents or carries information regarding the intended target. Intention biases action-relevant sensory capacities, either to maintain a working memory representation or to maintain vigilance. The two positions are strongly linked. Assume that early sensory activity is part of the neural basis of maintenance. Since, by hypothesis, those representations were activated during sensory encoding when the target was first perceived, they are also ready to be efficiently redeployed once the target appears again after the delay period. Maintenance activity is also preparatory. Similarly, if we think of the activity as preparatory, the basis of the agent’s being vigilant, this activity results from an intention which maintains specific targets in mind to guide future action, a mnemonic function. In principle, this activity enables decoding of mnemonic content from sensory areas even if these areas are not necessary for maintenance. Delay period activity reflects, informationally, the targets of intention. The ecumenical position consistent with the data is that delay period activity amounts to a readiness to attend and reflects, in the sense of allowing decoding of, remembered targets for task. This activity is maintained by the subject’s intending to act. With this intersection, intention’s biasing of vigilance can be recast through the image of working memory maintenance that treats correlated neural activity as underwriting the agent’s being active, holding something in mind ready to act. The neurobiological basis of working memory suggests a different way to think about an agent’s intention that is primed for action, not as a state that is ready to generate action like a mouse trap ready to spring, but as establishing and maintaining vigilance, a readiness to attend in action (cf. interesting work showing how vigilance waxes and wanes with oscillation in arousal; see Esterman et al. 2013, fig. 2A). Some intentions are distal, and it is these that are typically taken to be prospective memories that must be reactivated at the right time in the future if they are going to yield action. Where they are not reactivated, the agent fails to

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remember to act as intended. Such intentions are not active in the sense discussed in this section, for they are not yet at work. They are inactive. Distal intentions are related to the intentions proximal to action in that the former are transformed into the latter, so distal intentions as long-term practical memories must transition from dormancy to constituting the agent’s being active, beginning with establishing vigilance (cf. Cowan’s model; Figure 3.5). Distal intentions, prospective memories, are transformed to proximal intentions geared for immanent action and ultimately, to intentions-in-action. Intentions move from dormant prospective states to the agent’s active orientation toward action in maintained vigilance, and finally to attention in action.² The next section expands on this activity as seen in intention-in-action.

3.6 Steadfastness and Sustained Attention Intention-in-action keeps the agent steadfast, sustaining attention against distraction and preventing slips. Michael Bratman (1987) emphasized intention’s inertia that carries the force of a decision into the future so that at the appropriate moment, the agent acts as intended without having to practically relegislate matters. A similar steadfastness in intention is needed during action to sustain the agent’s staying on task. Attention as what guides response must also be sustained. Intention-in-action, what I typically will refer to by use of “intention” in this section, also reflects the agent’s being active. Crucially, steadfastness varies across agents. Two agents with the same intention can demonstrate different levels of steadfastness during action. Among the banes of steadfastness are two familiar ones: attention to the wrong target and attention to the right target but guiding the wrong response. The first is distraction in the colloquial sense of focusing on the wrong thing and the second includes slips of action (Norman 1981; Amaya 2013). Note that what is at issue is not just being able to stay on task during a long temporal time window, say when writing a paper during the morning, but also in the thick of the present moment of action. Steadfastness requires intention staying true to its target, not just in completing the task but in how the task is done. One agent might act efficiently while another is often bumped off track even if she reaches the goal. Steadfastness must balance flexibility against single-mindedness. Given a continuum between fixation on a task and behavioral chaos, most human actions lie between these extremes. Using distraction as the relevant measure, agents who are steadfast in the fixation sense are never distracted by task-irrelevant stimuli, never stray from their singlemindedness. At the opposite extreme are agents who are knocked off course at every moment, attention captured by each new stimulus, or constantly slip in

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response. Irresolute subjects never finish, their intentions always frustrated. They have no control. Consider the inattentional blindness paradigm as a test of distractability. In inattentional blindness, when visual attention is directed elsewhere, a striking stimulus like a gorilla is often not noticed (Mack and Rock 1998; Simons and Chabris 1999). Any implication of phenomenal blindness is, arguably, unsupported ((Wu 2017a), sect. 7.4). Instead, the paradigm suggests that being engaged with a task leads to resistance to attentional capture by a notable stimulus. Inattentional blindness can signal the agent’s focus and resistance to distraction. It can mark a virtue rather than vice, the correlate of sustained focus. In the cocktail party effect, an individual is conversing with another when her name is suddenly uttered behind her, capturing her attention (Moray 1959; cf. Mack and Rock 1998, ch. 5). One’s name is a powerful distractor. Since a sign of steadfastness is blocking distractors, steadfast intention should block distraction. In a dichotic listening experiment by Andrew Conway et al. (2001) where distinct verbal streams were presented in each ear, subjects performed verbal shadowing of one stream, ignoring the other. They were asked after the task if they had heard their name uttered in the unattended channel: 65% of low span (low working memory capacity) subjects reported hearing their name compared to only 25% of high span subjects (capacity was measured by operation span). No group heard a control name that was present (cf. related results showing greater resistance to “high reward” distractors with increased working memory capacity (Anderson, Laurent, and Yantis 2011; Anderson and Yantis 2012; cf. Section 5.2)). Additionally, low span individuals’ performance in shadowing exhibited more errors than high span individuals when their attention was distracted by their name. High span individuals were able to stay on task and perform better, suggesting sustained perceptual attention to the task-relevant stimulus, this despite all individuals having the same intention: to verbally shadow the cued speech stream. Working memory capacity as a proxy measure of executive control correlates with the agent’s propensity for distraction. It is not the content of intention that explains the observed differences in steadfastness. Despite having the same intention to perform the requisite task, the two different working memory capacity groups demonstrate different susceptibility to distraction. This, I propose, reflects different ways of being active in control, ways of intending. “Weighting a finger” on the scale in favor of task relevance is part of the agent’s capacity for control in action. As the differences measured by working memory capacity are not due to differences in the content of the intention, yet must reflect differential influence of intention, I hypothesize that they reflect differences in cognitive integration during action. Integration is the basis of the agent’s control, the agent’s being active in responding in light of how she takes things. For this reason, I hypothesize

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that whatever intention-in-action amounts to with respect to control, it reflects levels of being active in control at the subject level as revealed by differential distractability. This correlates with different values for working memory capacity.³ Of course, singular focus can turn into a vice. Consider the annoyance one feels when one calls the name of a family member who is busy at a task and fails to notice. Are they deliberately ignoring one’s call? One feels aggrieved and fails to be mollified by the other’s claims of having been deep in thought. Sometimes we should pick up on task-irrelevant cues, irrelevant to what we are currently doing but which are important in other ways. Such sensitivity is a virtue of attention (Sections 5.3 and 5.8). Accordingly, there is nothing wrong with noticing one’s name so long as one returns to the task at hand, politely continuing a conversation if one’s name is a distraction or responding to one’s name if it signals something important. Single mindedness is not always appropriate. Virtuous control involves maintaining an openness to action possibilities balanced with appropriate focus on a task over time. Put another way, in intending to act, we must balance vigilance toward alternative targets with steadfastness in attention (cf. Section 5.7 on sensitivity in the fields of thought). The nature of the instructed task, hence intention, matters. Gregory Colflesh and Andrew Conway (2007) found that if high-capacity subjects were tasked with monitoring for their name in the non-parroted channel while performing dichotic listening, 65% detected their name versus 25% in the original Conway et al. experiment. Interestingly, among low-capacity subjects in this experiment, detection dropped to 35%, reversing what was seen in the earlier work. A critical difference is that in the earlier Conway et al. experiment, the name captured subjects’ attention in a bottom-up way while in the later Colflesh and Conway experiment, the task was to detect one’s name in addition to the primary task, so the intention must maintain vigilance for the name during primary task performance. The idea is that differences in the agents’ being active is reflected in greater vigilance between groups even as the content of the intentions of the two groups remains the same, fixed by the same task instructions. The data regarding inattentional unawareness and working memory capacity does not tell a simple story. Dichotic listening is an auditory version of the inattentional blindness paradigm, so inattentional deafness (Neisser and Becklen 1975). The Conway et al. results suggests that high-capacity subjects are less distractable by unpredicted auditory stimuli. Yet when inattentional blindness is probed, that is visual attention, either the opposite effect is seen, so low-capacity subjects are more inattentionally blind, hence less distractable (Hannon and Richards 2010; Richards, Hannon, and Derakshan 2010) or no effect is observed (Bredemeier and Simons 2012). This contrast between perceptual modalities needs to be examined further. That said, electrophysiological evidence does suggest that low working memory capacity individuals have a harder time keeping task-irrelevant visible items outside of visual working memory or are more

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prone to attentional capture driven by such items, hence are seemingly more distractable (see Vogel, McCollough, and Machizawa 2005; Fukuda and Vogel 2009; and a related meta-analysis of seven additional supportive studies in Luria et al. 2016, sect. 4.1). What is suggested by the studies contrasting low and high working memory capacity subjects is that two agents who have the same intention based on task instructions also exhibit different levels of steadfastness and vigilance over the time period of action. I suggest that we treat intention as distinguished not just by its psychological mode (e.g. intention versus belief) and content (e.g. to do Φ versus Ψ) but as having an added dimension of activeness: the agent’s intending to act is a way of being active such that individuals can be more or less so. This being active is not an action but a constituent of it. The metaphysical categories that it would be natural to reach for in explicating this idea are those of process and activity. In this book, however, my aim is to understand the underlying biology first before stitching it together with the relevant metaphysical categories. My talk of the agent’s being active signals my biology-centered approach. These points are further underscored by the second bane of steadfastness, the slip to an incorrect response despite attention to the correct target. We have emphasized intending’s function in setting attention, but attention is always guidance of a response, so in setting attention, intention facilitates linking a relevant input to an appropriate response. Since intending sets correct coupling, its content sets the standard of what type of response is right given an input. Some slips of action are characterized by the wrong response produced to the right target. Accordingly, we can track levels of being active in intention during action in light of the agent’s propensity for action slips of this kind. A simple experimental demonstration of slips is the antisaccade task which involves the agent’s intention establishing a coupling anchored on an input that is normally associated with a prepotent response, namely an eye movement toward a stimulus. Subjects are instructed, however, to move the eye away from the stimulus so contradicting the prepotent response. The antisaccade condition sets a conflict between the intended task and a natural automatic movement, a tug of war between automaticity and control. Intention must prioritize one response against another, suppressing the prepotent response (cf. modeling the Stroop task and how task representations provide a counteracting bias to automatic semantic processing; Appendix 1.1). Again, differences in performance correlate with differences in working memory capacity. Given the same instructions, all subjects form the same intention to move in an antisaccadic direction. Yet subjects with lower working memory capacity show slower responses in correct antisaccade trials and more errors by moving the eye toward rather than away from the target. That is, they are more susceptible to an action slip, an incorrect coupling (Kane et al. 2001; Unsworth, Schrock, and Engle 2004). In contrast, no measurable differences in latency or

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error rate between low-capacity and high-capacity subjects are seen in prosaccade trials where the correct (intended) movement is in the natural saccade direction to the target (see also work on the Stroop effect, e.g. Michael J. Kane and Engle 2003; and Appendix 1.1). Here, the prepotent response aligns with what is intended. There are fine differences in subjects’ abilities to regulate task performance. The noted increase in incorrect movements in antisaccade trials occurs when the trials are blocked, that is, where each experimental trial requires the same type of response. In another condition, the experimenters mixed antisaccade trials with prosaccade trials in a block. Subjects had to be able to switch tasks, and those with low working memory capacity showed more errors in prosaccade trials relative to high-capacity subjects, namely more incorrect movements in the prepotent direction (Unsworth, Schrock and Engle 2004, experiment 2). The interleaving of prosaccade and antisaccade trials seems akin to the interleaving of two tasks in complex span, so requires dynamic regulation of attention. By hypothesis, this requires the agent’s being more active in her intention. My suggestion again is that differences in behavior, despite the same intention, suggest differential activity in biasing that solves the Selection Problem. Agentive control resides not just in having an intention with a specific content that one can successfully execute. It reflects an activeness associated with the subject’s intending to act. This active orientation is not itself an action, something that the agent also controls. Tuning of action capacities by intention is an automatic feature of action though it is tied to the agent’s intending to act. Biologically, the observed differences in vigilance and steadfastness in subjects with the same intention might come down to, among other things, the underlying neural implementation that supports cognitive integration or in the different levels of skill that the agent has acquired in learning. Theorists of working memory speak of goal neglect (Duncan et al. 1996). For example, Nash Unsworth et al. note: in situations when attention is tightly focused on the task goal, performance will be both fast and accurate. However, if attention is not tightly focused on the task goal, goal neglect can occur, which will lead to overall slower responses or to very fast errors that are guided by prepotent tendencies. (Unsworth et al. 2012, 327)

Since we have eschewed the spotlight model of attention, we can agree with the spirit of the claim, but reconceptualize it: where intention loosely maintains taskrelevant attention, then neglect can occur which leads to slower response or increased error. Alternatively, where intention tightly maintains task-relevant attention, the agent is focused on the goal with better control and performance. This more or less tight maintenance mirrors the agent’s being more or less active in intending to act.

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The idea of goal neglect and having attention more or less tightly focused on the task links vigilance with steadfastness. Norman Mackworth (1948) introduced the ideas of a vigilance decrement and a failure of sustained attention to task. Mackworth taxed radar operators to perform a mind-numbingly dull task over two hours (on doing a 10 minute version of this task, I was ready to tear my hair out). His subjects were Royal Air Force cadets who were instructed to monitor a hand moving like a clock hand against a plain background. Typically, the hand would move in a single jump but occasionally, the jump would be twice the size. The subjects were tasked with reporting the occurrence of the rare double jump. During the two hour task performance period, Mackworth observed a vigilance decrement, namely a noticeable drop in performance over time in the subject’s hit rate, that is, the correct detection of a rare double jump. The decrement demonstrates a drop in the ability of subjects to notice rare events. In the first instance, the decrement is measured by a failure to deploy perceptual attention in that the subject misses the target. This is tied to a failure to maintain vigilance for targets, a vigilance decrement (cf. our detective who might miss the passing thief; Section 2.1). While vigilance declines, there is also a failure to sustain attention. The attention at issue cannot be (just) perceptual since for much of the two hour period, there is no target to attend to in that no double jump appears. What one is vigilant for is a rare event which, since it rarely appears, cannot be the target of sustained perceptual attention. At the same time, one must continue to look at the hand, so failure of sustained visual attention to the hand would result in a failure to notice it moving in a distinctive way (again, cf. inattentional blindness to the gorilla because one is not looking at it). Sustained attention is complex, for what the agent must do is (a) sustain visual attention to the hand and (b) maintain vigilance, a readiness to visually attend, to odd jumps. When that jump occurs and the agent notices, (b) becomes (c) visual attention to the event, the jump. Notice that each step is mapped precisely to an action space constituting the Selection Problem in Mackworth’s task. Complexity continues. There is a fourth aspect of attention. Maintaining vigilance for the rare target is tied to working memory which holds that target active, and this is linked to intention to act on such targets. Working memory is maintained by the agent’s being active in intention, and this is to keep cognitive attention on the task intended. Accordingly, a failure to (d) sustain cognitive attention to task, effectively to forget the task, a temporary bout of amnesia, returns us to the idea of goal neglect, more precisely, neglect of the task. To maintain attention to the hand and vigilance for odd jumps, one must also be active in intention in sustaining perceptual attention (a) and vigilance (b). This requires sustaining cognitive attention to the task in one’s intending to do it, i.e. (d). Sustaining cognitive attention to task thereby sustains perceptual attention

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to a task-relevant object (the hand) and vigilance for the target event (the jump). This is our amalgam:

Figure 3.8 This figure depicts three crucial phenomena: (1) the intention which maintains the plan, so amounts to the agent’s cognitive attention to the task; (2) the visual taking of the frequent single (1x) jump of the moving hand, so visual attention that guides fixation and tracking of the hand; and (3) vigilance for the rare target event, the double jump which has not yet occurred (hence dotted line from the clock hand to that visual capacity to see the double (2x) jump). Vigilance is marked by a dark circle, a readiness to attend (cf. Figure 2.3). An appropriately active agent maintains the plan (1), focuses visual attention on the hand (2), and is sensitive to the rare event when it occurs (3), attending to it as necessary to guide report. Decrements in performance can result from disruption of any of these nodes.

Accordingly talk of “sustained attention” is ambiguous. The structure of action in the Selection Problem for Mackworth’s task provides crucial disambiguation. Correlatively, a failure of sustained cognitive attention to task, goal neglect, leads to a failure of intention doing its job leading to reduced vigilance to the double jump and perceptual attention to the hand/clock. This gives a concrete sense to the idea of goal neglect in terms of (cognitive) attention. The agent must remember what she is to do or is doing. In losing track of the task under mind-numbing conditions, such neglect in intention undercuts its role in maintaining vigilance and steadfastness. Because the activeness of the agent’s intending to act is found in biasing, it is not itself an action. As we have seen, individuals can differ in that activity as measured by individual differences in performance on the same task despite having the same intention. This raises interesting questions about individual differences in agency, ability, and responsibility. Assessment of an agent’s action

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comes down not just to what the agent intends, what she controls, but also to various automaticities in action, and many of these are revealed in differences in vigilance and steadfastness. There is a complicated issue about assessing the automatic features of action. Where an agent intends to do Φ and Φ is normatively assessable, the bias provided by intention can also be normatively assessable (see Section 5.8). If the bias is appropriate, then the action need not be problematic while if the bias is not appropriate, then the resulting action can be problematic. Still, biasing is not something the agent does just as the firing of her muscle cells is not, so assessing biases, especially automatic biases, is a complicated matter. That said, the agent’s past can play an important role in shaping how action is biased just as the past plays an important role in her muscle strength. To the extent that biases can be shaped by intentional learning and training, even if the sculpting is indirect, the agent can be held responsible for the biases that she has acquired in learning, even if the on-line activity of biasing is not something that she does (see Chapter 5 on automatic biases of epistemic and ethical concern). Her history matters. Still, not all biasing reflects learning. It might be that the relevant activeness of intention is hard-wired so that individual differences are rooted in the individual’s biological endowment. The hard-wiring provides limits on how the agent is able to act because it limits what she can learn and alter (cf. Figure 1.4 and note that different agents would have non-overlapping behavior spaces). What nature provides to agents sets limits on what they can do. It might not do to hold someone responsible for a facet of her action that is completely beyond her control. For example, agents differ in working memory capacity and this measure provides a window on the agent’s capacities for control in intention. The agent’s biological inheritance sets limits on her capacities for control. Biasing aids solving the Selection Problem by prioritizing a path in action space. Yet the case of a prepotent response brings out the possibility that paths not taken can vie for prioritization. This suggests that, sometimes, intention must not just raise up an action but must also tamp down other action capacities. These competitors for driving behavior sometimes begin with distraction, sometimes with a slip. In morally charged cases, they amount to temptations, distractions or responses that pull and distort one’s attention despite one’s intention. Part of what it takes to be an effective agent is the ability to suppress, even silence these other paths (cf. McDowell 1980 on silencing and practical wisdom). As we noted in Martin Luther’s case (Section 1.2), the normative action space might contain only one action, for sometimes, there is only one thing that must be done. Competitors are simply silenced.

3.7 Taking Stock Philosophical conceptualization of intention has depicted it as a state, possibly occurrent, something event-like. An important influence on philosophical

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theorizing here is folk psychology. Yet empirical psychology should have at least as much weight, if not more (cf. Section 2.2 for critical remarks). Focusing on the biology through research on working memory, I have emphasized that intention’s central role in action reveals it to be fundamentally dynamic. Intention near action and in action is active in setting, regulating, and sustaining attention. This being active by the agent in intending to act draws on the notion of activity tied to working memory. This is not to deny that we can speak correctly of the agent’s state of intention to Φ just as we can speak of the agent’s state of attention to X. There is explanatory power to appealing to a constant feature of the agent’s mind over time. Indeed, being steadfast is partly explained by the fact that over the time of her action, the agent intended to act in specific ways and accordingly, cognitively and perceptually attended in constant ways. At the same time, I take such mental states as supervening on the process of attending and intending, what I have called the agent’s being active (see Matthew Soteriou’s 2013 discussion of occurrent states; for critical remarks, see Helen Steward 2018). This points to a metaphysics of process and activity which I will take up elsewhere. The present point is that this shift is motivated by the biological perspective, secured once the alignment between the functional role of intention and the central executive revealed through working memory is recognized. The psychology and neuroscience of working memory uncover an active process of attentional regulation rooted in intention/working memory. At the subject level, this activity, seen in individual differences during action with the same intention, is expressed in functional differences in the setting of vigilance and steadfastness. Acting intentionally is constituted by a subject’s responding in light of her taking things given her intending to act, these three forming the triadic geometry of intentionally acting (Figure 1.5). Each element involves an agent’s being active which, when appropriately coupled in solutions to the Selection Problem constitute the agent’s intentionally doing something. The previous chapter identified the agent’s being active in attentionally taking things to guide her response during the life of her action. This chapter identifies intention’s activity in the agent’s control in maintaining vigilance and steadfastness during that same epoch. In her intending to act, the agent is being active in ways that reflects a practical memory. In the next chapter, I reveal intending as active remembering in thinking during action, a coordinated action that constitutes the agent’s fine-tuning her intention, a form of practical reasoning in action.

Notes 1. A brief note about arousal: Arousal provides a physiological background condition that affects attention, an important aspect of the biology but not immediately relevant to understanding what vigilance is. It is tied to what Michael Posner and Steven Petersen

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dubbed the alertness system of the attentional network (Posner and Petersen 1990; Petersen and Posner 2012) where “alertness does not affect the build-up of information in the sensory or memory systems but does affect the rate at which attention can respond to that stimulus” (Posner and Petersen 1990, 36). 2. Task-irrelevant vigilance: Does vigilance have to be task relevant? In Chapter 5, I shall discuss a variety of biases that lead to varieties of automatic attention, and these biases can begin their work with making the subject more sensitive in respect of certain inputs. Where the biases are distinct from intention, the resulting sensitivity or attunement is automatic and not tied to a current task. I am inclined to continue to use the term vigilance for task-relevant sensitivity and attunement for a broader range of sensitivities, including to initially task-irrelevant features, that are set by learning history and experience that generates systematic biases on what in general the agent is ready to attend to. This general sensitivity (attunement) is tied to the agent’s attentional character (see Wu forthcoming b, for further discussion). 3. Recent work on flexible control and the central executive: Denis Buehler’s work (2018b, 2022) emphasizes the central executive and cognitive control systems as constituting an agent’s capacity to guide her action where this guidance has a number of dimensions beyond setting attention to a target. More broadly, the executive system flexibly regulates a diverse array of resources during action. Buehler’s vanguard work is a detailed development of the Frankfurtian tradition that brings empirical work on the central executive to bear on core issues in philosophy of action (for another recent and substantial development of the Frankfurtian approach to control, Shepherd 2021; cf. my discussion of Frankfurt in Section 1.9). I see our approaches as fairly compatible though Buehler has been a critic of my account. Among salient differences are that (1) developing ideas in Miller, Galanter and Pribram (1960), I tie the executive capacity to practical memory and as illuminating intention whereas Buehler focuses on the flexible deployment of the central executive system in Franfurtian guidance, what I would call control, and (2) Buehler emphasizes the notion of a resource as a substantive explanatory posit, one whose current explanatory value I am skeptical of. There are, of course, energetic resources that limit mental processing including attention. It is a further question whether there is a distinct attentional or cognitive resource. For example, some theorists understand attention as a limited resource like energy (cf. Kahneman 1973). I do not think there are good arguments for this conception. There are lines of inquiry that draw on resource limitations, such as in Nilli Lavie’s “Load Theory of Attention” (2011) though the challenge for that theory is that the notion of load as a quantity still remains ill-defined (Benoni and Tsal 2013; Murphy, Groeger, and Greene 2016). Further, there is no reason to understand that theory as equating attention with load. Rather, load, whatever it is, modulates attention’s expression. For important cautionary remarks on theoretical invocation of capacity limitations, see Odmar Neumann (1987).

4 Intending as Practical Remembering 4.1 Introduction Acting on an intention involves the agent’s intending, a simultaneous action of practical reasoning as she acts. The practical life of intention is tied up with practical reasoning. Intention is typically established when an agent decides to act, and it informs additional reasoning such as identifying appropriate targets or determining means to ends, leading to more precise intentions. A distinctive output of practical reasoning is the establishment and elaboration, namely fine-tuning, of intention. Intention pervades all aspects of agency. Intentions can lapse (Section 3.6). One intends to do something but gets sidetracked. One sets some soup to boil on the stove, determined to come back in a minute . . . just one short email to finish. Two futures loom: (1) one efficiently sends off the email and returns to the stove, turning down the flame in time; (2) one gets engrossed in emails and suddenly smells something burning, such is the road paved with good intentions. Failing one’s intentions is failing to remember to act as intended. Forgetting, however, is not permanent. One does not then investigate the smell wondering who the agent was. Such complete amnesia would be a pathology (Section 4.2). Rather, on smelling the burning, one immediately recalls, too late, what one intended to do. We have many intentions, long-term plans based on prior practical reasoning and more recent and immediate decisions. Causally coherent agency requires expressing intention in an orderly and timely way. Forgetting, distraction, and slips are intention’s banes. In hindsight, it would have been better to have written one’s emails in the kitchen so that on hearing the boiling, one would have remembered to turn down the heat. That sound would have reminded one that now is the time to act (Section 4.7). Unattended action, through reduced vigilance, invites failure of steadfastness (Section 3.6). In the last chapter, we looked at intention as an active practical memory for work that biases action-relevant capacities. In this chapter, I shift to intending as practical remembering, an action. That is, as the agent acts with an intention, the agent is thinking about her action in her intention-in-action. I argue that practical remembering—intending—is maintaining and updating one’s conception of

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0005

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action. It is cognitively attending to action in remembering what to do. What distinguishes intending as a type of memory is that (a) it is the basis of agentive control in action, (b) takes part in sustained practical reasoning as intention is automatically fine-tuned in action, and (c) provides for the agent’s ability to keep track of what she is doing, providing her distinctive access to it.

4.2 The Continuity of Practical Memory Practical memory is the basis of the agent’s conception of her action that renders it intelligible to her. The burning pot raises the issue of remembering what to do at the right time. All would have gone well if the subject had been fully focused on cooking, but instead he prioritized another action, and vigilance to the pot waned. In his distraction, the subject lost track of his cooking even as he was doing it. As action extends through time, the agent must keep time with it else action goes off the rails. Indeed, without memory, the coherence of action is abolished. Coherence in agency requires a Continuity of Practical Memory in maintaining one’s plans, past, present, and future. Losing track of one’s plan, amnesia, disrupts one’s understanding of one’s action. One must remember what one has decided to do and what one is doing. Consider one of the most basic human actions, conversing. Your companion says that p and you respond with understanding by saying that q. Companion (C): “It’s going to rain cats and dogs today.” You: “Well, you better grab a heavy duty umbrella!” One way of describing your action is as your responding to your companion’s saying that p by your asserting that q. This is a description under which your action is intentional (Anscombe 1957). In your normal agentive awareness, you must conceive of your action as a response: I am saying that q in response to what you, C, said. As Anscombe noted, your intentionally acting is subject to the question, “Why?” Your answering the why-question by describing your action as responding to C’s report or by citing what C said requires that you remember that C said that it’s going to rain. Your response is, as you conceive it, to something just done. Consider targeted amnesia where this conception of your action is not available to you, for you have been made to forget what C said or that C said it. Accordingly, as you begin to say the words, your memory of what C said disappears in a specific bout of forgetting. The words you utter are sensical, but they cannot be conceived by you as a response to p or to C’s saying that p since you have forgotten this. Unlike the case of the burning pot, your practical memory is permanently eliminated. Your words are now words you utter responding to something C

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said though you know not what. This is already to undercut your normal link to what you are presently doing, indeed to your answers to the why-question. Your conception of your action as intentional is defective. Indeed, if the amnesia were deeper, eliminating your memory that there was a conversation in the first place, then your experience of your words must be as something randomly said in front of C, an embarrassing, spontaneous verbal fulmination, or so it seems to amnesic you. In consequence, you cannot conceive of your utterance as even a response since as far as you know, there is nothing to respond to. The same points would apply to your thinking that one better grab a heavy duty umbrella as a cognitive response to your own thoughts about the weather. Targeted amnesia would similarly destabilize your cognitive agency, leaving you to wonder about the point of the thought. Indeed, if some inference is like conversation, each inferential step a response to the previous step, then propositions inferred or asserted do not come as if from nowhere. Normal agency in inference, thought, and conversation must be responsive to a past that provides the context for the intelligibility of action to the agent herself, the basis of her being able to say why she acts. Our present actions are intelligible because we connect them to the past. Losing contact with action’s past undercuts our understanding of its present. When I smell the burning pot (ok, ok, it was me), I recall what it is that I was doing, setting the pot to boil, and what I am doing, cooking . . . whoops! Having lost contact with my cooking, the burning smell induces the recovery of an intention that receded from memory in a manner appropriately captured by talk of forgetting, slipping one’s mind, or losing track (see goal neglect; Section 3.6). In remembering it, my intention is re-active. I retake control, though too late. We have already noted intention’s mnemonic role in carrying forward my decision to act. Intention-in-action must also retain a backward connection to action’s past if it is to maintain one’s understanding of what one is doing.¹ Sudden amnesia or a lack of immediate access to practical memory destabilizes one’s grip on what one is doing by making unavailable a conception of it under which it is intentional. This points to a crucial form of memory in action. The Continuity of Practical Memory: In action, what one is now intentionally doing is intelligible as intentional because of its link to the relevant past in intending to act. Consider walking into a room and forgetting why one is there: to get a book, find a pen, open a window (cf. Velleman 1989, ch. 1)? One knows that one has come into the room intentionally, so has maintained contact with part of one’s action. This is like knowing that C said something though you remember not what; or that you were just thinking about some subject but lost the thread. One has forgotten a critical part of the intention, namely its aim as established in the decision, say, to

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get a specific book. What is forgotten concerns action’s future, its end, but this was something intended in action’s past, something to be remembered as to be achieved. Loss of contact with action’s future is based on loss of contact with action’s past, what was (and is) intended (Soteriou 2013). When practical memory is fragmented, so is our conception of our agency. The vicissitudes of our hold on intention track the vicissitudes of our remembering what is to be done. The dynamics of practical amnesia can be mapped to the division between guidance and control. When I am acting, my intention sets attention to guide a response. So, in walking into the room, my intending to get a specific book sets perceptual attention to guide me through the door and into the room. Yet my intending must retain contact with its past, representing the whole action to be done. This is coherence in agency. If I forget, I attend to the room as I enter it, but then attention becomes unmoored from an intention temporarily forgotten. The background goal of action being lost, I am practically at sea. Think of how patterns of attention depend on intention when we retain our plan (Yarbus; Section 1.6) and how the pattern might meander when the agent is rudderless in amnesia. If my eyes luckily alight on the wanted book and I notice it, then I might recover my intention through fine-tuning: that is what I came for (Section 4.3). Note the shifting dynamics of an intention as exemplifying one’s being active toward a planned action. Since practical memory is needed to explain why one acts, the Continuity of Practical Memory applies to acting for a reason. Elizabeth Anscombe emphasized that intentional action is subject to a why-question: Why did you do that? The answer provides a reason for action. A failure of agency would occur if one had a reason but forgot what it was. The response, “I don’t know . . . I can’t remember . . . did I?” would be an odd response that would show that one had failed to retain the reason for action, the reason on which one acted. That would be another substantial deformation of agency’s coherence. Note that a failure to remember at tn what one did at tn-1, so a failure to answer why one acted, is compatible with one’s having intentionally acted at tn-1. So, an inability to answer the why-question need not entail that one did not previously intentionally act. The argument from amnesia underscores a necessary condition on intentional agency if it is to be coherent. The agent must be able to answer the question “Why?” drawing on her conception of her reasons, her goals, and the temporal and environmental context of her action. Intentional agency does not exhibit practical amnesia. Where it does, it is usually temporary, an annoying but brief losing track. One finds this happening a bit more as one ages, and it is a biologically normal deterioration of control. When amnesia is more excessive, as in dementia and Alzheimer’s disease, it marks a distressing, ultimately debilitating dissolution of agency’s coherence and, at the end, of agency itself. Coherence, one’s conception of one’s action, requires the Continuity of Practical Memory.

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4.3 Practical Fine-Tuning Intending-in-action is constituted by fine-tuning of practical memory in practical reasoning. The Continuity of Practical Memory entails a movement of mind. Practical memory is not tied to a moment in action’s time, but must update in keeping track of ongoing action. This involves the agent’s thinking about her action as she does it, grounded in her intending to act. Specifically, such thinking is part of continued practical reasoning that changes the content of the intention-in-action by fine-tuning it. Fine-tuning helps to narrow a necessary gap between intention and action (Section 1.8). The agent’s intention is abstract, leaving a one-many relation between her conception of the action intended and its possible expressions. As an intention cannot represent all the features that a concrete action exemplifies nor can it represent those features with sufficient determinacy, there will always be numerous concrete actions that can satisfy it. Arguably, when the number of solutions is very large, one is not in a position to execute the intention. A student might despair of constructing an argument because each potential premise leads to more thoughts, ballooning her action space. Or consider a mundane situation: If one intends to eat cake, there are many ways to do so. A necessary condition of satisfying the intention is that there is cake accessible to the agent, but merely intending to eat cake will not make cake appear. Until one’s intention is more finetuned, action does not engage. After an initial decision that presumptively answers the Selection Problem, intention must be further fine-tuned by deciding what kind of cake to eat. Answering this practical question leads to a more specific intention: one should eat chocolate cake. The new intention provides further constraints on action space, with some paths now ruled out as others are prioritized (goodbye fruit cake!). Closing the gap between intention and action involves the reduction of relevant paths in action space that satisfy the intention as the intention is fine-tuned. Means-end reasoning is one form of fine-tuning. We intend to eat cake but are not close to doing so unless there is cake. There would be if one baked a cake so, reflecting on this, one decides to bake a cake. This new intention brings us closer to action through figuring out further means to the end. Deciding to bake a chocolate cake furthers matters, with the agent now focusing on needed implements and ingredients, leading to further refinements in intention until an appropriate action is expressed. Another form of fine-tuning identifies a concrete target of the intended kind. Here’s an example expressed as a practical schema for targeting: 1. I intend to Φ on X. 2. This is an X. 3. I intend to Φ on this.

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The first intention might be insufficient for action if only because an X is not present. During what we can call the practical delay period, the plan in (1) is remembered (memory for work) which induces preparatory activity in respect of X, namely vigilance. (2) is given by perception. It is not enough, however, to perceive a target, for perception of it is compatible with an agent who fails to notice it and thus fails to act on it. In the visual domain, such failure to notice is inattentional blindness (Mack and Rock 1998). Without attention to X, the agent does not act on it. Normal agency involves target specification in intention, namely proceeding to (3) on the basis of (2). Such practical reasoning involves a transition that begins with (1) and (2) as inputs to a practical response in forming the intention (3). When this happens, (2) constitutes perceptual attention for practical reasoning. It informs response, namely (3). Now, with an intention to eat this cake, the agent is in a position to do so. Consider a case where the action occurs without the agent forming the intention to act on the target. That is, we have (1) and (2) but not (3): One intends to eat cake and eats this cake, but one does not intend to eat this cake. While not paradoxical, there is something off-kilter about this statement: I am eating this cake but I did not intend to eat this cake. This pattern is often the case in habitual actions, but in many non-habitual actions, this disconnect between action and intention would at least be embarrassing if not disturbing. These would be cases in which we are less in control than we expected to be. Formally, the eating of cake in the off-kilter situation is automatic. One finds oneself eating a piece of cake that one did not intend to eat even if, thankfully, in eating cake one satisfies one’s general intention (1)—unless one finds oneself eating the sort of cake one dislikes. As a chocolate cake fan who dislikes fruit cake, one might be chagrined to find oneself eating fruit cake. This would not be a case of causal deviance but an odd automaticity (cf. utilization behavior as a pathological action form; Archibald, Mateer, and Kerns 2001). The point is that normal agency often involves the transition to a fine-tuned intention that ensures that the action performed is as the agent conceives it. This illustrates a constant tug of war between automaticity and control. The offkilter situation suggests that normal agency often involves control with respect to a target. My eating this cake is in my control because eating it is what I intend. But there is plenty of targeting that is automatic, as Yarbus’ experiment demonstrates. When looking at a painting to remember the clothes worn by the subjects, I fixate on each one, often automatically. I do not necessarily update my intention to pick out each target of fixation at each time point. It is not paradoxical to say that I intend to examine the painting to remember the clothes worn by each subject but I did not intend to examine this subject at this time. I just did it automatically. This is why I do not remember each and every eye fixation, or object thereby fixated on. My intention does not update to every movement. That said, the relevant targeting occurs at a higher level of abstraction: I intend to probe this painting or these

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figures. Some targeting by intention is needed to engage action although much targeting can be automatic. Recall the notion of a direct intention (Section 1.8): an intention to Φ is direct if it can cognitively integrate with the action capacity of Φ-ing to yield action without further fine-tuning. Thus, an intention to eat this cake, to look at this painting, to examine these figures is enough to yield action. Otherwise, the intention is indirect. A goal of practical reasoning is to specify an intention that can be direct: no more thinking; time to act! Similar points arise for means-end reasoning. One cannot act to achieve an end if one has not realized what the necessary means are. Only when the means are recognized is the intention closer to engaging relevant action capacities. The practical schema for targeting combines two perspectives: the subjective, first-person perspective of the agent and the objective, third-person perspective of the psychologist. The subjective perspective emphasizes the contents entertained by the agent: 1. Φ on X. 2. This is an X. 3. Φ on this. The psychological mode of (1) and (3) is intention. In (2), the agent perceives, thinks, or remembers. The result of the intention’s interaction with how the agent takes X, say in perceiving or remembering it in (2), results in a fine-tuned intention so long as she notices the X and hence attends to it to inform decision. As this first-person schema is ambiguous about the relevant psychological modes, we can shift to the psychologist’s description: 1. S intends to Φ on X. 2. S perceives/remembers that this is an X. 3. S intends to Φ on this. I shall begin with the third-person schema and return to the first-person perspective when discussing access to action based on intention. The practical schema shows a temporal transition in intention, say from a proximal intention to act soon to an intention to act now (Section 4.7). Consider: 1. S intends to Φ on X at t. 2. S perceives/recognizes that t is now. 3. S intends to Φ on X now [so S Φs, or straightaway she Φs]. Given the underlying mnemonic capacities, the schema also reflects a transition in practical remembering, namely an updating of one’s memory of what is to be

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done. This means that there is a specific movement of mind that occurs in intentional action, a way that we are sensitive to changes in the context of action as a way to maintain Continuity in Practical Memory.

4.4 Fine-Tuning as Practical Memory at Work Fine-tuning is practical memory at work, its dynamics revealed in the dynamics of working memory. Thinking along the practical schema can be automatic, for there need not be an intention to reason practically in this way, though there can be. Here is an empirical hypothesis: typically, we automatically update our intentions. We find ourselves with new intentions that are more determinate formulations of what we intend. This aids solving the Selection Problem by sharpening our conception of what to do. Automatic reasoning remains reasoning. We do this all the time, and we do not notice. I have been leveraging empirical work to test and enrich the theory of intention as memory. In Chapter 3, I argued that research on working memory uncovers the dynamics of intention in biasing attention and action. That work also illuminates automatic fine-tuning of intention. Consider the retro-cue paradigm (cf. discussion of this paradigm in the phenomenal overflow debate; Block 2008).² Given that working memory is set by task instructions, the lessons gleaned here generalize to other cases. The paradigm illuminates the dynamics of fine-tuning intention, dynamics illustrated in most experimental tasks. The retro-cue paradigm was first demonstrated by Ivan Griffin and Kia Nobre (2003) who had subjects retain a sample array of four colored targets around a fixation point for later test (for an overview, see Souza and Oberauer 2016). After the sample array was extinguished, the subject was presented after a delay with a test target and reported whether it matched one of the four original samples. The subject needed to maintain the sample in memory to guide subsequent response. Two conditions are relevant: a retro-cue condition where a cue is presented during the delay period that identifies one of the remembered array items as test relevant and a control condition with either a neutral cue or no cue. Performance in respect of accuracy and reaction time is improved when a retro-cue appears during the delay period (up to 9.6 seconds into the delay; Astle, Nobre, and Scerif 2012). This experiment has a similar structure to an oft-used paradigm for probing visual spatial attention, namely spatial cueing (Posner 1980; for discussion, see Wu 2014). In spatial cueing, a cue at a location prior to when a target appears there facilitates performance regarding that target. Since retro-cuing is a memory task, we can fit it to the memory conduit (Section 3.2). For the moment I leave out the intention, as its dynamics during

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this process will be discussed in the next section. The intentional action in the experiment begins with a presented stimulus array being remembered on the basis of visually attending to it, so encoding it:

Once memory is established, the array must be maintained during the delay period, given an intention to perform the task on it:

When the test stimulus is presented, we have a more complex response that depends on deploying memory of the array and the perception of the test stimulus, an interaction between mnemonic attention to the remembered array and visual attention to the new test item to inform response:

Note that the response depends on perceptual and cognitive attention. This conduit corresponds to the no-cue control condition. Now, redo the experiment with a retro cue. In this case, the cue triggers a reconfiguration of memory to prioritize the cued array item:

This is the crucial step, an updating of memory, so, a type of thinking. Given this structure, it is reasonable to predict a retro-cue benefit when we compare memory in the control condition versus the retro-cue configuration:

With a valid cue, the item to be tested is already the mnemonically prioritized item, so comparison should be more efficient, faster, and/or more accurate.³ For example, in intending to act on the test item, one prepares to attend to it (vigilance). This can improve performance, and the behavioral data supports this.

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On standard models of working memory, retro-cueing modifies the working memory store. Nicholas Myers et al. take the cue to reformat “one out of several currently held memories to guide the next action” (Myers, Stokes, and Nobre 2017, 449) in that “cueing a memory leads to reorganization of an outputoriented circuit that can then drive behavior faster and more accurately” (456), something akin to a change in what psychologists call task set (see Chatham and Badre 2013 on “output gating” in working memory). Such reformatting helps solve the Selection Problem, the cued item selectively informing the correct response. Here’s the bridge for our purposes: this perception-memory task probes fine-tuning of intention in that task. Return to the subject’s perspective, for the subject intends to act as instructed. Yet task instructions leave details underspecified, so intentions are initially general. Before the experiment begins, subjects are told to retain a subsequent array of colors in memory for later test. Subjects form a general intention to remember an array but the constituents of the array are as yet unknown. The intention cannot be direct for there is nothing to engage with. As the experiment begins, the presentation of an actual array determines what must be done, namely to remember this array (cf. eating this cake). The agent intends to act on it. The intention is thereby fine-tuned. A practical schema describes the encoding stage of the retro-cue paradigm: 1. Subject intends to encode an array for later test (as per task instructions). 2. Subject perceives this sample array. 3. Subject intends to encode this array for later test. Now, continuing the last intention, we introduce the retro-cue 4. Subject intends to remember this array for later test. 5. Subject perceives the retro cue indicating this (cued) remembered element. 6. Subject intends to remember this element of the array for later test. (4) to (6) of the practical schema parallels the memory conduit:

Since the memory at issue is practical memory (memory for work), we can rewrite the practical schema horizontally as follows:

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The shift from a general to a specific intention involves an alteration in practical memory analogous to manipulation of memoranda as when one remembers a sequence of numbers and is asked to add one to each (e.g. remembering 14512 and transforming it to 25623). In retro-cuing, the shift in intention is an alteration in memory as part of practical remembering of the action to be performed. This involves fine-tuning one’s conception of the action. Fine-tuning is a familiar form of practical reasoning drawing on cognitive and perceptual attention. The dynamics of intending are not restricted to the retro-cue paradigm. In laboratory settings, task instructions fix intention, thereby setting memory for work. These intentions are fine-tuned as new information (new stimuli) are presented to the subject who then adapts her response. These cases mimic what happens in mundane situations as we make plans and alter our intentions in response to the mutable world, thereby adapting our responses. This fine-tuning involves the exercise of practical capacities as in the retro-cue paradigm and in our boiling pot example though, alas, there too late. The agent’s intention-in-action develops during action, sensitive to contextual alterations, through the exercise of practical reasoning. In the retro-cue paradigm, one does not need to re-form a general intention to act on an array each time a new experimental trial starts. The subject does not forget the point of the experiment with each trial, requiring a constant reminder. Rather, subjects remember what to do, and the general intention remains in force, informing each practical fine-tuning given a new test array and cue. What is lost is the fine-tuned intention once it is executed. The general intention is maintained throughout the experiment. Though each fine-tuned content is prioritized in being expressed in action, the general content remains in the background until a new trial begins, initiating a new bout of fine-tuning. If one forgets what one is doing, loses track of the general intention, the experiment might be ruined. Again, as in the laboratory so in life. Consider your having decided to go to the hardware store to purchase a hammer. You have a general intention to make a purchase but do not know where the nearest store is. Using your smartphone, one pops up in your search. You fine-tune your intention to go to that store. When you arrive, the doors are locked and your intention is frustrated. What now? The fine-tuned action intended cannot be satisfied, so one forgets it (“Forget it!” one might say to oneself). Yet one is not left dumbfounded, for one’s intending to purchase a hammer remains in force. This informs renewed practical reasoning leading to searching for a new store. Intending to act involves a continued sensitivity to what one is doing, with intention constantly fine-tuned in response to changes during action. Finetuned intentions sharpen control through narrowing solutions to the Selection Problem. The dynamics of working memory reveal the dynamics of intention in practical reasoning.⁴

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4.5 The Dynamics of Thinking about Action, in Action As the agent acts, keeping track of what she is doing involves the exercise of practical reasoning as part of her developing her intending to act as she acts. Jennifer Hornsby noted: The abstract view of practical reasoning obscures the situation-specific character of the knowledge one has when one is actually doing something. And it makes no allowance for the fact that knowledge is acquired in the course of action. A person who is determined upon an end can set out being sure that she will find a way to the next step even as she carries on. She is not in the first place a reasoner who devises a plan, then afterwards an agent who carries it out. She is at every stage a thinking agent. (Hornsby 2013, 16)

The Continuity of Practical Memory entails that we must keep track of action’s past to make action’s present intelligible if only in remembering what one had intended to do. This enables us to answer the question, “Why?” During action, intention is modified by practical reasoning, so it keeps up with action. In such fine-tuning, one actively thinks about what one is doing. Consequently, intentional action involves two tracks of agency, one that exemplifies control because it is biased by intention, the other a typically automatic modification of practical memory that tracks the action it controls. As I shall argue, these paths are stitched together by practical reasoning and bias. Consider a mental action where the agent takes things as p₀, say she thinks that p₀ and quickly responds by thinking pn. So, she judges that pn on the basis of remembering that p₀. Such actions are seemingly over in an instant.

For example, one thinks, “What is 2 + 3?” and straightaway responds, “5!” The thought contents just diagrammed specify what the subject is cognitively attending to, first the question, then the answer, the mind’s taking possession of a train of thought. As this action is quick, access to it will likely be retrospective. To uncover access to what we are presently doing, consider extended thinking:

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As written, the sequence fits explicit deliberation about propositions such as in inference but also transitions that are seemingly seamless, say the mental rotation of an object one imagines. For purposes of analysis, take any mental action that you can describe as you do it, by access! That description is the basis of specifying a content conduit as above. I set aside non-propositional, i.e. imagistic, thinking for another time, and focus on thinking that involves concepts. The Continuity of Practical Memory requires that for a given thought px to be intelligible as part of one’s action, it must be conceptualized as being a response to previous thoughts, to what the subject aims to do or to why she is acting. The agent must do so in light of practical memory that represents the relevant past so as to make sense of present thinking. If amnesia sets in (Section 4.2), the agent’s current thought cannot be taken as something concluded, arrived at, or considered in light of previous thoughts. When the agent thinks intentionally, the agent intends to think about a topic T, say what cake to eat, how to solve a conundrum, or where to vacation. T identifies the agent’s initial conceptualization of her action: to consider (think about; deal with) topic T. Here, as ever, there is a gap between intention’s abstract representation of action and the evinced action’s particularity that necessitates automaticity (Section 1.4). For example, the intention to reflect on T is satisfied by thinking some proposition p that is relevant to T. This might be a single proposition as when one intends to remember what one had for dinner last night and I had fish comes to mind, or it might be a sequence of propositions as when one intends to figure out how to bake devil’s food cake and spins forth a plan. The result of the intention to think about T is a specific content conduit, represented as a sequence of T-relevant thoughts px:

The initial generation of each thought px will be automatic as that initiation was not intended. Thinking is diverse and complicated. For example, I want to allow for thinking that involves content in imagistic or iconic formats, say in spatial reasoning. Again, I simplify by focusing on propositional thinking that is linear, logically sequential, progressive, and steadfastly on topic. The automaticity of thought arises because the concepts exercised in the conduit are not identical to those exercised in conceptualizing the topic T in intention. Accordingly, given finite representational capacities in intention, there will always be automaticity in thought. The first step of pondering T, namely thinking that p₀, is by definition automatic since p₀ is not represented in the intention to think about T. Now, consider

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the set of T-relevant propositions that the agent can think: {px . . . p₀ . . . }. It must be automatic which T-relevant thought comes to mind since one doesn’t intend to think about p₀ unless p₀ = T. The intention to think about T biases T-relevant propositions over the T-irrelevant propositions, {qx . . . q₀ . . . }. This is the cognitive parallel to goal-relevant automaticity of eye movement in Yarbus’s experiment (Section 1.6): one intends to look at figures but the eye lands automatically on a specific figure, usually a relevant one (but not always). Intention biases taskrelevant attention. As p₀ comes to mind automatically, it need not be taken as relevant by the subject. If the thought that arises is not task relevant, say q₀, then mind wandering might ensue (Smallwood and Schooler 2006; Christoff et al. 2016). What comes to mind is a function of disparate biases (Section 5.6). Further, the proposition, if relevant, must not be merely entertained. The subject must notice its relevance to T, and this entails that attention must be properly directed. Recall the agent who intends to eat cake. She might see a piece of devil’s food cake, yet she will not eat it until she notices it. She must recognize its relevance to her intention and then target it. Indeed, she might be looking right at the cake, see it, yet be inattentionally blind to it (see Mack and Rock 1998, ch. 4, fig. 4.10c, for a case of inattentional blindness to a target at fixation). Her intention and perceptual experience provide the first two steps in the practical schema: 1. Agent A intends to eat some cake. 2. A perceives this piece of cake. Nothing in just (1) and (2) guarantees fine-tuning. Only on noticing the cake as something to eat leads to 3. A intends to eat this piece of cake. Without attention to the cake, the agent never fine-tunes her intention. She will not then intentionally eat cake. The same point arises in this schema: 1. I intend to think about T. 2. I think about p₀. Failure to notice can occur. Without properly attending to p₀, one can stall at (2) without proceeding to: 3. I intend to think about T through p₀.

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Note that this is only a frame. How we specify the content of intention will differ depending on the values for T and p₀. The present point is that in thinking about p₀ at (2), nothing yet shows that the agent is thinking about T by thinking about p₀ as opposed to beginning a bout of mind wandering that fortuitously begins with a T-relevant thought, one that the agent doesn’t intentionally take up because of a failure to notice its relevance (this is like the eye’s landing on one’s keys as one looks for them, and yet not finding them). As in noticing the cake to eat it, when the agent realizes the thought’s relevance, fine-tuning identifies it as a target of intention. Indeed, given the Continuity of Practical Memory, the transition to fine-tuning must occur at appropriate points else the subject will think p₀ without remembering the point, like a subject who enters a room she intended to enter yet suddenly forgets why she is there. Fine-tuning is basic practical reasoning, moving from a general intention to think about T to a fine-tuned intention to think about T through thinking p₀ as follows (recall the last form of the memory conduit; Section 3.2):

For example, I intend to think about which cake would be best to eat and the thought pops into my head that chocolate cake is particularly delicious (I have my biases; Chapter 5). Chocolate being delicious is relevant to deciding what cake to eat, but to satisfy my intention, I must notice the relevance of that thought to my intention to decide, not merely think it, just as I have to visually notice the cake, not merely see it, to satisfy an intention to eat cake. The transition is captured by the practical schema where my thinking p₀, in informing the subsequent intention, counts as cognitively selecting p₀ for fine-tuning and, so, cognitively attending to it. We can make attention clear in this way: 1. I intend to think about T. 2. I think about p₀. 3. I intend to think about T through p₀. Thinking in (2) constitutes task-relevant cognitive attention when it informs (3). The updating of intention involves practical reasoning captured in the practical schema. The practical schema captures the inferential form of the action of fine-tuning. This action has a familiar structure. The first step in thinking about T can be diagrammed as follows:

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Figure 4.1 An intention to ponder topic T leads to the automatic occurrence of a thought p₀ that is semantically relevant to T. Should the agent notice that this thought is relevant, the agent will update her intention. Otherwise, this thought might just be the first stage in mind wandering.

The intention biases T-relevant thoughts beginning with setting vigilance to thoughts in the task-relevant set {px . . . p₀ . . . }, though not necessarily with equal vigilance (see Section 5.7 and the example of the all-male conference lineup). Cognitively accessing p₀ occurs automatically, influenced by the intention to think about T, but it can result from other biases (Chapter 5). If p₀ is cognitively maintained, held in mind as diagrammed (dark solid arrow, Figure 4.1), this establishes (2) in the practical schema, one’s thinking p₀. That is, the agent has the intention to think about T and is pondering p₀ and not irrelevant thoughts such as q₀. When the agent further recognizes that p₀ is relevant to T, the intention is updated via an exercise of fine-tuning where the intention is one of two inputs (premises) in practical inference, again the practical schema.

Figure 4.2 The agent notices the relevance of p₀ for T and updates her intention in practical inference. This inferential action involves two inputs, two instances of cognitive attention, leading to a fine-tuned intention. This figure continues Figure 4.1 as the intention and thought lead to fine-tuning. As depicted, this updating is automatic as defined (Section 1.4). Revisit the Buridan Space (Section 1.5) for a related structure.

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Figure 4.2 shows the transition from (2) to (3) in the practical schema, the formation of a more specific intention. Accordingly, the agent notices p₀ as part of thinking about T, recognizing that p₀ is relevant. At this point, the continued pondering of p₀ is intentional. One continues to think p₀ under one’s control (by definition).⁵ Practical reasoning imposes control over thought. Indeed, the finetuned intention maintains the thought (as in working memory), keeping the agent on topic (as in steadfastness). This is a familiar geometry corresponding to one’s thinking p₀ as part of the cognitive action of pondering topic T (cf. working memory maintenance; Section 3.2, Figure 3.2).

Figure 4.3 The agent continues to think p₀ for a stretch of time which is sustained by her intention to so think, an intention that itself is maintained over time.

Note that the conceptual capacities needed to cognize p₀ are exercised both in the thought and in the intention conceived of as distinct attitudes. Can two states jointly instantiate the same resource? The idea of shared representational resources is already present in classical accounts of action. A desire to make one’s partner happy and the belief that by doing F, one’s partner will be happy deploy the same representational capacity for happiness. The belief and desire jointly exercise overlapping representational resources in bringing about the action they rationalize. Shared resources explain one way of closing the gap in solving the Selection Problem. There are many ways we can ponder T. By intending to focus on proposition p₀ to ponder T, we make more precise our plan of cognitive attack. Deploying specific concepts to think p₀, we narrow the relevant capacities biased by the agent’s intention to act. Thinking p₀ functions to stabilize and focus the agent’s thinking as well as to bias specific representational capacities as a cue or prompt for further thought, say other members of the set of T-relevant thoughts (cf. premises as cues; Section 6.2). There are many ways that p₀ can be linked to p₁. For example, in the set of T-relevant propositions, {pX . . . p₀, p₁ . . . }, certain claims might be semantically associated in the sense that the activation of one propositional representation tends to activate another that is semantically related, perhaps due to shared conceptual resources or previous co-association. For example, propositions that share the same concept, say p₀ and p1, and which are remembered by the subject can be jointly activated when one of those thoughts is first entertained. Such

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activation would be automatic as we have defined. So, in thinking that p₀, the agent might be more likely to think p₁ the thinking of which involves the same concept, say p₀ = a is F and p₁ = a is G (see Section 6.2 for a similar case in modus ponens). We can indicate this as

Figure 4.4 The agent sustains her thinking that p₀ because she so intends. The intention’s maintenance in memory sustains her thinking p₀. The agent then automatically comes to think p₁ for that thought is not intended. p₁ might automatically arise due to a semantic association with p₀.

In intentionally pondering T, noticing p₀ informs not just the updating of practical memory but the agent’s continued thinking as she intended, namely in coming to think p₁ given p₀. Note that thinking p₁ is automatic but it is guided by p₀ in that the thoughts share the same concepts or are semantically related so that a coherent, that is T-relevant, train of thought is established. As before, the agent must register the relevance of p₁ for pondering T. She must notice it as relevant. Again, this is not guaranteed since the progression to thinking p₁ is automatic and not controlled. What is controlled at this step is the agent’s thinking about T by pondering p₀. When the agent recognizes the relevance of p₁, she updates her intention treating p₁ as relevant to considering T. Now, p₁ becomes the target of intention and controlled. The process continues in this way for subsequent T-relevant thoughts. Not all thoughts entertained in pondering T lead to fine-tuning intention. Such thoughts proceed automatically, perhaps even a bit of task-relevant mind wandering. Two salient paths emerge. The first is an automatic train of thought that happens to be task relevant, here constituted by a move to p₀₁ then to p₀₂. We can insert this automatic train of thought in between the two thoughts modeled in Figure 4.4:

The intervening thoughts, p₀₁ and p₀₂, are not updated so the mind is carried along a task-relevant train of thinking about T. Notice that if the agent does not

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notice the relevance of these thoughts, then the project of T will not be completed even if this mental meandering is relevant to the task. She just keeps cognitively going. The agent who comes back to the task will fine-tune her intention to ponder p₁ for T. Accordingly, she will not necessarily remember p₀₁ or p₀₂, at least in intention. Think of all the intermediate steps in your reasoning that you cannot recall even as you remember central inferential waypoints. A second possibility often happens, namely that thinking goes off the rails. Thus

The cognitive project stands on the precipice beginning at q₀₁, for will the agent notice that her thinking is off topic? Can she recover p0, ex hypothesi under her control given prior fine-tuning, and get back on topic? “What was I thinking?” she might ask herself just as one might ask, “What were we talking about?” to get back on topic. If our thinker recalls her intention to think T through p₀, she can get back on track. But if amnesia persists, she is cognitively lost. How much of the past is retained in practical remembering is an empirical matter that concerns the mnemonic capacity limitations of each subject in this action context. Some of the limitations of steadfastness are directly tied to limitations in working memory which reflect our abilities to keep up with our actions in practical remembering (review Section 3.6 and measures of working memory capacity). After many steps in our cogitation, we might not remember in intention the thought six steps prior, but we must have some retention of the past to maintain coherence in planned action. To ensure coherence in the Continuity of Practical Memory, various thoughts that are entertained must be in the focus of the agent’s practical remembering (intending), and this requires fine-tuning and maintenance, precisely the dynamics of working memory that is tuned to a changing context. Practical memory must keep up with action. That said, the vicissitudes of memory guarantee that as imperfect cognitive beings, we can lose track of what we are doing and thereby lose control. Updating intention is generally automatic, happening not because the agent intends to do so. It is necessary for normal agency to maintain the Continuity of Practical Memory. In the formal representation of the practical schema, the two actions, the first-level thinking of propositions and the updating of practical memory in intention, are stitched together. Accordingly, we can rewrite the process of fine-tuning as a practical schema: 1. 2. 3. 4.

(I intend to) draw the appropriate consequence from p₀ for T. (I think) p₁. (I intend to) draw the appropriate consequence from p₁ given p₀ for T. (I think) p₂.

144 5. 6. 7. 8.

    (I intend to) draw the appropriate consequence from p₂ given p₁ for T. Etc. Etc. (I think) pn.

Within the schema, the first-level intentional thinking and the second-level practical remembering are given in even and odd numbered premises respectively, delineating two parallel paths of agency. The reference to previous propositions captures the orientation to the past that makes the present step intelligible. The practical schema and the action structure show in different ways how the two lines of thought that we have diagrammed are inextricably linked. That there are two actions going on might seem an odd and onerous multitasking. Here, it is important to remember the motivation for postulating working memory in light of maintaining plans (Miller et al. 1960). For the plans the agent maintains to constitute agentive control, they must remain sensitive to the changes the agent brings about on pain of her losing track of what she is doing. The schema mirrors, as it must, actual movements of mind, two distinct trains of thought that are nevertheless linked by practical reasoning and intentional bias. The empirical issue is not whether we do such updating but how well we do it (Section 3.4 on complex span as multi-tasking and control through intention).

4.6 First-Personal Access to Intentional Action Intending-in-action maintains distinctive, authoritative, and nonperceptual access to action through practical reasoning. Philosophers have claimed that agents have knowledge of their intentional actions that is non-observational (Anscombe 1957; Hampshire 1959). Further, Anscombe emphasized that knowledge of intentional action is fundamentally practical. These are compelling claims, but I am unsure how to understand and indeed adjudicate them. Accordingly, I focus on explicating a shared assumption relevant to such claims: if an agent, asked why they are doing Φ, responds with, “Am I? I didn’t realize that I was,” this is evidence that they do not know, at that time, that they are Φ-ing or that they have temporarily forgotten (“Am I? Oh, I forgot I was doing so.”). On the flip side, those who know that they are Φ-ing intentionally can answer the question, “Why?,” in the standard ways. As answering the whyquestion is an intentional action, it must reflect the Continuity, or failure, of Practical Memory, so it reflects the agent’s access to her intention. My goal in this section focuses on explaining this common ground regarding access: that the agent’s reliably accurate reporting (or her judging, taking it to be the case, etc.) that she is Φ-ing bears the conjunction of familiar features attributed to practical

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knowledge. Such accessing is non-observational, authoritative, and distinctively first-personal. The results of access provide evidence of knowledge, and accessing what we are doing is an intentional action we can explain. The structure of action shows that these features co-occur because the agent’s intention in action is the agent’s cognitive attention to her action that informs her report of what she is doing. Access draws on practical remembering, namely the updating of intention by fine-tuning. An agent keeps track of her movements of mind, in intention, by continued practical reasoning, and when she reports the content of her intention, she can report with reliable accuracy what she is doing. Behind her practical access is automatic practical reasoning in fine-tuning. Through the course of her acting, the agent is thinking about what she is doing as she does it. She need only access this thought, reporting it, to track her action. The access she evinces in her report and the corresponding judgment are based on her intention-in-action as input. Practical cognitive access is (a) authoritative, (b) non-perceptual yet attentional, and (c) distinctively first-personal. Intention provides distinctively first-personal access in that only the agent can cognitively attend to her action through her intention. Since an external observer does not share the agent’s mind, the observer cannot draw on the agent’s intention-in-action to constitute the observer’s cognitive attention to what the agent is doing. That the agent can do so, her intention constituting her cognitive attention in her accessing action, is an entailment of the economical theory of attention defended in Chapter 2. In contrast, the observer’s access is based on perception, either through observation or testimony. Given this asymmetry, the agent’s cognitive access is distinctively first-personal and nonobservational. Further, access to action based on intention-in-action is privileged in cases where the agent is competent to bring about her Φ-ing because she intends to do so. That is, she knows how to do so. In such cases, when she acts on the intention she generally succeeds. Accordingly reporting what she is doing by accessing her intention-in-action will be reliably accurate. As the form of the action, tied to the path in action space that is expressed, is fixed by her intention, the agent’s access does not face the challenge of the external observer who must sift through perceptible features that are consistent with many types of actions the agent could be doing (cf. the problem of underdetermination of theory by data; Section 5.3). What the agent does intentionally, what she is in control of, is set by the agent’s intention. The distinctive properties attributed to an agent’s access to her actions fall directly out of the structure of access as an action. No special explanation of such properties, at least in their basic form, is needed, for they emerge in solving the Selection Problem. Cognitive access to mental action is a type of introspection (Chapter 7). The discussion of practical fine-tuning that keeps track of the agent’s action, as necessitated by the requirement of the Continuity of Practical Memory, provides

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the structure of the agent’s distinctive access to her action. The agent’s intention serves as the basis of her access to what she is doing, the input to her report and judgment, and satisfies the distinctive properties noted earlier. When she accesses her intention for report or judgment, the intention constitutes the agent’s cognitive attention to her action that informs her report. To return to our engineer (Section 2.1), for the designed robot to access its action, it need only read out its memory of the current task, that memory, the analog of intention, constituting access for report.

Figure 4.5 The subject reports what she is intending, and so reports what she is doing, where her intention constitutes cognitive attention to her action.

The account I have provided of distinctive access to action is in one sense receptive (thanks to Sarah Paul for this observation). Receptivity is present because of practical updating which, as I noted, can be based on thought and perception, the canonical form of receptivity. So, intention is receptive through practical reasoning, and in that sense “keeps up” with action. Acknowledging this does not contravene the core idea that attention to action is non-perceptual, mediated through intention constituting the agent’s cognitive attention to her action. Even if the content of intention is updated receptively through perception, the psychological structure distinguishes between the agent who can access her action through intention as cognitive attention to action and the observer who can only do so through observation as perceptual attention to action. That said, the focus on mental action shows that receptivity is part of keeping time with one’s action. If that is surprising, then it is a discovery gained by explicating the psychology of mental action, not armchair reflection on what it is to access action. Recall that there is a gap between intention and execution that entails the automaticity of action. In mental action, say in thinking, the specific thought (content) that we arrive at is typically not represented in the intention. Yet the Continuity of Practical Memory requires that we retain the thought that automatically answers our intention, and so our intention comes to encompass that thought as well. Seeing this dynamic relies on technical notions of automaticity and control (Section 1.4). In thinking, we are in control (productivity), but

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the automaticity of thought plays a necessary role, and certain automatic features are then brought under control through practical fine-tuning (receptivity). We maintain control as we think by being both productive and receptive. Intentional mental action shows that keeping time with action is keeping up with action. Yet if receptivity is consistent with privileged access, this consistency is not abrogated by perceptual receptivity, so long as the agent’s access to action remains based on her intending to act, her remembering the form of her action under which it is intentional. Access is non-observational but practical reasoning is not necessarily non-observational. By rooting the basis of privileged access in intention, hence in cognitive attention, we make space for perception to inform intention without privileged access to our action becoming observational access to it.⁶ One striking consequence of such access in mental action is that during intentional mental action, two parallel streams inform each other. The first-level action done with an intention not only exhibits the structure of guidance at that level, but it feeds into the practical fine-tuning of intention-in-action. The second level tracks the first-level action as part of the agent’s continued practical reasoning that sustains practical remembering to keep track of action and is the basis of the agent’s control. This control founds our distinctive access to action.

4.7 On Keeping Time with Action By practical reasoning, the agent keeps time with her action through her intending to so act. As the agent acts, she occupies a temporal perspective rooted in her present intention-in-action. This perspective is sensitive to the here and now of agency. Regarding the burnt cooking—yes again, it was me—if I had more foresight, I would have recognized that I always get caught up in emails. I should have set a timer which would tell me that it is time to check the pot. The alarm’s ringing would indicate that now is the time to tend to the pot. Timers, used properly, are effective signals that it is now time to act. Prospective memory researchers might speak of the alarm as a way to direct attention back to the intention, but this imposes a faulty spotlight model of attention. Even in the thick of doing emails, I can maintain an intention to cook and so I remain cognitively directed at my cooking—active practical memory—even if my control is less active as seen by reduced vigilance (cf. the vigilance decrement; Section 3.6). Still, I can remain vigilant in respect of the time to act. When the alarm goes off, vigilance for the “now,” namely the time to act, becomes attention that guides my getting up and turning down the burner. I respond to the alarm as indicating that I must act now,

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and this because I move to the stove in response to it. In remembering what to do, my vigilance and attention are directed at the world. Of course, we don’t just use timers. The world provides a variety of proxies that we take advantage of to keep time. Perhaps I had the foresight to sit in the kitchen to do my emails. In that context, hearing the pot boiling does better than the timer in telling me that, again, now is the time to act. On hearing the pot boiling, I get up and turn down the burner. The world functions as my timer when I know what I am doing, for I am vigilant to relevant aspects of it that inform my ability to act. Consider also the priority of such a sound for an experienced cook versus someone boiling a pot of food for the first time. Sitting in the kitchen does not ensure that I act at the right time. My attention might not be grabbed by the sound of the boiling, a failure of vigilance, inattentional blindness/deafness, or distraction. I must notice the time and, when I do, I fine-tune my intention to the here and now. Intention leads to vigilance that prioritizes certain action-relevant intentional capacities over others. In the case of cooking, the relevant prioritization is the sound of the boiling, for that is one of the steps in my cooking, and my knowledge of how to cook influences my attentional orientation (cf. Section 6.6 on knowing how to reason). Learning how to do things sets appropriate biases that shift attention to better guide my actions (Chapter 5). My intention to cook readies me, and on hearing the sound, I respond guided by my so hearing it, given my intending to cook. I act at the right time. Being sensitive in this way to time, I come to intend to act at that time. My keeping track of time need not be with a time piece but in light of various temporal proxies to which I am attuned. We can explain certain transitions from a prior intending directed toward the near future to act then to an intention-in-action to act now. Our ability to act in light of our intending to do so prepares us to respond to the relevant target such that our taking it in guides our response. Time, as marked by the opportunity to act, is not something that I am insensitive to. In being sensitive to temporal markers or proxies by beginning to act in response to them, I show my grasp that now, not later, is the time to act. My conception of my action as I begin it is a conception of acting now though I need not conceive of time in explicitly temporal terms. It is enough to respond to the boiling pot to count as acting at the appropriate time.⁷ This use of the world, broadly construed, as a marker of time and as informing us about time can also explain the cessation of intention. We do, after all, stop acting at the right time, well, often enough. Consider again the seemingly instantaneous mental act that I noted earlier:

I pose a question to myself and an answer comes to mind, completing my thinking. Just as an initial occurrence signals the time to begin acting, another

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occurrence signals the time to end, here my recognizing an achievement, my having answered the question. For if I intend to answer the question, then my intention is fulfilled when I answer it. When I think pn my question is answered and so long as I know what I am doing—this varies with the clarity of the question in my own mind—I thereby recognize my completing the task. In concluding my thought, I know that I am done. This temporal awareness through awareness of the achievement is marked by what I would report: I am done; I have come up with the answer. I can be mistaken. For example, I might come up with the wrong answer though think it right. In this case, there is an event that I wrongly take to be an achievement, so I am wrong about the time to stop. Another mistake is to fail to see that I have achieved what I set out to do. The answer might be staring me straight in the face, for I have drawn the right conclusion, and yet a failure to recognize this leads me to go on thinking, searching for an answer. Here, I fail to recognize that now is the time to stop acting. This is like failing to see a piece of cake when I intend to eat cake. We speak of an agent’s knowing when the job is done, and this reflects her experience with actions of that kind, her recognition of what would count as that action’s fulfillment. This is a recognitional capacity critical to skill, to knowing how to act in the intended way. It is typically learned. What then of my intention when its job is done? Practical remembering aims to get action done at some future time, to impose a form on the agent’s action throughout its life until it reaches its appropriate end (if it does). We do not always recognize that we are done, but generally we do, and when we do, the content of practical memory ceases to be fine-tuned for action. The maintenance of a conception of action in memory is now of an action that is completed. At that moment, the intention is no longer a practical memory but a memory whose content is now of a rapidly receding past, kept in mind for the long term.

4.8 Taking Stock This chapter completes the core of the theory of action. I have focused on acting with an intention that is articulated against the Selection Problem to which the agent’s intending crucially provides a solution. The structure of action, arrived at a priori, is demonstrably instantiated in our biology. The previous philosophical arguments are to be seen as part of the project of cognitive science writ large, a science that aims to understand how the mind works, one that substantively integrates philosophical argument and empirical work. Philosophers and cognitive scientists can draw productively on the theory. In the final chapters, I will apply the structure to three important phenomena in philosophical psychology and show how each exemplifies a distinctive form of attention. Two issues concern actions central to philosophizing: the nature

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of reasoning and of introspecting. The next chapter discusses socially and epistemically relevant forms of bias that characterize all practice.

Notes 1. Action through a pause and pausing action: Is the subject still cooking as he sits there doing emails? He is no longer in the kitchen stirring, even looking, at the pot. Well, maybe he is cooking, maybe not. We can give a precise sense to (a) when the agent continues to act through a pause such as when waiting is part of the act and (b) stopping by pausing action (“I’ll do it tomorrow”). We have acting through a pause in the cooking case when the intention to act maintains cooking appropriate vigilance through the pause. This means that the agent maintains a propensity to attend to relevant changes such as the boiling even as he does something else. He will notice the boiling. The problem with the case discussed in the text is that the agent has forgotten what he is doing, getting caught up in another intentional activity, leading to a vigilance decrement and failure of sustained attention to task (Section 3.6). This reflects the challenges of working memory executive function in dual task situations and goal neglect as a type of amnesia. The cook’s intention is no longer active and he fails to be vigilant. As a result, he only returns to his intention when a salient signal grabs his attention. When temporary amnesia sets in, the agent has just inadvertently paused, namely stopped cooking. Of course, the agent could have intentionally paused by turning off the stove to do the emails; and, here, the agent can properly forget, for now, the cooking project until later (“I don’t have to think about the pot. I’ll cook later as I have other tasks”). The intention then becomes what psychologists speak of as prospective memory, and a crucial question in that literature is how to ensure that the intention gets reactivated when it is “backburnered” (ok, sorry, couldn’t resist). Here, my concern is not to cohere with any folk psychological intuitions about when action stops during a pause, but to set a fairly precise account of two ways of pausing that link to remembering and forgetting. These are meant as suggestive remarks, not a full-blown theory of pausing. 2. A second relevant experimental paradigm: The points that follow can also be made using the so-called modified Sternberg Paradigm (Oberauer 2001). In this case, two distinct arrays are presented for memory, with one later being cued as not task relevant. The paradigm is used to investigate the removal of items in working memory with a central question concerning whether the removal is active or passive (for a recent overview, see Rhodes and Cowan 2018). Certainly, the fine-tuning of an intention means that in forming an intention with a narrower focus to one of the two arrays, what is guiding action is also narrower than the original representation of two stimuli. So, there is a change in working memory as well as a change in intention narrowed to select one of the two arrays as target. The fate of the irrelevant array raises interesting issues about the time course of these changes, and in that sense the time course of shifts in fine-tuning intention. Notice that the modified Sternberg Paradigm is a special instance of keeping two tasks in mind, and then discarding one task on the basis of further information. The basic dynamics of this movement is the same in the

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experimental and in the mundane case. The relevant intentions are fine-tuned. We shall also see narrowing of cognitive attention in deductive reasoning (Chapter 6). 3. Underlying processing of retro-cue based prioritization: Alessandra Souza and Klaus Oberauer (2016) canvas the various options for understanding how the cued item in memory is prioritized. Based on their assessment of the extant evidence, removal of uncued items from working memory or some sort of downgrading of those items is a significant basis for the observed retro-cue benefit. This is not the only source of the retro-cue benefit, and they note evidence for strengthening of cued representation among modulations. The differentiation of priority between cued and uncued items is sensitive to cue validity, namely the probability that a cue will indicate the item to be tested (Gunseli et al. 2015). 4. Changing one’s mind versus misremembering: If one’s memory changes, in many cases this leads to misremembering, a defect of retention. Yet if intention changes, this is often because we appropriately change our mind. Again, we have a mismatch between the ordinary scheme for intention and that for memory. We do differentiate intention and, say, episodic or semantic memory in this way, but working memory as memory typically involves a change of mind in its content without this alteration counting as misremembering. Part of the function of working memory in action allows for manipulating its memoranda. The fine-tuning of intention is of this type of change of mind. Yet it is not the only change of mind intentions undergo for we often change our minds more substantially, revising intentions drastically such as terminating an action in order to do something else, or dropping the intention altogether. For if I remember that it is Sunday and all the stores are closed in my town, then it is pointless looking for another hardware store. Recognition of the day leads me to drop the intention (“Ah, forget about it!”). This is to engage in practical reasoning that is higher order in that it can be directed at the intention. Rational reflection in practical reasoning is also an action that will exhibit the same basic action structure. I set such higher order forms of reflection aside, but note it as an important topic to which the structure of action applies. The influence of automaticity even in rational reflection should be kept in view. Most changes of mind are due to automatic responses to relevant considerations. Thus, a watercooler conversation with co-workers where one of them casually mentions that he is now able to go to another co-worker’s party might lead to a situation where, despite an initial intention to go, I find myself having a change of heart. I “find myself” now with an intention to do something else rather than go to the party. Yet unbeknownst to me, my change of mind is a response to a new consideration, namely that the co-worker, who as it turns out I implicitly avoid, has decided to go to the party as well. This is an automatic change of mind, one that passes without my noticing it. Much of my reasoning, specifically responses to considerations, is automatic, reliant on my sensitivity to those considerations (Chapter 6). Similarly, reasoning can go awry when I am insufficiently sensitive (this note owes much to discussion with Helen Steward). 5. Instantiating the mechanism of working memory: Recall that in Chapter 3, I spoke of the central executive as the substratum of some of the functions associated with intention such as the control of attention. In pondering p₀ with the intention to do so, one is holding p₀ in mind, so maintaining it over a period of time. This parallels

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cases in which I ask you to hold a phone number in mind. On models of working memory, this would involve the central executive focusing attention on a sequence of numbers held, for example, in the phonological store. Since I argued that the central executive must store a plan (Section 3.3), then the subject’s intention would be to remember that very number. There is a way in which the distinctions that one draws in the working memory structure begin to blend, for we have a central executive that is set by task instructions to remember the sequence ###, and a storage system that also holds ###. The collapse might begin because it seems that the representational capacities for ### are invoked by both the central executive and the storage system. Similarly, switching to the subject level, the conceptual capacities needed to think p₀ are the same as those needed to intend to act in respect of p₀. So, it seems that the intention and the thought exercise the very same conceptual capacities. There is an interesting question about the individuation of aspects of mind when representational capacities overlap in this way (consider the belief that if p then q and the belief that p when one is in a position to infer according to modus ponens; Chapter 6). 6. Inferential accounts of access to action: Access to action “in intention,” as explained here, is not inferential in the ordinary sense of drawing a conclusion from premises. Rather, access to action instantiates the ordinary phenomenon of saying what one thinks or what one experiences. If the idea of reporting what one believes or perceives is perfectly ordinary, so is the idea of reporting what one intends. Where the intention is an intention-in-action, then one is reporting what one is doing. The proposal differs from a different explanation of access, indeed knowledge, by Sarah Paul (2009; see also 2012). Paul offers an inferentialist account of knowing what one is doing on the basis of knowing what one intends. The model is that “when the agent knows that he intends now to φ, he will tend to believe he is φ-ing, and not on the basis of observation of the action in question” (17–18). This model requires that Paul explain how we know that we intend to act, for knowledge of a mental state, an intention, forms the inferential basis for epistemic tracking of action. My account takes intention as an attentional rather than inferential basis for tracking action. In intention, one thinks about action as one does it. My view requires only that we have the ability to report what we believe, see, and intend. If intention-in-action can keep track of what the agent is doing as she does it, then this intention is the agent’s own taking in of what is going on in the world, namely what she is doing. We do not find controversial our access through belief and perception to the world, as we take it. Similarly, we should not find controversial access through intention. In expressing our intention-in-action, we report what we are doing. In the case of mental action, access-in-intention will count as a type of introspection, attention to the mind. In Chapter 7, I separate simple introspection from complex introspection. Roughly, simple introspection relies on a single channel. For introspecting perceptual experience, simple introspection relies only on perception. Similarly, the basic case of access-in-intention is simple in relying only on intention. Complex introspection is not simple, hence it relies on multiple channels. The different factors that Paul elucidates helpfully identify epistemic channels that can influence what we report (see sect. 5 of her paper).

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There is an anemic sense in which access-in-intention is inferential in that the transition between intention to report might be depicted as repeating the content of intention, so it has the form of a trivial inference: φ therefore φ. Triviality, in psychological terms, means effortless or mundane: we report the contents of our thought all the time. On Paul’s picture, the inference is: I intend to φ, therefore φ. To the extent that both views count as inferential, Paul’s account is more substantial (see also Paul 2009). But what it takes to be genuinely inferential is itself an important question about a type of mental action that I take up in Chapter 6. Inferring as action involves actualization of inferential capacities. Crudely, on my model, in access we have actualization of report capacities, not inferential capacities. Of course, I have also argued that the intention keeps track by inference, namely practical inference in fine-tuning. 7. A more complex case: Vigilance is not recognition that now is the time to act, though attention as an expression of appropriate vigilance is effectively such a recognition. The biological and psychological details matter. Having learned my lesson after burning the food, the next time I cook the same meal, I watch the pot carefully. I note that the pot is beginning to boil, but I want a vigorous boil, so I wait. This sensitivity to different ways that the pot boils is an expression of my skill and perceptual sensitivity in light of my abilities as a cook. In one sense, what we have is vigilance, so I am not yet turning down the burner since it is not quite time. But in noting the relevant signs, I focus my attention further to guiding features (not just that the soup is boiling, but some aspect of the boiling’s temporal properties). In doing so, I begin to act in the sense that I am attending to the boiling soup though I have not yet turned down the stove. So, in thinking about our conception of acting now, this conception will be as sensitive as we are to the different guiding features that change over time. See the discussion of radiological skill in Chapter 5.

PART III

M O V E M E N T S OF T H E M I N D A S D E P L O Y M E N T S OF A T T E N T I O N Attentional movements in biased behavior, reasoning, and introspection.

5 Automatic Bias, Experts and Amateurs 5.1 Introduction Bias is a critical factor explaining acting well or poorly, and accordingly, attention is a critical factor in such explanations Agency requires bias. Bias explains solutions to Selection Problems (Section 1.5). This chapter focuses on biased behavior of epistemic and ethical concern driven by attention. Attention is the basis of many forms of doing well and doing badly. Accordingly, it is a target of normative assessment, and important normative dimensions include the level of skill in attention and the propriety of training that establishes specific attentional capacities. Bias, understood in the ordinary sense, is of social concern because of its negative consequences. Explicit bias includes racism, sexism, and ageism, and is a source of inequality, injustice, and concrete harms to individuals and groups. An intention to exclude or harm others because of their race is an explicit negative bias that leads to explicitly biased behaviors. Recent research has focused on implicit bias. A familiar example of implicit bias to academics is the department member who loudly trumpets egalitarian attitudes toward underrepresented groups, yet in social interactions, hiring practices, and doling out invitations for participation in conferences, said person consistently favors individuals of overrepresented groups. When this is pointed out to them, they express surprise, even denial. They evince unawareness of their biases (cf. Section 4.6 on the inaccessibility of automatic features in action). Researchers on implicit bias hoped to explain biased behavior by illuminating its psychological sources, often referred to as implicit attitudes (see Brownstein 2019, esp. sect. 2). A promising development was the design of implicit measures as a means to reveal implicit attitudes. The most well-known of these is, arguably, the Implicit Association Test or IAT (Greenwald, McGhee, and Schwartz 1998). In the IAT that probes racial preference, the preference for one racial group relative to another is revealed by reaction time differences in a categorization task where this is thought to be driven by different evaluative associations across racial categories. How well does the IAT reveal the underlying causes of biased behavior? One concern is that the correlations between an individual’s performance on the IAT

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0006

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and their actual biased behaviors are often low (Oswald et al. 2013; but see Greenwald et al. 2009). While this issue remains debated, some have drawn pessimistic morals. Oswald et al. (2013) conclude that “the IAT provides little insight into who will discriminate against whom, and provides no more insight than explicit measures of bias . . . social psychology’s long search for an unobtrusive measure of prejudice that reliably predicts discrimination must continue” (188, quoted in Machery 2022a who is similarly sober; cf. Gawronski et al. 2022 for a reply as well as Machery’s rejoinder 2022b). Recently, Michael Brownstein et al. (2019) suggest that rather than treat implicit measures as capturing proxies for mental states, we begin with the observation that implicit measures assess behavior . . . Keeping in mind that implicit measures capture various kinds of behavior allows us to treat statements about mental constructs as theoretical hypotheses that seek to explain test performance rather than as objects of assessment themselves. (2)

What then about the theoretical entities postulated to explain implicitly biased behavior? A variety of proposals have been floated but consensus remains elusive. Among philosophical proposals are aliefs (Gendler 2008a, 2008b; see also Brownstein and Madva 2012a, 2012b); patchy endorsements (Levy 2015); propositional mental states (Mandelbaum 2016); non-representational functional structures (Johnson 2020); and traits (Machery 2016). Arguably, a precise causal account of implicitly biased behavior has yet to secure consensus. This chapter takes a different approach by focusing on automatic biases of epistemic and ethical concern that are driven by biased attention. Automatic biases yield automatic attention. Attention is a psychological construct that we understand well at multiple levels of analysis (Parts I and II). By identifying automatic attention as a common thread in many virtuous and vicious actions, the theory of automatic bias provides explanatory unification. Causal factors biasing attention provide targets for normative assessment and, indeed, targets for transformation through appropriate learning.

5.2 A Structure for Explaining Bias A central source of bias on attention is revealed in the setting of priority, including that set by historical influences. Control in action is set by the agent’s intentions (Section 1.4). Other biases are automatic, and among these are historical biases (Awh, Belopolsky, and Theeuwes 2012), diachronic sources that sculpt the agent’s attentional capacities by altering how the agent prioritizes inputs in action space during future action. Given their

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establishment through experience dependent learning, these capacities can be assessed along the dimension of expertise and amateurism, a type of normative assessment. They can be characterized in terms of skills. In Section 2.6, I invoked the empirical construct of priority maps as a way to understand how automatic biases in visual spatial attention remain constrained by the agent’s goals. Recall that priority maps are feature and object silent. They assign a priority value for represented locations but do not contain information about what object or feature is at that location. Accordingly, priority maps do not directly map onto everyday talk of an item as having priority or being salient. To capture these broader ideas, I opted for a general notion of bias. I link bias to talk of salience (or priority) in its ordinary sense as follows: A target T is salient or prioritized by subject S relative to a background set B iff S attends to T rather than elements of B. S’s attending to T can be automatic or controlled, and S’s attention fixes what is salient or prioritized for S (cf. conscious attention as phenomenal salience; Wu 2011b). Drawing on work on inputs into priority maps (e.g. Todd and Manaligod 2018), we can identify a variety of historical biases:

Figure 5.1 There are many biases on the Selection Problem. The standard biases are top-down biases from intention, and bottom-up biases tied to the salience of the target attended to. These biases are indicated in gray. In addition, a set of historical or learned biases can shift how the Selection Problem is solved. The dotted circles identify different biases that can occupy the learned bias node (the list is not exhaustive).

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For each bias, there is a substantial empirical research program that provides detailed understanding of how attention is modulated. Drawing on this structure, the explanation of bias can proceed along concrete dimensions of causal analysis. In many cases, these biases might be taken to be implicit in that they are automatic and not (easily) cognitively accessible. To choose one historical bias on attention, consider reward and value. When our response to specific items has been rewarded in the past, we come to treat them as salient (everyday sense). Hence, we attend to them. Past rewards teach us to assign a positive value to those items. With value, normative assessment gains a foothold. A normative system might judge that the ascription of a specific value to a target is inappropriate, say one ought not to value it, or incorrect, say one has ascribed it the wrong value. This assessment carries over to the agent’s action when informed by said valuation. Similarly, if attention is biased due to the ascribed value, then we can assess the agent’s attention (Siegel 2017, 160). There has been extensive research on reward and value in attention. Vidhya Navalpakkam et al. obtained evidence that representations of value can be integrated with visual salience, in the salience map sense (Section 2.6), to inform prioritization in visual decision (Navalpakkam, Koch, and Perona 2009; Navalpakkam et al. 2010). R. Blythe Towal et al. also found evidence of integrating value and salience to explain eye movement in economic choice with value having greater weight in changing target prioritization (Towal, Mormann, and Koch 2013). Value contributes to the construction of the priority map. In an exemplary study by Brian Anderson, Patryk Laurent and Steven Yantis (2011), subjects were trained to locate two targets, a red circle and a green circle, while eyes maintained fixation throughout. Subjects reported the orientation of a line contained in the targets. These targets were presented among distractor circles of other colors. One target, say the red circle, was associated with a high reward 80% of the time and a low reward 20% of the time, this being reversed for the green circle (20/80). During training, subjects learned this reward structure. After training, subjects were tested in a new task where the target object was now defined by a unique shape, a diamond among distractor colored circles. Subjects were informed that color was no longer task relevant and that they had to report the orientation of a line in the uniquely shaped target. Notably, in some trials, a red or green circle was presented as a distractor. Where a high-valued distractor appeared (here the red circle), reaction time increased as if the distractor captured attention. This influence on behavior is not explained by a difference in salience due to color (Anderson, Laurent and Yantis, 2011, 10369, table 2) nor is it explained by an intention to act on the colored object since color was task irrelevant. Rather, the change in priority is an automatic effect reflecting past experience (training). When subjects were allowed to move their eyes in the same paradigm, Anderson and Yantis (2012) observed that a colored distractor opposite in space to the shape defined target tended to pull the eye to it (cf. Yarbus; see also Hickey

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and Van Zoest 2012; Hickey and van Zoest 2013; on eye movements as having greater test-retest reliability than reaction time measures, see Anderson and Kim 2019). Learned prioritization was long-lasting, up to at least six months after training even without further reinforcement (Anderson and Yantis 2013). Learning of the value associated with a feature can alter the priority assigned to it as instantiated in the automatic bias to attention. In a different paradigm, Hickey et al. (2010) showed that value can establish a bias toward a reward-associated feature even if that feature is never task relevant (see also Le Pelley et al. 2015, exp. 1). In their paradigm, subjects had to report the orientation of a line in a unique shape, say a diamond among circle distractors. Importantly, one of the distractors was a different color than the other shapes (say, one red circle among green circles, with the target being a green diamond). Color was never task relevant. Interestingly, when a target was highly rewarded in one trial and was colored red, reaction time slowed when colors were swapped in the next trial so that a single distractor was now red. That is, the color, task irrelevant yet associated with a previous high-value target, now captured attention (11097, Fig. 1). This case illustrates the automatic learning of structural features of the experimental context, what we might see as an instance of the general phenomenon of learning the statistics of the environment. Agents quickly pick up correlations so that their behavior is biased along the irrelevant dimension (cf. Munton n.d. on salience structures). I have noted how automaticity and control are often in conflict. It is worth noting that in respect of attention, value-based bias contravenes explicit strategies by the subject. In one experiment, Hickey et al. told subjects that when a target was high-valued (they received a large reward in that trial), the task-irrelevant colors would swap in the next trial. Knowledge of this contextual shift could enable subjects to strategically ignore the colors associated with reward in the earlier trial, and it would be advantageous for them to do so. Despite this, the highly rewarded color continued to capture attention against the agent’s intention to ignore it (11098, fig. 2b). Again, from the perspective of socially impactful biases, the inability of explicit intentions to suppress automatic bias is notable. An intention to act differently need not be successful against established automaticities. The previous experiments focused on the association between value and lowlevel visual features. Similar results have also been observed for audition where letter sounds (e.g. “A”) were associated with different values and which were capable of distracting subjects during a second test task (Anderson 2016b; cf. also the cocktail party effect and hearing one’s name). Other experiments suggest that the alteration in priority can be at the level of categories such as objects (cars) and human bodies (Hickey, Kaiser, and Peelen 2015; see also Failing and Theeuwes 2015). Indeed, if the rewarded view of the target body is of the legs and feet, the appearance of a torso in a later trial can still distract the subject away from the actual target. Thus, reward need not be just tied to a visible object but also to its

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semantically associated parts or to the category (Hickey et al. 2015, exp. 3). Perhaps attention can also be modulated by experienced positive social response (Anderson 2016c; Hayward et al. 2018). These experiments which are foundational in research on reward and attention provide evidence that attention shifts given past reward and resulting valuation. Recent work has examined the neural basis of reward and value-based modulations of attention and this is an area of active research (for recent papers, see Itthipuripat et al. 2019; Tankelevitch et al. 2020; Bachman et al. 2020). We are filling out a Marrian explanation of value-based bias on attention (for detailed reviews, see Anderson 2016a; Bourgeois, Chelazzi, and Vuilleumier 2016; Anderson 2019). Given our interest in biases of social concern, these studies are suggestive. Notably, color can be quickly assigned a high value through a reward structure where this valuation remains stable over time. The result is that in contexts where color is irrelevant, it can nevertheless automatically bias the agent’s behavior. Indeed, given the automaticity of bias and learning, subjects need not be aware of the sources and extent of their biases. This is not to suggest that the noted studies shed immediate light on automatic or implicit racial biases. Rather, they identify attention as a basis of biased behavior for which we have a detailed understanding of correlated mechanisms and neural substrates. This provides a concrete foundation to investigate the causal basis of biased behavior of social concern. Indeed, one could accurately describe some reward-based biases as implicit biases, even characterize value-based shifts in prioritization as a type of implicit attitude. For example, the value assignments to features established from past reward can constitute a form of memory which biases subsequent deployments of attention. Theorists of implicit bias should find attention a fertile ground for theorizing. Reward and value are only one type of bias. I have already discussed intention as a (traditionally, top-down) bias tied to control and salience maps as automatic biases on visual spatial attention. We have good causal understanding of these cases which directly informs understanding how agency, as a solution to the Selection Problem, arises. The key idea is that bias is conceptually and causally tied to the structure of action, and that structure provides a causal framework to explain biased behavior through its causal components. In the next sections, this structure provides explanatory unification of various virtuous and vicious biases, highlighting attention as contributing to explanations of biased actions.

5.3 Epistemic Bias Is Necessitated Bias Epistemic bias often begins with biased attention. Louise Antony (2016) and Gabbrielle Johnson (2020) have argued that bias is required to solve the underdetermination of theory by evidence: the data available

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to a theorist is compatible with many incompatible hypothesis so does not favor one over the others. If epistemic agents require that a unique hypotheses be favored by the data before they act (draw a conclusion), they would not be able to settle inquiry in the face of alternatives equally compatible with the evidence (for a thorough discussion of epistemic agency that shares many points of contact with this book, see Fairweather and Montemayor 2017, esp. chs. 4 and 5). That a bias is needed is revealed by the fact that these theorists face a reverse Buridan Space (Section 1.5). .

Figure 5.2 The agent can judge that H1 or that H2, but both are equally compatible with the data D, so barring any bias that favors one hypothesis over the other, the agent might not respond. This is an inverted Buridan Space (Section 1.5), resolution of which requires a bias.

Let us treat H1 and H2 as “reasonable” hypotheses that members of a scientific paradigm take seriously. The problem of underdetermination is posed relative to fixed evidence: given evidence D (data), which of many compatible hypothesis should one endorse? In assessing possible hypotheses, epistemic agents cognitively attend to this evidence, establishing a one-many Selection Problem (this simplifies the situation for heuristic purposes). Every action requires a bias, so Antony and Johnson are right that inferential movement to a unique theory or hypothesis in response to evidence requires a bias. In other words, the epistemic case at issue is just a Selection Problem. The simplest bias is a familiar cognitive inertia: one reaffirms the hypothesis already held. It is “cognitively salient.” Yet the theorist ought not to affirm one hypothesis over another on evidence alone when they are equally supported by the evidence. The epistemically proper response, it seems, is to treat both hypotheses as live options, bracketing bias from prior belief and theoretical inertia. Further evidence can provide a tie-breaker. Until the evidential impasse is resolved, the hypotheses now become materials for ampliative inference, inputs into action space, things that the agent should further ponder and not merely accept. Consider the theorist who is committed to H1. The evidence gathered provides confirmatory data on H1. She describes her action thus: “I ran the experiment to test my theory, so I remained open to the possibility that I might be wrong

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(I suspended my belief). The evidence confirms my theory’s prediction, hence I properly reaffirm my judgment.” But consider different theorists. First, imagine a young graduate student who has not yet grasped the whole literature in her field including opposing views or has not thought through “salient” alternatives because she takes cues from her lab, well-known for defending H1. So, for her, the relevant hypothesis space, a doxastic action space (Section 1.2), might contain only H1 and her action space is correspondingly narrowed. Cognitive myopia is common. Hence:

Figure 5.3 The agent endorses hypothesis H1 and, when confronted with new data D consistent with it, reaffirms H1. Yet another prevalent hypothesis, H2, is also consistent with the hypothesis but is not even registered by the agent hence not currently one of the inputs to the agent’s action space. This might reflect ignorance.

Given her doxastic action space, the agent does not find it untoward to reaffirm her hypothesis (belief). She isn’t in a position to know better, being genuinely unaware of alternatives. If epistemic agency falls short of where it should be, the student’s advisor has done her a disservice by not apprising her of alternatives to consider. An external bias, here based on a faulty learning environment, limits the set of inputs into action space. Of course, the student is a participant in a broader joint-action space, and as she gets older, if her doxastic space does not change, the responsibility arguably falls more and more on her in failing to be sensitive to alternatives. A theory of epistemic virtue in her practice holds that she ought to be able to reason out alternatives. She should notice other possibilities. Now consider a case where we can add the alternative hypothesis, H2, to the agent’s action space, for perhaps she has come across work of opposing groups in journal articles or conferences, or perhaps she has come up with the alternative herself. Let us also consider the simplest case where she affirms her original hypothesis. Given underdetermination (Figure 5.1), this is not the appropriate response so why did she reaffirm her belief? The theory of attention identifies concrete reasons why, and we can canvas three:

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1. Her hypothesis is salient in a way that pulls her attention. 2. Her hypothesis is valued in a way that leads to automatic attention and affirmation. 3. She intentionally focuses on her hypothesis. This is not an exhaustive list but it suffices to illustrate how an action structure that highlights the role of attention can facilitate normative assessment in terms of how attention is deployed. Option (1) deploys “salience” in the everyday sense, so not in the sense fixed by the salience map. I suggested earlier that salience entails attention. Since the salience of a proposition or theory is not driven by low-level perceptual features in salience maps, the focusing of attention in (1) must be explained by other factors. (2) and (3) provide potential explanations. We noted empirical work on reward, and given that science is a practice that is built on a complicated reward structure (publication, grants, recognition, tenure, and prizes), one future line of inquiry will aim to extend our understanding of basic reward mechanisms to more complicated behavior as in the current case. Still, the shape of a plausible explanation is clear and allows the following posit: A graduate student learns the value that her advisor and lab colleagues accord to H1 even without an explicit intention to do so. The student develops an automatic bias to H1. The result is that the agent’s learned value, even if only implicit, can conflict with epistemic demands just as it can conflict with explicit intentions. The resolution of this conflict is seen in how cognitive attention is automatically deployed. We might say that the agent exemplifies cognitive fixation on her hypothesis, given assigned value, despite the epistemic demand to attend to alternatives. Option (3) might seem nefarious as an explicit bias, but the intention need only be to focus on testing H1, a perfectly reasonable move in science. Accordingly, we can return to the inattentional blindness paradigms that vividly demonstrate that when a subject is focused on a specific target, she withdraws from other targets, missing what is noteworthy. Adapting the result to the present case, we have inattentional cognitive “blindness.” Thus, the researcher’s experiment is appropriate in that it is her intentionally testing her hypothesis. Yet if she remains cognitively blind to alternatives (as subjects are blind to a gorilla; Simons and Chabris 1999), she will fail to recognize that her inference is not warranted because the evidence does not favor her hypothesis over others that she does not notice. Talk of cognitive blindness expresses a normative assessment, a charge of failed vigilance or sensitivity to relevant alternatives. This charge, tied to inattention, applies to all cases where the withdrawal of attention is inappropriate. Epistemic virtue, here in theory testing, depends in part on what we consider and what we ignore in attention (Siegel 2013; Wu 2017b; Fairweather and Montemayor 2017; Smithies 2019).

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Now consider the space of hypotheses consistent with the data: {H1, H2, H3 . . . }. Perhaps this set is infinite. If so, no human can cognize all of these positions. Let us restrict the relevant background set to {H1, H2, H3 . . . Hn} where n = the number of positions that the scientific field would deem relevant on reflection. If each H is intelligible to a scientist, S, then there is a behavior space for S that contains each H as an input. Let S have heard of some subset of the background set: {H1, H2, H3 . . . Hm} where m < n. We can now define an action space for S as follows:

Figure 5.4 The agent believes or remembers a set of hypotheses {H1 . . . Hm} along with new data D relevant to those hypotheses. In assessing these against the data, she will come to a conclusion, affirming hypothesis Hy. In this diagram, Hm is rendered in a grey circle to indicate less vigilance to it. If y = 1, then the agent reaffirms her beliefs as in Figure 5.2.

Since the inputs are cognitive states, the set of inputs identifies what I will call a cognitive field just as the set of perceptual inputs would identify a perceptual (e.g. visual) field (Section 5.7). S believes one of the hypotheses, say H1, but we assume that S at least remembers all the others, some of which are hypotheses believed by S’s competitors. Now, given new data D, what we hope S will do is confront D to each H or at least recognize that other relevant hypotheses exist. This requires that S attend to each H, yet as should now be familiar, S might be “blind” to a given H even if this hypothesis best fits D. What we hope is that S will notice relevant items in the cognitive field, the set of hypotheses that she has learned as inputs to action space. This requires that relevant hypotheses have a pull on the agent’s attention, that S can move given appropriate biases. Yet S is not sufficiently vigilant to alternative hypotheses.

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To move out of a Buridan Space, we want a justified bias. In the absence of gathering more data, a partisan and neutral theorist might agree to appeal to theoretical virtues such as simplicity. Let us assume that there is a metric of simplicity such that hypothesis H1 is simpler than H2. How might formal principles solve the Selection Problem? One possibility is that knowledge of the principle biases the response (Figure 5.2). In knowing the principle, one recognizes that H1 is simpler than H2 so affirms it in light of the data over H2. This suggests a way in which the content of knowledge directly biases action, a move that involves recognitional skills. Here, knowledge activates an instance of theoretical inference (see also deduction in Chapter 6). Another way to understand how simplicity metrics figure in theoretical reasoning is to state the metric as a proposition that is then applied in addition to the evidence. The data plus metric justify concluding H1 rather than H2 where the metric is a premise in the inference, hence an additional consideration that the agent cognitively attends to (thinks about). In this case, the principle is not a bias but explicitly guides the response as a target of cognitive attention in the agent’s thinking. The fine line between objectionable and appropriate bias in attention is reflected in the fine line between good and bad training that instills biases. Science education enforces biases in how trainees generate, process, and assess data. Often implicit in this training is the inculcation of partiality to work from specific sources with necessitated withdrawal from others: specific countries, universities, departments, labs, individuals, journals, research programs, and so on. The papers discussed in journal clubs, the papers presented as foundational in lectures and those that are treated as crucial for a field become “salient” in academic education and practice. In general, this is appropriate, for expertise in a field depends on specialization—a bias consistent with the practice. It might seem platitudinous to proffer these points. This would miss the punch line: in situating epistemic bias within the theory of attention, we link epistemology and philosophy of science with cognitive science. This opens up avenues for determinate and testable causal explanations as well as providing a map of targets for normative assessment. Platitudes are only coarse grained postulates that must be subjected to empirical friction with our understanding of the biology. The advance is to move beyond the platitudes toward a systematic theory that is empirically plausible and philosophically substantive.

5.4 Overt Attending as Mental Bias Every movement involves a mental guide in attention, so no action is “purely bodily” including overt visual attending.

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To further explanatory unification, I will broaden the structure of solving the Selection Problem to a common form of bias: attention in the eye’s movement. As overt visual attention involves eye movement, it is classified as a bodily action. For our purposes, a sharp division between mental and bodily actions is not necessary, but an initial explication focuses on output capacities. Bodily action involves bodily response capacities, mental action does not. Given the structure of action, even if every bodily action involves the deployment of an action-relevant bodily capacity, it also necessarily involves attention in guiding response, mental selection for action. While overt visual attention is not a pure movement of the mind, it necessarily involves mental activity in attention. We can say that visually guided action has as a constituent a visual experience that constitutes visual attention. Eye movements provide an illuminating window onto bias. We have already considered Yarbus’ demonstration of eye movements that illustrates intentionbased bias. Consider free viewing of the painting, The Unexpected Visitor, and the observed pattern of eye movement.

Figure 5.5 The figure demonstrates “free viewing” of the I. M. Repin painting by one of Yarbus’ subjects, the same subject depicted in Figure 1.9. Note that a bias toward faces in fixation is apparent. Reprinted by permission from Springer Nature: Springer, Eye Movements and Vision by Alfred Yarbus (1967, 174, fig. 109).

The agent was not performing a defined task set by the experimenter but allowed to freely look at the painting. Clearly, human faces draw the eye. Visual meandering is not completely random. The meandering of the eyes often serves a purpose that is discernible when we know the intentions of the agent (Section 1.6). Even when they move “freely,” the eyes exhibit a bias toward certain features of the scene, what is salient or prioritized. Between task constraints and freedom in meandering is the panoply of human bias, neutral, good, and bad. Following the eye reveals how the Selection Problem is

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solved. Because the basis of solving the problem is the basis of agency, the eye teaches us about bias. One can treat it as an indirect measure of internal bias.

5.5 Epistemic Virtue in the Eye Virtuous automatic bias in visual attention can be learned through practice and training as demonstrated in epistemic skill in medicine. One can attend well or poorly, appropriately or inappropriately, in more or less skilled ways. We train attention through explicit shaping of bias, shifting from a novice’s inadequate attention to the skilled agent’s appropriate attention and finally to sublime forms of attention that indicate expertise. Consider the development of overt visual attention in medicine, specifically in visual search during radiological assessment of x-rays (for philosophical discussion of attentional skills in sports, see Wu 2020a; for appropriate attention to artifacts, see Wu 2008). The goal of reading x-rays is to detect anomalies and to categorize them as concerning or benign. For a patient who has a cancerous growth visible in an x-ray, the gold standard response is detection and correct categorization. The x-ray image is a complicated source of information, presenting the radiologist with a Selection Problem. The image contains much information only some of which is relevant to the task of detection (yes/no to the presence of an anomaly) and discrimination (categorization of anomalies). Decades of research on visual search in radiological assessment consistently show that medical training changes the character of visual search. Specifically, radiologists develop a visual bias that leads them to ignore irrelevant parts of the image and attend to abnormalities. As Waite et al. (2019) note: “expert radiologists do not scrutinize all regions of the image equally but direct their attention and eye movements more precisely to relevant areas. This heightens efficiency but can also mask unexpected findings” (2019, 3). Efficiency and masking are, of course, part of the give and take of selection and withdrawal, of focus and inattentional blindness. The development of appropriate bias is seen in the changes in eye movement patterns across different levels of training, from novices to attending radiologists. That the pattern of overt attention changes with experience is commonly observed. For example, Donovan and Litchfield (2013) compared novices, firstand third-year radiology students (with 12 and 28 weeks of clinical experience respectively) and established experts by recording eye movements during visual search of pulmonary x-rays. The curated images either contained a suspicious though subtle pulmonary nodule or they were normal and contained no suspicious growths.

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Figure 5.6 Heat map of eye fixations during visual search for an anomaly in an x-ray. Novices focus on a visually salient healthy structure in the center of the x-ray while experts focus on the anomaly. Copyright © 2012 John Wiley & Sons, Ltd. Figure is adapted and reproduced with permission of Timothy Donovan and of John Wiley & Sons from Timothy Donovan and Damien Litchfield. 2013. “Looking for Cancer Expertise Related Differences in Searching and Decision Making.” Applied Cognitive Psychology 27 (1): 43–9.

Novices with no training tend to fixate on visually salient but task-irrelevant parts of the lung, such as healthy, visually prominent structures (note the amount of time spent on a vertical section of the right lung, the hilum; left side of image). Novice visual search is driven by salience in the narrow sense tied to salience maps (Section 2.6). Radiologists in early stages of training who are taught to be systematic in search carefully scrutinize the image as if applying rules of thumb in categorizing visual features. Expert radiologists, having acquired visual and conceptual expertise, are more efficient in noting and categorizing the abnormality. The change in patterns of eye movements indicates a shift of visual bias over training, with a move away from search driven by visual salience to an increased ability to focus on task-relevant features of the display and to respond accordingly (cf. Kelly et al. 2016, 257, fig. 4). In a review of past studies on eye movement in radiological assessment, van der Gijp et al. (2017) note that experienced radiologists consistently spend less time on images than less experienced practitioners, require fewer fixations to locate an abnormality, and land more quickly on it (776, table 3). Additional studies have correlated different levels of training with differences in scan path length, saccade length, proportion of time spent on regions of interest, and fixation times

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(van der Gijp et al. provide a sober assessment of these claims, but a more upbeat summary is given by Sheridan and Reingold 2017, table 1; for similar results in other forms of visual expertise, see Gegenfurtner, Lehtinen, and Säljö 2011). Overt visual attention changes its character with training to facilitate appropriate visual selection that informs diagnostic judgment. These changes reflect the increased epistemic reliability and authority of radiologists. Recalling our aim of explanatory unification, note parallels with the epistemic reliability of the scientist attending to data and her ability to notice relevant inputs such as alternative hypotheses (Section 5.3 and Figure 5.4). The notion of attention deployed is not merely apt description, but reveals a common psychological structure in solutions to Selection Problems in respect of epistemic judgments. The effectiveness of these solutions depends on an appropriate bias. How does a radiologist learn the appropriate bias? Standard radiological training emphasizes beginning with a systematic approach to assessing x-rays and the use of rules to guide assessment. One textbook (Goodman 2020) notes: The keys to reading x-rays well are a good understanding of normal anatomy and an orderly search pattern. This chapter . . . helps you develop a search pattern that you can apply to every radiograph. By being systematic, you will miss fewer important findings . . . Learn this ordered approach and then stick to it in case after case. (36)

For example, early stage trainees are instructed to begin at the bottom of the right lung and then systematically scan up the lung in a zigzag pattern and then shift to the top of the left lung performing the same systematic scan downwards. Finally, a visual crisscrossing scan of both lungs is undertaken. We can invoke the notion of a direct intention here (Section 1.8): subjects intend to locate an anomaly by implementing the taught scan pattern. Such an intention might explain the more diffuse patterns seen in first- and third-year radiology students in Figure 5.6, with “pruning” of visual search over time. It is not clear that systematic viewing improves performance (e.g. Kok et al. 2016; see p. 190, table 1), and systematic patterns are largely abandoned by practicing radiologists as they gain more experience. Carmody et al. (1984) observed that only 4% of recorded searches among radiology instructors exemplified the systematic pattern noted (see p. 463, table 3, for some characteristic patterns). As expertise is acquired, intentions no longer explicitly encode and implement a rule. The doctor simply intends to examine the x-ray for anomalies and scans in the way she does automatically. Strikingly, in the expert observer in Figure 5.6, the anomaly seems to literally pop out, to grab attention, pulling the eye inexorably to it. Notice that we can speak of the abnormality as salient in the everyday sense, something important and worth noting. Thus, while novices begin with behavior driven by visual salience as explained by salience maps, there is a transformation of the bias with training to

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render the subject sensitive to salience in a medical sense. A good radiologist notices medically salient things quickly, efficiently, and reliably. Recent work on training has focused on providing direct and immediate feedback. Chen et al. (2017) note that typical radiological training is rule based with little immediate “gold-standard” feedback which would provide cues to aid in perceptual learning, cues such as indicating the relevant features (on perceptual learning, see Prettyman 2019). In their study, naive observers were trained to detect hip fractures in x-rays. Subjects were presented with cropped images of the left or right pelvis where half of the images contained an anomaly. X-rays were selected from a large set without replacement, and depending on the experiment, training involved 200 to 1,280 image presentations. When subjects failed to detect an anomaly, they received feedback with arrows showing the missed target, redirecting the subject’s visual attention. Subjects were rewarded for each hit by a point system during training and additional cash if they hit a performance target during a test (cf. Section 5.2 on reward and bias). The learners were subsequently tested on a set of new images. Strikingly, performance accuracy was well above chance after training on only 200 images (about 80% accuracy where 50% is chance). With more training, performance improved to nearly 90%. Two control groups of radiology residents and of board certified radiologists performed at around 93% accuracy on the test images. Thus, after only a few days of training, performance by novices began to approach professional levels of accuracy, albeit for very specific types of images. The work suggests that directed learning over a short period of time with focus on directing visual attention to relevant guiding features in the image can tune reliable recognitional capacities guided by attention to task-relevant targets (cf. Sowden et al. 2000). This result along with those of the reward and attention literature attest to how attention can be appropriately and rapidly biased by learning. We began with the Selection Problem which demands bias for solutions. Between bias that is explicitly intentional and bias driven by low-level perceptual salience is a wealth of automatic bias. Many automatic biases are learned through experience, and stabilized through practice and repetition. Learning need not be concerted, as when we simply follow others in selecting sources, reinforcing our biases unwittingly through within group social interactions that lead to us picking up statistical structures that influence how we perceptually and cognitively attend. Even in concerted cases where instruction aims to impart skill and expertise, bias can become unwittingly (automatically) rigidified. An advisor’s own interest in a specific thinker can lead to a bias in what papers are discussed in learning contexts with her students, and an undesirable weight gets established that prioritizes one thinker when others are worthy and, indeed, epistemically demand attention. Finally, with care, biases can be shifted in the desired way in the interaction between student and teacher, as happens when trainees jointly look at an x-ray with the attending radiologist who points out and explains anomalies.

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The aim of training is not to eliminate bias. It is to transform it along the standards for a practice. Epistemic virtue must be learned and stabilized with appropriate habituation. The alternative is a type of amateurism that begins with poor attentional practice or that reflects a deterioration of ability through bad habits.

5.6 Automatic Bias and the Distribution of Gaze as Good Gaze is a good whose distribution is automatically biased in ways that can have negative consequences in academic and social settings. Let us consider biases of social concern revealed by the eye. Begin by casting epistemic bias in attention in a schematic form that supports generalization, namely as instantiating a Selection Problem. Consider a target X (e.g. data) of putative epistemic relevance, what the epistemic agent, S, should attend to given her epistemic project (the “should” can be defined by standards of good practice in her field). In addition, let there be relevant distractors, Y₁ to YM. To further her project, S must attend to X and withdraw from the Ys. For example, the radiologist should attend to the anomaly and ignore wasting attention on visually salient but healthy structures. As we have seen, the challenge for a novice or trainee is that attention can be pulled to the salient distractor Y and not the task-relevant target X. This could result in inattentional unawareness of X. In a different context, one of the Ys might also be epistemically relevant, but if the agent is fixated on X, she will again be inattentionally unaware of matters she should focus on (recall steadfastness; Section 3.6).

Figure 5.7

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Just as epistemic bias can lead to inappropriate focus on one target to the exclusion of another, a social instantiation of this schema can be inappropriate. Consider the following three-person scenario: X, Y, and Z are having a conversation where Z is talking and X and Y are listening. In respect of the conversation, X and Y are “equals” yet Z’s gaze is largely directed at Y rather than X. Since gaze is an overt sign of attention, the speaker’s attention is largely devoted to one individual rather than another. This is formally a bias. Here is the same bias structure in a social context: consider when X is a woman, and Y and Z are men. Z’s attention when speaking is directed at Y rather than X, as if he is largely talking to Y and, at least in terms of social cues, effectively ignoring X. The frame can be elaborated in more complex situations, say where Z is a teacher with a number of students, X, Y, . . . and where Z’s attention is directed at a subset of the students (cf. Figure 5.7). I have noticed biased gaze at academic social functions where gender is plausibly the basis of the bias, a male speaker seems only to be talking to me and at least with his eyes, seemingly ignoring the woman who is also part of the conversation. Having presented this scenario to colleagues and audiences over the past year, I have been surprised at how often people immediately recognize the situation and attest to having experienced similar exchanges in different contexts. Many others report it in classroom contexts, at work meetings and social functions, or when talking to workers at their home, and in one case, even with parents (a mother who always looks at the son rather than daughter when speaking to both). While my example focuses on gender, it can also involve bias in respect of power, social status, race, economic class, nationality, and so on. These categories are often correlated, so what is ultimately the guiding feature attended to can be hard to dissect (recall the Hickey et al. 2010 experiment that showed taskirrelevant features biasing attention). This is the challenge of actual bias in many situations. Social interactions are complex, and the motley influences on how we act therein, including gaze, are difficult to clearly isolate. Each individual brings something different to the table that will explain their behavior in light of different past experiences and upbringing. Bias and eye movement captures other cases familiar in academia. At the end of many talks, there is a moderated question period. The moderator must look out toward the raised hands in the audience to establish a queue. During a break at a philosophy conference several years ago, I was part of a conversation about apparent gender bias in the prior question periods, something several independently noticed: Most of the philosophers called on were men, despite a large number of women who had their hands up. Since establishing a queue requires visual search, and since many hands go up, the queue will be based in part upon who is seen first or who jumps out to the speaker. Bias in saccades in the moderator’s visual search will show up in bias in the composition of the queue.

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The guiding features that lead to biased behavior are likely a complicated panoply of various elements that are salient in the sense of pulling attention. For example, many of us know how to game queue moderation: If you want to ensure being called on, make yourself salient by sitting in the front row. Thus, one question to resolve in cases where queues are biased is whether spatial bias is an important driver of bias exemplified by who is granted access to the floor. If so, ostensibly neutral proxies, here spatial location, can still lead to a negative bias although the target of attention is itself neutral (front versus back row). Behavior is complex, but in focusing on attention, we can in principle test what guiding features in a given context drive the specific biases of interest. That there are negative biases in question and answer periods is acknowledged by many academics. To deal with bias with respect to power or status, many departments have instituted an explicit policy of having graduate students ask the initial questions (cf. the rules for scanning in radiology). That is, a bias toward faculty (often senior faculty) is debiased by imposing a counter bias which is instituted intentionally by the moderator, leading to their visually searching for hands from students. Indeed, moderators often verbalize the rule before starting the discussion. Departments who have instituted such a policy have reasoned that providing opportunities for students to engage in the colloquium arena helps them develop professional abilities, to have a seat “at the front.” This is taken to be part of a good education. If having student engagement is valued, then there is a corollary value in having faculty recognize students to facilitate engagement. Here, we can reintroduce assessments of skill in mentorship. For recall in the radiology case the transition from a novice driven by inappropriate salience, to the early stage radiologist who intentionally performs the task by implementing a rule (a systematic scanning rubric) to the expert who simply locates an anomaly. This marked a dimension of increased skill characteristic of good medical action. In the queue case, we can identify a similar transition, along the dimension of skill, from a moderator who was once driven by inappropriate salience, always calling on the senior male professors, to a stage where he must intend to establish a fair queue by implementing a rule (call on students first) to one who “need not think” but automatically does so in intending to moderate the discussion period. Arguably, many of us academics never progress beyond the explicit use of a rule stage in queue moderation, an early stage in practice. Naturally, running a queue once a semester does not good practice make. Of course, queue moderation is not as important as scanning a patient’s data. Still, the goals in both cases are the same: we wish to move from being insufficiently trained to at least being able to do the right thing automatically. Attention provides a unifying thread to understanding bias, a unification pinned to the structure of action. When attention becomes the focus of investigating bias, we can move beyond speculation of what biases are since the variety of

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biases on attention is well delineated. Understanding why biases occur and how they occur can be addressed with the might of our conceptual and empirical scheme for attention. By understanding such attentional biases as essential parts of agency and drawing on the structural account detailed in Part I, we can deploy different levels of analysis to understand bias as a necessary agentive feature.

5.7 Automatic Thinking in Fields of Thought Perception and cognition operate over a biased field, the set of inputs in an action space, its structure revealed by automatic perceptual and cognitive attention. Bias in overt visual attention is seen in what the eye lands on first. By analogy, bias in cognitive attention can be seen in what thought lands on first. We can address cognitive bias by reinstating the structure of action in specific cognitive domains. By analogy with perceptual bias in queue moderation, consider the bias exemplified by the all-male line-up of conference speakers or contributors to special journal issues. Well-intentioned conference organizers or editors, who in many contexts explicitly aver egalitarian attitudes, likely proceed similarly: in intending to create a list of candidates, they begin thinking of people who work on topic X, or the best people who do so. Then . . . people pop into cognitive view (recall thinking about topic T and the automaticity of relevant propositions that pop into mind, Section 4.5). In cognitive and perceptual attention, where the mind lands is the outcome of the tug of war between automaticity and control. Recollection is controlled in that the agent recalls people who work on X rather than Y, as she intends. Yet the specific persons who come to mind do so automatically. After all, if the agent were to intend to recall, say, Dr. Smith, in intending to do so, the agent would already be a thinking of Dr. Smith and recall would not be necessary (Strawson 2003). Intentions to remember cannot be direct in that sense (Section 1.8). So, intentional recollection of appropriate names means that the specific names that arise necessarily do so automatically, for they are not represented in the intention. Accordingly, partiality in recollection, say in gendered ways, will be explained by sources of mnemonic automaticity set by past experience (cf. biases in education and statistical learning). As the sameness of perceptual and cognitive biases is framed within the Selection Problem, we can expand the idea of a perceptual field in a psychological sense, namely the field as containing the perceptible phenomena available to an agent given their perceptual orientation to their environment. The perceptual field defines the perceptual inputs into an agent’s action space. Given the similarities that I just noted between perceptual and cognitive bias, I suggest that we can also

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speak of a cognitive field (Christopher Peacocke (1998) notes that the associated phenomenologies are different; cf. Fortney 2018). Both fields are determined in part by the relevant input, perceptual or cognitive, in an action space (cf. the detective in Section 2.1 and our scientist in Section 5.3). Prioritization identifies shifts in weighting in the structure of the perceptual and cognitive field at a time. As prioritization changes, so do the properties of the fields, the structure of inputs to action space. A perceptual field identifies the inputs available to a subject who then acts in light of that field, with a specific input guiding an output when the Selection Problem is solved. The subsequent action changes the contents of the field and the resulting alteration of action space affects how the agent responds over time to what she perceives. For example, given this shift, the agent might stay on task or the agent might get perceptually distracted by inputs that are newly available due to the agent’s actions (Section 3.6). The same dynamical push-and-pull between automaticity and control applies to the cognitive field. At the time before our organizer intends to construct a list, the hypothesis is that the cognitive field encapsulates a certain priority in potential targets of cognitive attention. This will reflect a type of cognitive vigilance. When the subject forms an intention to recall speakers on X, then the intention biases relevant capacities regarding speakers on X as opposed to other topics. A corresponding swathe of the cognitive field is prioritized (think of a “cognitive group” of speakers in the agent’s mind, and who is sitting closer to the front in memory access). Since the intention does not pick out specific speakers, further competition ensues. As the intention which initiates the competition cannot provide a further bias (it does not represent specific names), resolution will depend on biases set by other factors. At this point, learned biases will have a significant effect, say a bias toward American and British philosophers, if X is a topic in analytic philosophy, or biases toward those philosophers that were the frequent focus of reading groups or the usual suspects in the table of contents of edited volumes recently read by the organizer. We can intentionally implement debiasing measures as exemplified by the rule to prioritize students in question and answer periods. Similarly, in coming up with lists of participants for speaking or publication slots, we can institute rules that facilitate achieving an ideal of fairness in distribution of opportunities. It is important to note, however, that an agent’s goal in such contexts should not only be to act responsibly at a given moment in time, but to change one’s agentive capacities by transforming one’s biases. So explicit debiasing should serve a diachronic as well as a synchronic purpose. It should be part of training. What the agent in question should aim for is not just to do the right thing now but to be the type of agent who always does the right thing. Ideally, doing well will involve a level of skill that obviates having to think about what the right thing to do is. One just does it. Here, habituation to generate the desired automatic biases is crucial.

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Priority will shift in the cognitive field in ways that are influenced by but also compete for influence on behavior in the face of the agent’s intentions. This parallels the case of eye movement and the continued competition of objects in the visual field to draw the eye to them. Even during a task where the eye moves in ways that support one’s intention, distractors can pull the eye toward it as noted earlier in discussing reward and distractors (Section 5.2). Similarly, even as one thinks through a train of thought as part of a project, one’s train can be deflected. We can be cognitively as well as perceptually distracted. The way we move in light of a perceptual or cognitive field, and accordingly through the action space of which the field is part, exhibits a meandering reflecting the forces of automaticity and control. Cognitive deflection is the capture of cognitive attention, distraction that typically moves us away from our intended or current line of thought. Where thinking moves away from the current task, the mind is said to wander (Smallwood and Schooler 2006; Christoff et al. 2016), and the agent’s thought, being automatic, reflects a loss of control, a distraction, or cognitive slip (Section 3.6). Mind wandering raises interesting questions about the dynamics of task-directed attention and distraction in cognition. It is, as a form of distraction, a phenomenon of attention, typically automatic (on the idea of intentional mind wandering, see Seli et al. 2016). In the typical case, mind wandering will be a passive action, the fully automatic actualization of action capacities that are independent of and typically contrary to one’s intention (Section 1.4).¹ As the mind meanders, discoveries can be made. Locating solutions for problems for which no algorithmic decision or reasoning procedure is available depends on the automaticity of thought. By definition, we have no control over the searched for thoughts as this would require these thoughts to be already held in a direct intention (this is not to say that we can’t prompt automaticity in various ways, such as retracing steps when we try to recall the location of a lost object). If we already knew the solution, there would be no need to intend to find it. Intending to solve problem P will bias a set of cognitive capacities that are relevant to P. What capacities are available, which are constituents of action space, depends on prior experience. Someone who has grappled with P for a long time, or tackled similar instances of it, will be able to bias relevant capacities that can lead to a solution. This is in contrast to a novice approaching the problem for the first time who might have to cast about in desultory ways in the hopes of latching onto a productive line of thought. The expert will be more efficient (cf. the radiologists; Section 5.4). Yet there are dangers lurking. An old hand might overly weight familiar methods in dealing with P which will not, in that instance, lead to a solution and she might persist in this unproductive line of thought (cf. fixation in steadfastness; Section 3.6). A less experienced thinker, with a less biased action space, might come upon a solution missed by the old hand precisely because her cognitive attention is not trapped in familiar routines.

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The structure of action allows us to unify skilled behavior of the highest form, say in life-saving abilities in radiological diagnosis, the gutters of epistemic and social vice, the passivity of cognitive distraction, and the automaticity of cognitive insight. The central explanatory concept is a substantive, uniform notion of attention embedded in an action space that sets a Selection Problem tied to the dynamics of the psychological fields that define the inputs as potential grounds of the agent’s attention in action. We have unified disparate phenomena and set them within a causal framework that draws on precisely defined notions needed in a theory of agency. In that way, we begin to explain them together.

5.8 The Norms of Attention Attention is a target of normative assessment, and the panoply of biases on attention provides a map for such assessments. Is attention normatively evaluable (Watzl 2022)? If attention is part of action and action is normatively evaluable, then attention will often be as well. Consider recent work on the norms of attention through the notion of salience. Philosophers have suggested that “we can harm someone simply in virtue of making certain things salient about them” (Whiteley 2022, 193), that we can be held responsible “for what is and is not salient to [us] in the first place” (Archer 2022, 114), and that “evaluations of an agent’s character as virtuous or vicious are properly influenced by what the agent finds salient or attention grabbing” (Yetter Chappell and Yetter-Chappell 2016, 449). Theoretical accounts of salience forge a link to attention. Recall our initial analysis: A target T is salient to subject S relative to a background set B iff S attends to T rather than elements of B. Given the theory of attention as guidance in action, we can derive A target T is salient to S relative to a background set B iff S selects T to deal with (act on) rather than elements of B. Given the structure of agency, we get A target T is salient to S relative to a background set B iff S selects T rather than elements of B in solving the Selection Problem. Finally given the necessity of bias in action:

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A target T is salient to subject S relative to a background set B iff S is biased to T rather than elements of B in solving the Selection Problem. There is no controversy that certain biases can be normatively evaluated. Thus, if certain biases are good or bad, then the resulting forms of attention, hence salience, can be brought into the same normative perspective. For example, biases that harm can explain the harm associated with salience because the agent’s attention guides generation of harms. Biases for which we can be held responsible in having or not having can explain responsibility regarding what we find salient or what we miss, and so how our actions develop, or not. Biases characteristic of specific virtues or vices can lead to correlated salience because our attention reflects virtuous or vicious aspects of agency. The possibilities for normative evaluation are potentially as diverse as the biases that influence what we do and what we attend to. The causal map of biases identifies different targets of assessment (Figure 5.1). Where we have attention, the agent is doing something, and evaluation of that doing need not just be concerned with what is done but also how it is done. In a given context, there need not be anything problematic with having a conversation with a man and a woman, but should one’s visual attention when speaking be biased toward the man and withdrawn from the woman, then that part of action, how attention drives one’s response over time, can itself be a target of evaluation. For example, the visual selectivity might be intentional, the speaker intending to snub the woman. This yields a new action description under which the act is intentional, say the speaker addresses the man so as to snub the woman. More often, the bias is automatic and the speaker is unaware of doing so. Consider a case where the automaticity might be judged excusable impoliteness, for the man is a good friend whom the speaker has not seen for years, and if the speaker’s visual partiality is pointed out, he might excuse himself: “I’m sorry, I’m just so excited as I haven’t seen my friend in a long time.” Here, one’s emotions might be the primary bias, and in many contexts, the bias is understandable, even excusable. Our normative assessment of bias will depend on the evaluative standards, the details of the situation, and on the psychological profile of the agent, among other factors. On the assumption that some negative assessment of the speaker’s behavior is warranted by the standards deployed, what we have discussed is a family of biases that can explain the agent’s partiality from an explicit intention to snub, a positive emotion toward the biased target, or the historical influence of past reward and learning. Thus, how one thinks, feels, has been brought up, and how one’s world is structured provide targets for evaluation centered on attention. Crucially, the causal structure of bias in Figure 5.1 also provides a map for targets of normative assessment. It identifies biases relevant to understanding good and bad action. The normative evaluation that begins with salience but is unpacked in terms of the structure of agency focuses on actual attention. Yet we are also concerned with

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what we might call the attentional character of an agent, what they are poised to attend to in a particular context. This is tied to patterns of vigilance, and more broadly attunement, which is also a target of normative evaluation, for the biases that yield attention begin with biasing the agent’s attunement toward the objects to be attended. This is tied to a modification of the structure of the action space that influences how the Selection Problem can be solved. Thus, one’s attending to a target reflects one’s attentional character, what one is vigilant for and attuned to (Section 2.1, Figure 2.3). Such attentional character matters for it is what we must change to establish appropriate automaticities, as we noted when talking about intentional debiasing as a basis for habituation in the examples of queue moderation and coming up with conference speakers (cf. Archer 2022 on the “epigenetic” aspect of salience). Attentional character, and associated capacities for vigilance and attunement, are important elements of virtuous character and, ultimately, virtuous action (cf. Yetter Chappelle and Yetter-Chappelle 2016).

5.9 Taking Stock Skill as exemplified in good action requires concerted learning, training, and practice to transform and sustain appropriate biases. Biases that influence attention are often acquired through experience. We learn to be biased. In explicit training, students submit to the standards set by teacher and practice. They begin with explicit control in intentionally bringing about specific features in action by repetition: a doctor’s focusing on guiding features in an image or a performer drilling bodily movements to learn the correct technique. Concerted and consistent practice sets automaticity that obviates keeping the relevant features explicitly in mind, marking a transition in what the agent can directly intend. The goal of learning is to instill correct automaticity. Much learning, however, circumvents intention. We acquire many biases automatically, i.e. without intending to. Our intentional action depends on but also battles against various automaticities that arise from different sources. Further, over time, it is easy to acquire bad habits in attention, and appropriate attentional form can deteriorate even at the highest levels. Thus, experts continue to practice, interact with teachers and coaches, to ensure that the biases trained in are maintained and calibrated. Where bad habits arise, where form regresses toward amateurism, the agent “gets back to basics.” This might involve retraining certain features of action that were last trained up in a concerted way during initial learning. One has to think again, to intend explicitly to do what was previously automatic but lost due to degradation of skill. This might involve doing basic actions directly in order to retrain an automaticity in one’s ability that has degraded. There is nothing shameful in this retraining. It is part of the normal practice of acquiring, developing, and maintaining skill.

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I have argued that attention is an important facet of skilled action. There are ways of deploying attention skillfully and ways of deploying it in an amateurish fashion. Further, there is a continuum from amateurism in the novice to the highest levels of skill in various human practices, a continuum defined by ideals and norms regulating those practices. In recognizing that attention is an essential part of skill because it is necessary for action, we must also recognize that it can be established, developed, and maintained only through appropriate training. Where attention is not appropriately directed, this reflects an absence of or a failure in learning, the acquisition of bad form or the deterioration of know-how. Accordingly, shifting bias begins with recognizing that where automatic bias is treated by the community as negative, it implicates violating a norm or ideal. I argued in Chapter 4 that agents have special access to intended aspects of action. Yet most of the features of action, such as problematic biases, are automatic so are not rooted in intention. These automatic features will not be accessible to the agent through intention. They will need to be brought to her attention in other ways, say by her own observation or through the testimony of others. For this reason, addressing bias will often not begin with the agent’s own recognition, but will rely on others. We must be receptive to these sources precisely because of our own blind spots to automaticity in action. Even the most skilled can be blind to decline in form. Academics who profess egalitarian ideals, whose intentions are good, are often the hardest to change because they are often biased against evidence of lingering sexist or racist tendencies. Yet we all automatically drift from our ideals. Those of us who have actively trained to acquire athletic, artistic, or other skills have learned to be receptive to criticism of our form. Whether we live well is also appropriately subject to criticism, and we should learn to be receptive to corrections relating to deterioration of that skill. How should we deal with problematic biases? By focusing not on elimination per se but on transformation. Bias is necessary for action, so it is a matter of ensuring that we acquire the correct biases by concerted learning and then maintain those biases by reassessing our performance. Rather than thinking that there is something wrong with someone, we might more productively think about how they can improve their skills. This is exactly what the most skilled agents do. Focused training and practice, practice, and more practice. While understanding the underlying causal processes is not necessary to mitigate a problem, such understanding provides an important route to informed improvement. In the case of eye movement, we know that training shifts how our eyes process the visual world (cf. the radiologist). In cases where biased eye movement provides the foundation of negatively biased behavior, training begins with intention. I noted the institution of student-first rules to direct moderation in question periods, and moderators should treat such tasks as opportunities for practice and look for further opportunities to reinforce learning. Moderating questions after talks is but one infrequent moment in academic life. It does not

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training make. One might as well try to learn an instrument by practicing once a month. The length of training required to shape eye movement in the expert radiologist underscores that transformation of bias cannot be a one-off thing (though, see the concerted and intense training regimen described in Chen et al. 2017). The diversity seminar has become obligatory in many universities, and while it is helpful to deal with bias in an explicit way, it is not enough just to attend a seminar or to simply be aware of the existence of bias along with an intention to do better. This is perhaps necessary but not sufficient. We don’t want the doctor who comes in to examine our chest x-ray to be repeating a classroom-learned checklist in her head and an intention to do her best. We want her to have her attention automatically lock onto the desired target. We want her to be well-trained, practiced, and skilled. We want to create equal opportunity in the academy and the world. In coming up with lists of speakers, contributors, or finalists for graduate school and jobs, we have come to be more intentional about ensuring equal access. Yet it is not enough for the chair of the committee to announce the importance of noticing underrepresented candidates and then leave it to the good intentions of committee members. Automaticity skirts control (see Section 5.2). The only way to combat this is to mold automaticity by appropriate habituation. That is hard but necessary work (for additional discussion, see Wu, forthcoming b).

Note 1. A comment on mind wandering: There has been growing interest in mind wandering as a psychological phenomenon. Mind wandering is typically passive cognitive attention, so contrary to intention. Zachary Irving (2016) follows Frankfurt in invoking guidance as crucial in intentional action, so that mind wandering is “unguided” attention. My use of “guidance” is technical and contrasts with Frankfurt’s (Section 1.9). To integrate discussion of mind wandering in line with a general theory of agency, note that mind wandering typically involves the absence of control in its automaticity. Mind wandering is the agent’s automatic, indeed often passive, cognitive attending to thought contents. There is an initially odd category of intentional mind wandering, but I think this fits nicely with Galen Strawson’s (2003) idea of stage setting, so mere ballistics (Seli, Risko, and Smilek 2016; cf. Murray and Krasich forthcoming). Even in such cases, what is missing is thoroughgoing cognitive randomness which would not amount to action, passive or not. Rather, thinking involves thoughts interconnected in a variety of ways. One way is that conceptual capacities can be exercised in a variety of thoughts. We see this in modus ponens where the conceptual capacity exercised in the unconditional premise is also exercised in the conditional premise (see Chapter 6). In general, in thinking thoughts about T, we prime other thoughts about T in the cognitive field. In attempting to solve a problem regarding T, we can let the mind meander from a

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cognitive field through action space, letting the connections go automatically, often via conceptual associations. Think of trying to recall the name of the parent you met at last night’s school event. Once one intends to do that, there is nothing left to control, else there would be no need to recall, for one would intend to find a name one already has. We are better off letting automaticity take over. Allowing the mind to wander is a way of exploring the space of possibilities constrained by intention in order to solve a problem. In such cases, the mind wandering is intentional in that one aims to solve a problem, say recall a name or figure out the best decision. Intention controls the space of automaticity, but the precise contents that arise, even the name or decision, will be automatic. This schema also fits motor control (for my thoughts on this, see Wu 2008, 1010; cf. Burnston 2021 and also the issues arising in the interface problem in Butterfill and Sinigaglia 2014; Mylopoulos and Pacherie 2017; Burnston 2017b; Brozzo 2017; Shepherd 2018). As we shall see when discussing pitfalls in introspection in Chapter 7, introspection of mind wandering as typically passive episodes will not obviously be accurate. At the very least, such introspection will not bear the marks of the privileged access the agent has of her intentional action. An oft cited early estimate of the amount of mind wandering in normal waking life pegged it at around 40% (see Seli et al. 2018 on questioning how we assess the frequency of mind wandering). Weinstein et al. (2018) gathered evidence that framing effects can influence detection of mind wandering episodes during a reading task, namely when subjects were asked to reply yes/no to (a) “my mind was on the text” or (b) “my mind was on something other than the text.” Subjects reported more mind wandering when responding to (b) rather than (a). Vinski and Watter (2012) provide evidence that subjective reports of mind wandering are susceptible to demand characteristics where subjects surmise the hypothesis behind the experiment and adjust their reporting accordingly (Orne 1962). Specifically, subjects would overestimate mind wandering (here, report and behavioral measures would tend not to align). We return to general issues about introspective accuracy and reliability in Chapter 7, but work on mind wandering will need to resolve the challenges of noise and bias in introspective reporting. See Smallwood and Schooler (2006) for a review of measurement methods for mind wandering.

6 Deducing, Skill and Knowledge 6.1 Introduction Reasoning is the deployment of skilled cognitive attention.

At root, reasoning is entertaining a sequence of thoughts where one thought follows from another. “Follows from” means more than temporal or causal sequencing, but what more? Consider two sequences whose contents fit modus ponens. Case 1—Mere Causal Sequence: Subject S first entertains (1) the thought that if p then q, then (2) the thought that p, and finally (3) the thought that q. Case 2—Reasoning (Deducing): S entertains (1) the thought that if p then q, then (2) the thought that p, and then (3) concludes that q. What is the difference between these two cases? Not that the sequence instantiates the form of modus ponens. Not that they follow in an appropriate order or temporal sequence. Thoughts with these properties do not suffice for reasoning. Here is a simple answer: Case 2 must be an action while Case 1 need not. Indeed, Case 1 could just be a form of causal deviance, not “caused in the right way.” If so, it recapitulates a crucial omission seen in causal deviance, attention as the guiding thread (Section 2.10). This means that Case 2 involves exercising representational capacities that are action relevant and whose coordinated expression yields coupling constituted by attentional guidance. Accordingly, there is a guiding feature for deduction that is the target of attention. A theory of reasoning must identify what this guiding feature is, how attention to it guides formation of the conclusion, and how the underlying capacities are acquired. Reasoning is an action that instantiates the structure discerned in Part I. Deduction involves the agent’s narrowing of cognitive attention from premises to conclusion. Such cognitive focusing is something we learn, coming to know how to reason through practice. Learning directs attention to relevant guiding features, whether semantic or syntactic. Rules of reasoning contribute to control through intention. Specifically, rules set attention. Explaining these ideas is the task for this chapter.

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0007

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6.2 Deducing with Models Deducing, on semantical accounts, is constituted by sharpening cognitive attention in moving from premises to conclusion where said premises provide cognitive cues for attention. Some have argued that a capacity for modus ponens is hard-wired into our cognitive architecture (Eisenstadt and Simon 1997; cf. Quilty-Dunn and Mandelbaum 2018). The ability to do basic conditional reasoning is found early in development: 6- to 8-year-old children can reason correctly with conditionals including drawing modus ponens (Markovits et al. 1998; Markovits 2000). Still, children and adults make mistakes in conditional reasoning, often inferring invalidly such as affirming the consequent (if p then q, q; so p) or denying the antecedent (if p then q, not-p; so not-q). There are also conditions in which subjects who are able to reason according to modus ponens in one context will fail to in another. Ruth Byrne (1991) showed that subjects who can draw the right conclusion from if p then q and p will not always draw the conclusion q if an additional premise is added, say if r, then q. This exemplifies what Byrne called a suppression effect (see also Cariani and Rips 2017). Perhaps more premises distract appropriate attention leading to incorrect response (see slips of action in Section 3.6). Empirical theories of deduction aim to explain both good and bad cases of actual reasoning in human agents. I will not adjudicate between different theories as research is ongoing. Still, any plausible model of inferring must fit the structure of agency, and thus the specific role of attention. Specifically, any model must identify guiding features as targets of attention in deduction. For heuristic purposes, I discuss mental model theory, one of the dominant accounts over the past decades. Nevertheless, any plausible account of deductive reasoning must explain appropriate attention given that deduction, as an action, must solve a Selection Problem. The mental model approach was developed largely by Phillip Johnson-Laird and Ruth Byrne (Johnson-Laird and Byrne 1991, 2002; Byrne and Johnson-Laird 2009). This approach deemphasizes syntactical rules (cf. Rips 1994). Rather, to deductively infer, a subject evaluates premises by considering possibilities. The subject-level phenomenon of considering possibilities is constituted at the algorithmic level by the construction and manipulation of semantical models of those possibilities. Once appropriate models are constructed, a model corresponding to the conclusion is arrived at, so long as the model is consistent with all models of the premises. Computations over mental models constitute or realize the agent’s deducing propositions from premises. It is critical to separate subject-level phenomena from those below that level. Talk of considering and reasoning from premises, interpreting them and attending to guiding features are at the level of the subject, the agent’s activities and

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actions. The construction and manipulation of models, however, are not done by the agent, but by a computational system that realizes the agent’s subject-level activities and actions. That said, a well-confirmed computational model can motivate recharacterizing how to understand the subject’s grasp of relevant premises, such as conditional claims. Take the case of modus ponens. Given the conditional if p then q, an initial step is that the subject entertains the possibility where p and q are the case. We begin treating such “entertainment” as a cognitive taking, an input in the agent’s action space. When the subject reflects and entertains a specific claim, then we have cognitive attention to that claim. This has a similar structure as seen in covert perceptual attention, namely an input that maps to itself (Section 2.5). Entertaining a claim corresponds to maintaining an item in working memory (Figure 3.2). The Argument from Targeted Amnesia emphasizes that we must retain relevant premises to be able to respond to them (Section 4.2). Now shift to the algorithmic level. On mental model theory, a model is constructed corresponding to the premise the subject entertains. For ease, I’ll refer to the model where p and q are the case as [P, Q]. While it would be natural to think of the models as consisting of propositions, Johnson-Laird and Byrne think of them as involving “mental tokens” that do not reflect linguistic or conceptual structure but are iconic: “in reality individuals use iconic mental models of actual situations—iconic in the sense that the structure of a model corresponds to the structure of what it represents” (Orenes and Johnson-Laird 2012, 360). The nature of these “tokens” will not matter to our discussion. It is enough to note that when the agent considers a possibility this entails that a computational system constructs a corresponding model. Take the mental model as corresponding to a line on a truth table where p and q are true. On its own, [P, Q] does not distinguish between the subject’s entertaining a conditional versus entertaining a conjunction, p & q, since both sentences are true if p and q are true. Further models must be entertained to separate the subject’s entertaining a conditional from her entertaining a conjunction. In mental model theory, a fully explicit model of the conditional as the material conditional includes [not-P, Q] and [not-P, not-Q], models not entertained when thinking of conjunction. Finally, the model [P, not-Q] is not constructed as it is not a possibility if the conditional is true (Johnson-Laird invokes a Principle of Truth guiding what models are considered: “Each mental model of a set of assertions represents a possibility given the truth of the assertion, and each mental model represents a clause in these assertions only when it is true in that possibility” (Johnson-Laird and Byrne 2002, 653; also, Johnson-Laird 1999, 116)). So, in considering the premise if p then q, the underlying computation involves constructing three models. This constitutes the subject considering relevant possibilities in taking if p then q to be true, say because the subject believes the conditional claim or supposes its truth for sake of argument.

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Among the constructed models, some are explicit, others implicit. Mental model theory assumes that model construction is constrained by working memory capacity: The more information that has to be represented explicitly, the greater the load on the processing capacity of working memory, and so the initial models of a proposition represent as much information as possible implicitly. Implicit information is not immediately available for processing, but it becomes available when it is made explicit. This process adds to the number of explicit models to be taken into account in reasoning from the premises. (Johnson-Laird, Byrne, and Schaeken 1992, 421)

To avoid cognitive overload, when reasoners are considering a conditional premise, the subpersonal computations explicitly construct only [P, Q] with the other models [not-P, Q] and [not-P, not-Q] implicit as a “mental footnote.” As only [P, Q] is maintained in working memory, then only that possibility is cognitively attended. The other relevant models are not, but the subject is vigilant for them. She is ready to attend to them. At the subject level, in entertaining the conditional claim, the agent makes salient one possibility over others though the others remain accessible. For example, if the agent considers not-q, this can render salient the implicit model [not-P, not-Q] so that it is now attended to. On mental model theory as we are construing it, a subject’s considering the material conditional if p, then q can involve different orientations of cognitive attention toward possibilities entailed by the conditional proposition. These differences are captured in shifts of attention and vigilance driven by the underlying changes in the computation of relevant models. We might say that “full grasp” of the semantic import of the conditional involves attention to all relevant possibilities but that the immediate comprehension of the meaning of a conditional can differ along dimensions of vigilance and attention to relevant models. I have been construing the algorithmic construction of models as realizing or constituting the subject’s entertaining relevant possibilities in assessing the premises. For assume that the postulated computational processes are not constitutive of the agent’s involvement, her reflecting on the premises if p then q and p so as to conclude q. These computational processes determine that q is consistent with the relevant possibilities that model the premises. Having completed this assessment, the system then induces the subject to think q after she considers the premises. Yet all that this would engender is Case 1, not Case 2 (Section 6.1). The resulting story recapitulates a defect found in causal deviance in that the agent is not involved in the relevant process, here in considering relevant possibilities during inference. Her guidance of her response is missing. Rather, something that is not constitutive of her involvement selects the relevant models to evaluate to ensure valid inferential behavior. By hypothesis, this is neither the agent nor

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constitutive of her doing so. We have instead the bubbling up of an appropriate thought as envisioned in Case 1 due to a process distinct from the agent’s own doings. So, if mental models are to explain deducing as an agent’s acting, then the proposed computations must be understood to constitute the agent’s own reflection of relevant possibilities that inform her drawing a valid conclusion. For this reason, if mental model theory were correct, the dynamics of attention and vigilance capture different ways of understanding a proposition via different ways of making salient different parts of its content. Mental model theory must then specify the guiding feature that the agent attends to in deducing a conclusion. On that theory, the subject’s considering premises is her entertaining specific possibilities in order to draw appropriate conclusions. In the case of modus ponens, the agent concludes q on the basis of the possibilities associated with the premises: if p, then q and p. The agent’s cognitive attention must be initially directed at relevant possibilities, the guiding features. Her attention to these features must then inform the conclusion that she draws. How so? To track the dynamics of cognitive attention in deduction, let us consider an analogy to perceptual attention. Consider the cuing paradigm for visual spatial attention (Posner 1980). Typical implementations involve the subject’s maintaining fixation while doing a target detection task where the target can occur on the left or right of the fixation point (for more discussion, see Wu 2014). The subject must indicate the location of a target by a key press. A cue facilitates target detection. Some cues are direct, occurring at the target location with, say, a probability of 80% validity (target appears at cued location) and 20% invalidity (target does not appear at cued location). Indirect cues are often symbolic cues that occur at fixation, say an arrow that points to one of the two locations with similar probabilities of validity/invalidity. The standard behavioral effect is that valid cues facilitate detection by increasing accuracy and decreasing reaction time. The cue facilitates appropriate attention. Analogously, the non-conditional premise, here p, serves as a cognitive cue for modus ponens. With the conditional premise in modus ponens, the underlying computational processing constructs three models: [P, Q], [not-P, Q] and [not-P, not-Q]. For simplicity, I will treat all possibilities as explicit, so actively maintained, hence remembered in working memory, over time. We can speak of a corresponding cognitive field (Section 5.7) that is characterized attentionally as earlier: specific possibilities guide response. This cognitive field, a way of characterizing the relevant inputs in an action space, constitutes the agent’s grasp of the truth-functional import of the material conditional. The agent’s cognitive attention is then focused on three possibilities. Now, in considering the second premise, p, the subject entertains the possibilities corresponding to its truth, so a model is constructed: [P]. Given [P], the subject entertains that p. In the model, [P] functions as a pointer to the relevant

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model among those entertained just as a visual cue in spatial cuing points to a relevant location for the target. The model for the non-conditional premise, [P] points to the model [P, Q] rather than [not-P, Q] or [not-P, not-Q]. In effect, at the subject level, in thinking that p, the agent is cued to think of the possibility where p and q hold rather than the not-p possibilities. After all, the representational capacities needed to entertain [P] are also deployed in entertaining [P, Q]. One’s cognitive attention to p makes salient [P, Q]. The other possibilities are no longer relevant, given p. Consequently, the subject’s cognitive attention in respect of the premises has narrowed to the task-relevant possibility (cf. receptive field contraction; Section 1.6). The analogy between inference and spatial cuing in visual attention is not fortuitous. It reveals the principle illustrated in every action: the Selection Problem must be solved if we are to achieve our goals. Inferential and visual cuing instantiate the required selection. In lay terms, successful action involves appropriate focus. Once a subject considers the possibility that p and q, she can naturally conclude q for she is already thinking that possibility: [P, Q]. In thinking simply that q, she further narrows her cognitive attention to a specific aspect of the possibility entertained. In drawing the conclusion that q, she re-cognizes the relevant possibility already present in thinking if p then q and p. To think that q is to re-think what is already grasped in having focused cognitive attention on the relevant possibility constituted by [P, Q] given the premise cue in entertaining p. Recognition is expressed in the reapplication of the concepts in the conclusion that are deployed in entertaining the propositions in the premises. Inferring as per modus ponens is narrowing cognitive attention in a way that reveals it as non-ampliative. Attention in action necessarily guides response. Guidance also allows us to make sense of fallacious reasoning as the inappropriate deployment of attention, perhaps due to cognitive limitations. For example, consider affirming the consequent: if p, then q; q, therefore, p. A child whose working memory limitations lead her only to entertain the possibility associated with the model [P, Q] when considering the conditional will likely respond to q in affirming the consequent as she responds to p in modus ponens. In both cases, the minor premise will serve as a cue to the relevant possibility, by hypothesis, the only one that she cognitively attends to given working memory limits: [P, Q]. As the child’s conclusion will be guided by attention to a single possibility, she will in this case draw a conclusion whose truth is not guaranteed by the premises properly understood, so she infers p. Consequently, she affirms the consequent. This fallacious reasoning is intelligible given the narrow focus of her initial cognitive attention. We might say that in this instance, she inadequately understands the premise. In effect, she receives an invalid cognitive cue that prompts an incorrect response for she is not prompted to the right model, [not-P, Q]. If this model is not available to her, perhaps we should not say that the child understands the conditional for she does not differentiate between conditionals and conjunction.

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Consider a similar condition with an adult who is not considering, that is attending, to all relevant models though she is vigilant for some of them. This adult reasoner will also be prone to the same mistake, a failure to reason correctly. If there is a failure along the lines of the child, say fallacious reasoning by affirming the consequent, the failure is attributable not to misunderstanding (or incomplete understanding) but to a failure of proper vigilance, namely of readiness to attend to an appropriate possibility. Unlike the child, the adult has access to a relevant possibility, [not-P, Q], which along with [P, Q] should give her pause. Nothing in the models requires concluding p since q is compatible with not-p. In contrast, for the child, given cognitive limitations, concluding p makes sense. This marks a difference between child and adult worth further analysis: we might say that the child wasn’t able to think through all relevant possibilities, a limitation explained by her memory capacity, while the adult was able to do so but failed to notice them. We might excuse one failure to a lesser degree than the other, and this differential assessment recognizes different cognitive capacities. In any event, in both good and bad cases, attention is the central thread in understanding guided thinking in deduction. This will be true, indeed must be, for any plausible theory of deductive inference as action.¹ Deductive inference involves more than modus ponens, and a skillful deducer will be able to draw conclusions from more complicated premises. As premises become more complicated, the cognitive field will be more cluttered and subject to cognitive limitations and distraction. Keeping the appropriate possibilities in thought and finding the appropriate conclusions within the clutter will involve cognitive search facilitated by cues of various sorts but also by the agent’s prior experience regarding the topic or inferences of similar types, experience that can lead to different cognitive biases that influence how successful we are in reasoning (Section 5.7).

6.3 Formally Deducing and Learning Rules Rules typically contribute to control, rather than to guidance, and in this way a reasoner can explicitly invoke rules. Let us turn to formal deduction in symbolic logic, and consider a different guiding feature, logical form. Formal accounts of the psychology of deduction postulate a recognitional capacity (for related approaches, see Zalabardo 2011; Besson 2019). Syntactical approaches have fallen out of favor in the psychology of deduction as the mental model theory ascended. Yet we can successfully teach symbolic logic, so students can learn to attend to the relevant syntactical-logical properties. This takes practice. To bring out the core agentive features, I focus on reasoning according to modus ponens.

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A developmental approach facilitates understanding action capacities by probing how they are acquired. Learning how to do something involves doing it, initially clumsily, inefficiently, with effort, directing attention to details, and fine-tuning intentions to perform subactions. Later, one need not think about these details, but simply acts. Intending becomes direct (Section 1.8). My hypothesis is that one can learn to infer by producing instances of argument forms even before one learns the governing rule. The crucial step is not coming to explicitly use a rule but developing a recognitional capacity for logical guiding features. This can be achieved independently of the agent’s knowing and applying a rule. Indeed, most recognition works this way. We recognize faces, places, flavors, and smells without having formulated rules for doing so (if we had, computational approaches to perception would be much easier). Similarly, we can recognize logical form without having formulated and thereby cognized formal rules of inference. Attention to appropriate features guides response. Rules are not, of course, otiose. A logician can intend to apply the rule of modus ponens, say in deciding to lecture on that rule. The rule occurs in her intention, in her conception of what she will do, and the intention accordingly biases appropriate action-relevant capacities. In intending to apply the rule, she readies herself to act, writing lecture notes, choosing appropriate examples, and deciding on heuristics to instill the capacity for modus ponens in her students. The rule represented in her intention contributes to solving the Selection Problem, biasing attention to relevant logical properties. Indeed, the instructor’s goal is to initiate joint attention with her students so that the students effectively come to attend to the same guiding features that she does. So long as she operates according to the rule and can teach effectively, her students come to attend to guiding features without themselves invoking the rule. The reach of the rule is indirect, biasing their behavior through their teacher’s grasp of it. If the teacher is effective, her instructions can fill the necessary gap between the student’s intention to reason in a particular way and the student’s actually drawing the appropriate inference. Instruction eliminates practical ignorance and allows thought to link to action. Gilbert Ryle once told this story: A pupil fails to follow an argument. He understands the premisses and he understands the conclusion. But he fails to see that the conclusion follows from the premisses. The teacher thinks him rather dull but tries to help. So he tells him that there is an ulterior proposition which he has not considered, namely, that if these premisses are true, the conclusion is true. The pupil understands this and dutifully recites it alongside the premisses, and still fails to see that the conclusion follows from the premisses even when accompanied by the assertion that these premisses entail this conclusion. So a second hypothetical proposition is added to

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his store; namely, that the conclusion is true if the premisses are true as well as the first hypothetical proposition that if the premisses are true the conclusion is true. And still the pupil fails to see. And so on for ever. He accepts rules in theory but this does not force him to apply them in practice. He considers reasons, but he fails to reason. (Ryle 1945, 6)

Poor kid! Given a poor instructor, we can understand why the pupil fails to recognize logical consequence. One might as well teach violin by shouting instructions over and over (Section 1.8). The student has no idea what to focus on and how to respond. The rule fails to help. For the rule to be effective, it must direct the novice’s attention so that the required response is appropriately guided by logical form. How precisely attention shifts in reasoning “syntactically” in accordance with modus ponens is an open question that is not to be uncovered from the armchair. The enlightened teacher’s strategy, at least, gives a possible clue. In her voice: When I write down the argument before the student,

I point to the logical connective and the sentential variables or sentences connected. I prefer the arrow notation because it suggests a path from antecedent to conclusion, and here I use analogies to render logical flow salient like an indirect cue in visual search. The arrow points to the deductive target. So I speak of a bridge: as long as I have p, I can cross over to q. Then I point to the second premise and emphasize that p is present, now pointing to the antecedent, moving my pencil across the arrow to q which I then write down.

Drawing on her testimony, we can formulate a hypothesis regarding what the teacher is attending to in thinking through the premises in this methodical way by “slowing down” her thought, and what she hopes to inculcate. Following these instructions, the pupil’s eye might start at the arrow, move from p to q in (1), attend to p in (2) then move back to p in (1) with a jump to the arrow and then to q. This postulates a parallel between visual attention in this case and cognitive attention in mental model theory: the unconditional premise serves as a cue. Now, the student, under instruction, writes q. I doubt the pupil’s eyes move in exactly that way although if the teacher uses a pointer to direct visual attention, this might happen at the early stages of learning. How the eye would move in the proposed context is an empirical question, but we might predict a bias here as we observed in the radiologist (Section 5.5). What is crucial is what is attended so as to guide action, and talk of eye movements is just a heuristic to pick out the relevant

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features. The idea of guiding properties entails that attention must be anchored on those properties if action is to be appropriately guided. Training a student to construct proofs, where the student intends to learn, inculcates a recognitional capacity that involves appropriate conceptual response to logical form. One does not need to know the rule of modus ponens to recognize a repeated form in these instances:

In this case, what is inculcated is responding with Q or T respectively on attending to the appropriate formal feature. Similar patterns are presented in various contexts and with alterations:

Exposure to additional examples expands the subject’s recognitional capacities and trains up propensities to move along certain paths in action space. Attention is tuned so that the subject takes possession of one thing while withdrawing from others. The student comes to know how to deduce as per modus ponens within the given formal system by learning to recognize a pattern. This is an acquired actionrelevant capacity given an intention to do so (for an overview of an automated tutor system for natural deduction, see Sieg 2007). What the student acquires in learning is what psychologists refer to as a schema. Schemata identify what we learn through appropriate practice. They have the following properties: • Schemata provide a structural (algorithmic) description of the acquired knowledge. • Schemata are remembered, retained over time. • Schemata are set by extracting shared structure in exposure to multiple instances of relevant targets. • Schemata are more abstract than any given instance in the subject’s learning history. • Schemata regulate remembering and perceiving, that is information processing. • Schemata are often procedural, so tied to producing a behavioral output. • Schemata are often hierarchical, so schemas can be embedded in schemas. The notion of a schema has already been invoked though not so-called. When we considered Φ-ing by doing X, Y, and Z in Section 1.8, we effectively

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schematized Φ by breaking it down to subactions. In schema theory, the schema for Φ-ing would involve ordered representations (sub-schemata) for X, Y, and Z that constitute the complex schema to Φ (Rumelhart and Ortony 1977). In a learning context, it is the teacher who imparts her understanding by communicating relevant aspects of the schema she herself acquired. In doing so, she aids her students in directed practicing that inculcates the same capacity. If the student successfully learns, then in intending to Φ the student will do X, Y, and Z. She will have acquired the teacher’s schema and in that sense, she will know how to Φ by doing X, Y, and Z. Schemata make theoretically salient the structural changes acquired in learning to act. To acquire a schema tied to modus ponens is to have learned something that enables one to respond in the way one was trained when confronted with the appropriate guiding features. One brings what was learned, that is knowledge, to bear, and the deployed schema directs attention to the guiding feature. Since attention is set in intentional behavior by intention, this suggests a link between capacities for intention and knowledge learned through practicing action. Schemata are a basis of the agent’s having learned how to act. I discuss this link between intention and knowledge in Sections 6.5 and 6.6. We can now say this regarding rules. Rules can play a role in learning even if the learner does not yet explicitly know the rule. A teacher can know the rule and use it to inform her teaching. Rules direct attention. On the one hand, they can do so intrapersonally, through the subject’s intention to act according to a rule as in the teacher deploying it in lectures and lesson plans. On the other, rules can take hold interpersonally as exemplified in instruction guided by the teacher’s intention. The student benefits from the rule not by applying it—the teacher might save the formal expression of the rule for last—but by being guided indirectly by the rule. If the teaching is effective, it instills a capacity for logical recognition independently of explicitly using a rule. The student automatically conforms to the rule. In completing a course on logic, the student learns the rule explicitly, but this inverts the method Ryle described. Learning the rule can come at the end. By then, the bulk of learning to do things aright has been completed.²

6.4 Taking and Sensitivity Taking premises to support a conclusion is grounded in the acquisition of recognitional capacities through rule-based control during learning that avoids Carroll’s regress of rules. Rules have played a central role in philosophical accounts of the contrast between Case 1 and Case 2. To explain the difference, Paul Boghossian suggested the following:

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(Taking Condition): Inferring necessarily involves the thinker taking his premises to support his conclusion and drawing his conclusion because of that fact. (2014, 5) Lewis Carroll taught us that if taking involves an attitude toward the rule in applying it, say a belief that the conclusion follows from the premises, a vicious regress ensues (Carroll 1895). Return to modus ponens. One implementation of Boghossian’s suggestion is that if we take the premises (1) p and (2) if p then q as supporting the conclusion (3) q, we must explicitly cognize (believe) the transition as one where the premises support the conclusion. We can represent this explicitly with the further belief content (4): if p and (if p then q), then q. This is effectively specification of the rule or an instance of it. Yet if this taking provides an additional premise to support the original inference, then one bases (3) on (1), (2), and (4). Now the question arises again regarding how the reasoner takes these premises to support the conclusion. This requires another taking of those premises as supporting the conclusion, with an ensuing regress. Clearly, the use of the rule as a premise is a route to oblivion. Perhaps the most common response to Carroll’s challenge is to deny that “taking” involves an explicit representation of the rule and various options have been explored. This evasion will not solve the problem, however, for as long as there are some cases of inference where there is an explicit taking, where subjects explicitly cognize the rule and act on this cognition, the regress will take hold. And there are. Any competent student of logic can illustrate such a case (as does the teacher writing a lecture). The student understands the inferential rule and in certain contexts will explicitly invoke it, say repeating it as a reminder in a stressful testing context. She can think about the rule in applying it. In doing so, her mind does not overheat or freeze in an infinite loop. The demand to explain such cases does not dissipate by claiming that rules are implicit. This turns out to be an evasion. Carroll’s original challenge remains. Carroll’s regress shows us that we have failed to explain reasoning as inferential agency in a basic case. The structure of action provides a diagnosis: we are asking the rule to play the wrong role, namely that of guidance where the rule is the target of cognitive attention and provides an additional premise. That route leads to regress. Recognition that the rule cannot be a premise tells us something about what the role of the rule must be in action. From the theory of action, the solution emphasizes control not guidance: the rule controls but does not guide action. It biases appropriate attention, say through intention. For example, cognizing a rule in explicitly intending to act according to it, as with our student reminding herself of the rule in the exam or the logic instructor’s writing a lecture, biases inputs in an action space so that specific takings of guiding features inform appropriate conclusions. The rule sets attention. It contributes to bias. This allows us to appeal to an explicit attitude of taking in respect of a rule that avoids the regress. The solution

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to taking is, as it should be, revealed in the solution inherent in confronting the Selection Problem (see Pavese 2021 for a recent proposal). I have noted that one can learn to act according to a rule that one does not explicitly know in learning deduction. This is probably the statistical norm. In logical education, we ultimately learn the rule, so we acquire knowledge of it. This allows us to bring knowledge to bear to guide extant capacities for inference, but it is knowledge that controls rather than guides response. If rules are tied to knowledge, then knowledge can contribute to solving the Selection Problem as a bias, by setting attention to the appropriate guiding features. In inferring “in the wild,” no explicit taking of the rule needs to occur in one’s intention so long as one is a well-trained reasoner who has learned to respond accordingly, indeed automatically. We have reasoning even here because the competent reasoner exercises an inferential action capacity inculcated in a normative practice that emphasizes training by attending to syntactical or semantical features. We saw the indirect influence of explicit rules in the joint activity of teaching/learning that occurs between teacher and student, with the instantiation of joint attention to guiding features and inferential transitions that conform to the rule. The intelligence of the student’s actions reflects a rule in the sense that the rule informed her learning how to reason, her acquiring an inferential ability. To count as conforming to that rule, she need only exercise that same capacity, irrespective of whether she explicitly entertains the rule or automatically exercises capacities learned under the rule. Human reasoners can form explicit attitudes toward their actions. In inference, they can hold that the conclusion drawn is correct, supported by their premises. The taking condition enshrines such attitudes as necessary for an action to count as inferring. This does not seem to me to be correct. I have drawn a distinction between Cases 1 and 2 by emphasizing the central role of cognitive attention. In deduction this involves cognitive focusing, the selection of a claim from the initial considerations that include it (see mental model theory; Section 5.3). This seems to me sufficient for a form of thinking deductively in the sense of appropriately drawing a conclusion from premises that deductively entail it through appropriate cognitive attention, again typically automatically. That said, subjects can take higher order attitudes toward their actions. It is a distinctive cognitive achievement in being able to not only traverse the space of reasons (Sellars 1963) but to recognize that one is doing so. A child can properly draw modus ponens because she recognizes the conclusion in the premises. She need not be able to trade on the concepts of support, entailment, or equivalent notions. Rather, in properly understanding the conditional, say in forming (“grasping”) the correct semantical models, she deduces validly by appropriately attending to what follows from the content of the conditional—what follows from the model—when appropriately cued by relevant premises (Section 6.2). That said, the Continuity of Practical Memory does impose

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a constraint that the coherence of her action as intentional requires that the agent, a child or adult, be able to recall the context of her action. In Section 4.2, I noted losing the thread of a conversation in induced amnesia abolishes agentive coherence, measured in a loss of an ability to answer why one is acting as one does. Similarly, losing the premises abolishes deductive coherence. Memory of the basis of one’s action provides an answer to the question, “Why did you do that?” Crucially, the form of the answer involves because. In the context of inference, the Continuity of Practical Memory involves one’s keeping track of the premises of the argument in order to maintain coherence. One answers the question, “Why did you conclude p?” or “Why p?” by answering, “Because q” where q is a reason, a step in the “inferential conversation,” and must be remembered to maintain coherence. Otherwise, the entertaining of p amounts to a puzzling, potentially disturbing, cognitive fulmination. In reasoning, the explicit practice of exchanging because for why, and hence the identification of specific paths in the action space, that is the deductive space of reasons, has its roots in a general condition on intentional agency that one remember the basis for one’s response. A practical upshot is that the give and take of reasons in discourse is a way of practicing tuning internal attention on the relevant past, to keep track of one’s thinking as part of explaining why one thinks as one does. Thus, the practice of asking “Why?” and answering it with “because” is not just a social transaction, but a way of exercising a memory capacity essential to reasoning itself. In doing so, the subject can thereby take what she remembers as a basis for what she concludes. The taking condition is a sublimated form of a necessary ability to explain why one acts, an explanation that relies on practical memory.³

6.5 Skill and Knowledge Knowing how, understood as what we acquire in learning and practice, involves the acquisition of schemas. In explaining reasoning, I have highlighted development and learning and, accordingly, the acquisition of action capacities and the ability to deploy rules. The development of action capacities is an achievement that we can characterize as coming to know how to act. Know-how (Ryle 1960) is a topic of intense investigation in recent philosophy (Stanley and Williamson 2001). I will not directly engage with that debate as there are many things we might mean by speaking of knowing-how. That said, know-how is tied to learning: we can be taught and learn how to do things, and in so learning, with practice and understanding, we come to know how to so act. An important form of know-how is linked to the development and refinement of action capacities. Such learning eliminates specific forms of practical ignorance and amateurism.

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Learning is an antidote to ignorance but so is fine-tuning an intention. Practical reasoning can close practical gaps and expand practical knowledge. Such reasoning can be a form of discovery, or rediscovery, of how to do things. Sometimes it is not ignorance that must be addressed but a temporary amnesia. One has forgotten that one can Φ in this way: X, Y, and Z. Reminders can also do the trick, again involving fine-tuning but with external direction, say from a teacher recalling last week’s lesson. At some point, when she is skilled in doing Φ, the agent does X, Y, and Z automatically. A subject who learns how to directly Φ has transformed her action space from one where an intention to Φ engages with action capacities only after fine-tuning its content, as in the beginning of training, to an action space where that intention, without fine-tuning, can cognitively integrate with an action capacity to Φ. Having learned how to Φ, she can do so simply by intending to Φ. Her intention is direct (Section 1.8). No further thinking things through is needed. Learning changes the character of intending and its biasing role. In intending, the agent brings to bear what she has learned, what she knows how to do. The discussion of rules raises a central question: how does conception engender action? This gap between thought and action is illustrated in Ryle’s example of logic instruction. It is not enough for one to know the rule of modus ponens to be able to reason according to it. One can learn a rule by rote memorization, yet not possess the knowledge instilled by appropriate learning. Knowledge properly learnt fills the gap between intending and acting that constitutes practical ignorance. How then does a conceptual representation of an action engage with action capacities? I suggested that intending must engage with action capacities directly through cognitive integration. The learning-based conception of knowing how to act is intertwined with intention. In cognitive science, schemata refers to what is acquired when one learns how to act, picking out the cognitive structure that is established by appropriate learning. A related idea is present in the working memory literature in the concept of a chunk, a mnemonic unity integrating more basic memoranda (recall “CIA” versus “C,” “I,” and “A”; Section 3.3). Chunking is deployed in explaining working memory in highly skilled action, say in expert chess playing (Gobet and Simon 1996). Specifically, chunks are invoked to capture practically relevant structures that experts learn to recognize through dedicated training. For example, welltrained chess players come to recognize relevant configurations of pieces that inform their decision making, say recognizing gambits that render salient through experience-based biases a set of appropriate responses to consider (Lane and Gobet 2012). Similarly, in deduction, we can see the logical form of modus ponens as identifying a chunk, a type of logical form, that well-trained deducers learn to recognize. A chunk can bias response when it is subsequently activated, given the agent’s intending to do what she has learned.

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Schemas and chunks are acquired through experience though given that the former notion seems to be a broader phenomenon, I take chunks to be components of schemas. How to stitch these notions together requires further analytic work, but the key point is that acquired knowledge in the schema literature converges on acquired skill tied to chunking in the working memory literature. What unifies these ideas is concerted learning as the basis of acquiring these structures that explain one’s coming to know how to do things. K. Anders Ericsson and Peter F. Delaney drew explicit connections between working memory and highly skilled behavior in epistemic terms: At the most general level, the essence of the concept of working memory (or that part of memory that works) is that only a minute fraction of all the knowledge, skills, and information stored in subjects’ vast long-term memory is influencing the subjects’ behavior and thought processes at a specific instant of time. Hence, the phenomenon of working memory includes all those mechanisms that maintain selective access to the information and the procedures that are necessary for a subject to complete one or more specific concurrent tasks. (Ericsson and Delaney 1999, 260)

Because I have argued that manipulating memory in working memory tasks is a way to probe the agent’s control through the dynamics of her intending to act, we can similarly think of the knowledge at issue, linked to working memory, as also connected to the agent’s intending to act as the basis of her control in acting. After all, the knowledge learned is the result of intentional performance, and it reverberates to inform future intentional actions of that kind. Learning allows history to impinge on present intention as a bias (Chapter 5). When the agent learns to directly intend to Φ, this is the result of practicing Φ-ing by doing other actions that she already knew how to do directly: X, Y, and Z. Learning to Φ involves coming to do X, Y, and Z automatically. The transformation of her capacity for direct intention tracks her learning how to act in a skillful way. To capture this broader influence of what the agent brings to bear in intending to act, Ericsson and Delaney wrote of long-term working memory which reflects a complex skill acquired to meet the particular demands of future accessibility for information with tasks within a particular domain of expertise. Domain-relevant skills, knowledge, and procedures for the task are so tightly integrated into the skills for encoding of information that the traditional assumption of a strict separation between memory, knowledge, and procedures is not valid for skilled performance. (Ericsson and Delaney 1999, 257; my emphasis)

Relatedly, Guillén Fernández and Richard Morris described the neural basis of schemata as “a framework of acquired knowledge, skills or attitudes implemented

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within a network of connected neurons in which memory traces of associated information have been stored that, when activated, can alter [better: bias] the manner in which new information is processed, including memory encoding, consolidation and retrieval” (2018, 657). When we put practical memory to work, that is when we are intending to act, we bring to bear what we have learned that is relevant to performing the action intended. This broader intelligence is secured by appropriate practice, and influences the character of intentional action. Agents with the same intention but different learning and training histories will bring different levels of experience (knowledge how) to bear (cf. the automatic biases linked to experience and reward; Section 5.2). This knowledge, linked harmoniously with an intention to act through concerted learning, modulates how agents move through their action space. Knowledge jointly contributes to the setting of attention, appropriate responses, and the character of their coupling. Training transforms the shape of intending’s activity. The transformation is not in the content of intention in the sense of what it is about, for before and after training, the agent’s action begins with intending to Φ, an attitude directed at Φ-ing. Sameness of reference is, in the typical case, preserved across training. Consider Ryle’s novice student who intends to reason as per modus ponens. The student has sufficient grasp of the concept to form a plan but is unable to act on his intention directly. When the student has finally acquired the schemaMP for modus ponens, he has learned to recognize appropriate logical form as part of chunking, the acquisition of a deductive schema. Given the same intention to reason according to the rule for modus ponens, he now directly attends to the appropriate guiding feature. His intention is the same, to reason as per modus ponens, but training transforms it as revealed in its directness in cognitive integration. The final section speculates on how learning alters the agent’s understanding of the relevant actions.

6.6 Knowledgeable Control and Practical Understanding Learning shapes the agent’s knowledge of how to act, and this knowledge provides a developmentally based bias on the agent’s action, one often coordinated with the agent’s intention to act in the way learned. When we learn how to perform an action, Φ, so that an intention to Φ shifts from being indirect to direct, we come to know, or improve our knowledge of, how to so act. In part, this will involve the agent’s concept of the action Φ integrating with action capacities when she intends to so act (Section 1.7). In earlier work, I argued that in certain cases of seeing F as an F, one’s possessing the concept F is involved in the seeing, specifically in setting visual attention to F or in attending

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in an “F-way” (Wu 2008). The involvement condition holds that when one takes a target as F, some appropriate cognitive structure is involved in one’s taking in the target to constitute the agent’s attention to that target. In such cases, we can say that seeing is partly cognitive or conceptual, for seeing as constituting attention to the F target involves the concept F. Conceptual involvement requires more than possession of the concept. It explains conceptual biasing of attention toward guiding features. In my earlier discussion, the bias concerned artifact concepts, say HAMMER, as tuning visual attention to action-relevant properties that inform functionally appropriate use of a hammer as a hammer. Action-relevant functional properties constitute the guiding feature of an appropriate response to the artifact. Such conceptual influence is the basis of one form of cognitive integration. One’s deploying the concept of a hammer in an intention tunes visual processing that is constitutive of seeing the actionable features of a hammer under the aspect of a hammer. So, the agent attends to the proper place to grip the tool to guide motor movement. In this way, the concept in intention acquires a practical significance which, for artifacts, is acquired through learning and which can be lost in semantic dementia (see Wu 2008 for further discussion; for discussion of related issues in primate vision, see Mahon and Wu 2015). The development of the agent’s ability to intentionally Φ tracks the agent’s changing understanding of the action learned. Here, the features of a schema noted earlier, the package of acquired knowledge, attitudes, memories, and attunements, are the results of learning that enrich action understanding. After all, in mitigating practical ignorance by learning how to act, the subject thereby alters her understanding of that act. This adds a further dimension over and above cognitive integration by intention. The contrast between the poor student in Ryle’s story and the successful logic student discussed in previous sections identifies two moments in the history of learning that provide a basis for mapping intellectual and practical growth. It also identifies a difference in control in intention. Both students intend the same action, to reason according to a rule or to draw a conclusion as per modus ponens, yet Ryle’s student fails to act correctly while the other automatically sees the answer. Were the students the same individual at different times, the difference between these stages marks a transition where, at the start, the student’s intention is at best indirect but, where it becomes direct, it yields appropriate action simply because she intends to so act (again, Section 1.8). As we saw in the case of vigilance and steadfastness (Sections 3.5 and 3.6), the content of the intention need not change in that, throughout, the agent intends to perform the same action, yet performance can vary in striking ways that track different capacities for control and different learning histories. As there is a change in practical understanding that alters the shape of intentional agency, this must reflect a change in the influence of the agent’s intention.

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Yet given that the intention need not change, it being an intention to Φ across training, an intention directed at the same action (sameness of reference), how does the change in understanding manifest? Learning induces a shift in understanding that is coordinate with the agent’s intention during learning. Having a concept of Φ endows the subject with a capacity to think about Φ. Since we are hypothesizing that the agent continuously thinks about the same action as she learns, the change in the agent’s understanding is not rooted in her altering what she thinks about. It is her understanding of the same act that improves. To capture what is acquired in learning to act, philosophers have discussed the idea of a practical concept or practical senses (e.g. Pacherie 2000; Pavese 2015; Mylopoulos and Pacherie 2017; Pavese 2020). I want to add a developmental perspective to this discussion, driven by reflection of learning to act. To explain how understanding can change while the intention stays constant in the action it refers to, consider the holism of practical significance. We can think of the practical significance of thinking about Φ in terms of the capacities that are activated when one intends to so act. When learning, the subject acquires a panoply of capacities, captured in the idea of a schema that allows her to change how she engages with her action space. What precisely makes up the constituents of a schema is something that I set aside beyond the general characterizations noted earlier, the set of knowledge, attitudes, and memories (among other things) acquired during learning that alters the agent’s intentional performance. Consider a constituent of the schema for modus ponens, an explicit representation of the rule of modus ponens that constitutes knowledge of what the logical form of modus ponens is. This is knowing a rule of inference. One can have knowledge of modus ponens without forming the intention to reason according to the rule (the logic instructor) and one can intentionally reason according to modus ponens without intending to do so or having knowledge of the rule (the beginning student doing a problem set). Where such knowledge is involved in deduction, Carroll’s regress is avoided by linking the regulatory role of the rule to control, not guidance. The rule biases attention. Return to our logic teacher who is skilled in proofs and has explicit knowledge of formal rules of deductive inference. Part of her expertise is constituted by capacities for deduction that often are done automatically without an explicit intention to reason in the relevant ways. Still, the teacher knows the rules, and were she to intend to reason in specific ways, say as part of a lecture illustrating those rules, the concept of the deductive inference deployed in her intending to so act can also activate her knowledge. When that knowledge is active, it can provide an additional bias on her action space. This influence of her knowledge is, by definition, automatic, but it is tightly linked to her intention in that it is because she intends to reason in a specific way that the relevant knowledge is brought to bear (in the well-trained reasoner, the knowledge can also be brought to bear without the intention).

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There is a contingent connection between the agent’s intention to Φ and her knowledge regarding Φ. Having that knowledge reflects the understanding that she has acquired through years of dealing with logic, and the strength and quality of her training shapes the automatic links between her intention to Φ and what she has learned in training to Φ. The agent’s intending to Φ and her knowledge about Φ-ing can jointly bias her so acting. In this case, the intention and the knowledge constitute what we can call an enriched practical understanding of Φ. Such understanding reflects the agent’s learning. Accordingly, the practical significance of her concept of Φ is fixed by the learned aspects of her understanding of how to Φ (her schema), an understanding that is active when she intends to Φ. If we allow that a schema acquired by learning how to Φ is constituted by all sorts of “knowledge, attitudes, and memories,” all of which are contingently and differentially activated by an intention to Φ, then there is the possibility that the agent’s action will be differentially modulated by these elements. That is, depending on the context, her working memory capacity and cognitive load, her current internal states that affect her distractability and focus, different amounts of what she knows will be brought to bear. She will be influenced by different levels of action understanding, depending on time, context, and internal state. This is what I mean by the holistic practical significance of the concept Φ that tracks the agent’s learning to act in that way. How she engages in Φ-ing is determined not just by her intending to do so, but also by the influence of her learned knowledge of how to Φ. With understanding tied to practical significance via learning’s link to shaping our intentions, we can, if we wish, speak of a qualification on control in intention, a higher order form of control that draws on what is learned (knowledge): knowledgeable control. This is control in intention that brings with it knowledge as a correlated influence. When the agent comes to intend Φ directly, the concept Φ cognitively integrates with action capacities. Yet the knowledge how that is learned in coming to be able to Φ directly also enhances the agent’s understanding. The elements of what is learned, captured by talk of a schema, can be jointly deployed when the agent intends to Φ, and when they are, they partly constitute an enriched practical understanding of Φ-ing that contributes to agentive control. Speaking of knowledgeable control brings to the fore how learning informs and enriches intentional action.

6.7 Taking Stock This chapter has focused on a specific movement of mind central to philosophical practice and indeed, many other theoretical disciplines: deducing as reasoning. A general lesson is that our attempts to solve problems raised by an action kind K should not be fixated on the specific features of that kind at the expense of

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recognizing that all actions share a basic structure that can provide a basis for addressing the specific problems raised by K. Long ago, Lewis Carroll presented a paradox of reasoning with rules, and subsequent attempts at solving this problem did not draw on the core structure of action. By focusing on control and guidance, on intention and attention, we can explain what is distinctive about deducing qua action and, along the way, address Carroll’s paradox. Central to understanding deducing as action has been examining the role of learning in inculcating knowledge and skill. Learning involves the transformation of capacities for control in setting action guidance, and it is an epistemic achievement that implicates the involvement of knowledge. Getting better at acting, becoming an expert, tracks the balance of automaticity and control, and hence the alteration of what intending to so act comes to. We reason (well) because we have learned how to do so, and what we learn is a positive bias on rational movements of mind.

Notes 1. Probabilistic models of deduction: I have focused on mental model theory as it facilitates merging the structure of action with psychological models of deduction. There are, however, a variety of psychological theories of deductive inference that arguably are in ascendancy (for an attempt to test of four of them, see Oberauer 2006). Here, I want to mention probabilistic approaches. As noted by Jonathan Evans, an important development in the psychology of reasoning has been to theorize about reasoning with conditionals in terms of conditional probabilities (Evans 2012). Evans, Handley, and Over (2003) have suggested a suppositional account of modus ponens where in reasoning with if p then q, subjects compute the conditional probability P(q|p) (see also Oaksford and Chater 2001; Evans and Over 2004). Thus consider how conditional reasoning initiates: A conditional statement if p then q focuses attention on the general possibility of its antecedent p. This possibility then divides into the pq and p¬q possibilities. To the extent that pq is judged more probable than p¬q, the conditional probability of q given p is high, and a high probability is assigned to the conditional if p then q. To the extent that pq is judged less probable than p¬q, the conditional probability of q given p is low, and a low probability is assigned to the conditional if p then q. (Evans, Handley, and Over 2003, 325; my emphasis). Evans, Handley, and Over are explicit about a role for attention. In effect p provides a pointer, a cue. On the suppositional account, the response to p is to divide possibilities, [P, Q] and [P, not-Q], and respond with their probabilities. Perhaps this involves calculating said probabilities though there can be other sources that inform arriving at assignments of probabilities:

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One might have said “salient” possibility. In this case, the considered possibilities also serve as cues for beliefs (memories) about the probabilities of both, so no explicit probabilistic computation is needed, just a subsequent comparison of values. Here, attention to possibilities leads to recollection of relevant mnemonic content in belief, so additional cognitive attention that informs assessing which value is larger. One then concludes that pq is more probable, so if p then q is assigned a high conditional probability. This assignment influences subsequent inferential behavior. I note this alternative approach to acknowledge a wealth of important and often opposing work in the psychology of deduction, but also to emphasize that each theory attempts to probe the structure of deducing as a train of thought (James’ cognitive attention), a specific logical movement of mind. It should not be surprising that we should locate in those approaches the structure of action that we have discerned and a fundamental role for selective attention. One should not see this invocation of attention as a mere convenient redescription of inferential action. The notion of attention deployed is part of the substantive theory given in the previous chapters. 2. Is there reasoning without knowing the rule? Begin with an uncontroversial fact: that the teacher and the student differ when the teacher knows the rule and the student follows the rule given the teacher’s instruction. In deduction, the student grasps the relevant possibilities (models) in entertaining the relevant proposition, and, in being appropriately sensitive to cues, subsequently attends to the relevant possibility. So, if she attends to the possibility [P, Q] she can focus on Q, that is select it to establish a new cognitive state. This is drawing the conclusion Q by appropriate cognitive attention. Note that this can happen automatically, and often does. The subject can fail to draw the conclusion due to attention deficits: she is inattentive, doesn’t notice Q, is fixated on P, or gets distracted. But where she recognizes Q by attending to it given her grasp of relevant possibilities, this form of focused attending, of moving from [P, Q] to [Q], exemplifies the core of non-ampliative reasoning. One might see this as too thin to be reasoning, but we should also consider the biological facts that suggest that subjects such as young children without understanding of higher level concepts of consequence, support, follows from, and so on can reason appropriately (cf. Markos Valaris on the belief requirement; Valaris 2014, 2016). One can acknowledge the attentional core of deductive inference without denying that something does change when one learns the rule. For example, knowing the rule opens up additional possibilities for further actions, for one can now teach logic. 3. On senstivity to the psychological state: In reasoning, we must be sensitive to the nature of the type of taking, the psychological mode of the input state. Whether we believe a premise or merely entertain it matters to what response we evince (see Julia Staffel 2019 for a concise argument for being sensitive to the attitude as well as the content). Reasoning is taken by some to involve working memory as a workspace in which we manipulate premises. If we must be sensitive to the type of takings, then a picturesque description of reasoning would be that it operates over states with content where, to

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push the metaphor, these mental entities are items to manipulate within the workspace of cognition. There is, however, a different way of understanding sensitivity to the type of taking in play especially when the agent is sufficiently skilled in the action in question. Consider that visually guided movements are sensitive to visual states though an expert agent need not respond to her visual experience by recognizing that the state is a visual one. She is not, of course, bodily responding to the contents of her experience irrespective of the type of experience that it is. Nevertheless, she is sensitive to the fact that she is seeing her target, for that is why she reaches as she does, namely toward where she is looking. Similarly, we are not merely reasoning over cognitive contents irrespective of the attitudes that we take toward them, for we are sensitive to those attitudes. Yet we also need not represent the attitudes associated with the contents. Rather, we reason about a content in believing it or in supposing it. The way that we take the content, the relevant psychological mode or attitude toward that content, informs the type of response because of the specific action capacities that the perceiver has acquired, action capacities learned through experience and practice including the honing of appropriate practical memory in the give and take of asking for reasons. Generally, we acquire through learning sensitivity to an attitude. We need not de novo plumb the mental depths to uncover what type of attitude is at issue and then respond to it. Central to such action capacities is that they link specific types of takings to specific types of response, so visual experience to movement, belief to judgment, suppositions to (say) conditional judgments, or intentions to further fine-tuned intentions (among others). The relevant sensitivity is built into the learned action capacity. Being able to deduce from one’s beliefs is just being sensitive to beliefs in responding appropriately to them, for the beliefs, or some active analog, constitutes one’s cognitive attention informing the subject’s coming to a conclusion. We do so because we have learned to do so. This need not involve explicitly representing the inputs as the type of states that they are (though one could, if one wished, a power of agents who are able to conceptualize their own states, see Chapter 7 on introspection of perceptual experience).

7 Introspecting Perceptual Experience 7.1 Introduction Introspecting is like any action: there are contexts in which it is reliably successful and contexts in which it reliably is not. Intentionally introspecting experience informs many areas of philosophy and science. It involves a subject’s accessing her mind in a first-personal way. In this chapter, I focus on introspection of perceptual consciousness, but the issues generalize to any use of introspection of experience and of how things phenomenally seem. When reflecting on the mind from the armchair or as part of an experiment, we introspectively detect and discriminate aspects of our subjective life to report what they are like for us. The problem is that for many contexts in which scientists and philosophers use introspection, they have no idea whether the reports are accurate. Indeed, as far as we know, introspective judgments might be reliably inaccurate (Schwitzgebel 2011; Irvine 2012b; cf. Spener 2015; Michel 2021). For example, introspective reports might be theory-ladened or distorted by demand characteristics (Orne 1962) leading to reports that consistently fail to accurately describe subjective reality. Introspection can be accurate. Medical contexts provide a venue where positive clinical outcomes attest to introspective accuracy. A patient complains of pain in the foot. The physician takes this introspective report at face value but also probes. For example, she gently manipulates the part that is in pain while the patient detects and discriminates: I don’t feel it there, ow, ow, now I do, that hurt a lot! Clinical interventions begin with assessment of outcomes measured by reports (see Section 7.5). While reports are initially taken at face value, interventions, manipulations, and assessment of clinical outcomes validate that value. That is the right way to proceed. In most contexts where we use introspection, however, we stop at face value. We take subjects at their word, and go no further. Yet, we do not similarly accept just face value data in other areas of science. Rather, scientists collect data in carefully designed experiments where measurements are calibrated and appropriate controls are run. They replicate results and formulate hypotheses to make predictions about new data. In contrast, introspective data collection is often not subject to the same rigorous standards. A theorist introspects experience, reports some

Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0008

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phenomenology, and takes the deliverances at face value as a basis for theorizing. No further working up of the data is undertaken, no running of controls, or assessment of potential confounding conditions that might render the results unreliable. Introspection has its say, and subjective matters are thereby settled. We effectively treat introspection in practice as a supernatural capacity. Yet all natural detection and discrimination measurements are subject to context varying noise. Accurate detection and discrimination is a function of being able to separate signal from noise, and normal signal detection is subject to error. In signal detection theory, error such as a false alarm is a function of two variables: sensitivity and criterion. Yet if our practice is to assume that introspection is highly accurate, perhaps infallible, we ignore the reality of noise. That is, we treat introspection as exempt from a fundamental challenge that all natural detection capacities face. This is to treat introspection, in practice, as supernatural. I am not an introspection skeptic, for I have argued that introspection of controlled features of our intentional mental actions is reliably accurate (Section 4.6). At the same time, I think there are notable examples of introspection that are demonstrably inaccurate or where the face value of reports has been misinterpreted in science and in philosophy (Wu 2020b, 2023). As theorists, we should justify why the raw introspective reports we rely on are accurate. Given context varying noise, we should develop better methods to collect introspective data. What is needed is a theory of introspection as a psychological phenomenon, something which we lack. Understanding the phenomenon begins with understanding it as an action. In doing so, I begin the project of explaining introspection, articulating principles for assessing accuracy grounded in the psychological structure of introspective access, and identifying limits to accuracy (cf. Spener 2015). Introspection is hard to do well, yet we often proceed in practice as if it is the easiest thing in the world, for how can we “miss” in reporting on what things are like for us and how things seem, subjectively speaking. To ensure that introspection yields the facts, we must introduce more rigor in how we collect reports, guided by an understanding of introspection’s structure and limits. This chapter is a step toward a robust cognitive science of introspection.

7.2 The Need to Carefully Define Introspective Tasks As an action, introspection’s reliability is sensitive to task instructions. Eric Schwitzgebel has forcefully argued that introspecting is largely unreliable, drawing on a set of cases where it plausibly fails (see Schwitzgebel 2011; for a critical response, see Watzl and Wu 2012; for empirical concerns, see Irvine 2012a). One case concerns visual consciousness. Here is how Schwitzgebel proceeds:

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Look around a bit. Consider your visual experience as you do this. Does it seem to have a center and a periphery, differing somehow in clarity, precision of shape and color, richness of detail? Yes? It seems that way to me, too. Now consider this: How broad is that field of clarity? Thirty degrees? More? Maybe you’re looking at your desk, as I am. Does it seem that a fairly wide swath of the desk—a square foot?—presents itself to you clearly in experience at any one moment, with the shapes, colors, textures all sharply defined? Most people endorse something like this view when I ask them. They are, I think, mistaken. (Schwitzgebel 2008, 254)

What and how much one consciously sees is controversial especially the question regarding how rich visual experience is.¹ Still, there is substantial agreement among theorists of visual consciousness that the folk are mistaken in basic introspective judgments about what they see, as in Schwitzgebel’s example. The folk wrongly take clarity in the visual field to be broader in extent than it actually is. To demonstrate the mistake, theorists use introspection to reveal the correct answer. They invite us to maintain fixation in a scene and then introspect parafoveal and peripheral vision. For example, fixate on a word in the center of this page and then covertly attend to words in the periphery. Now answer the question: How broad is that clarity of detail? It will seem that peripheral vision lacks the clarity of foveal vision. So, if one was previously inclined to introspect that a large part of the field of view was equally or similarly clear, then one’s current introspection of visual experience of text reveals that judgment to be inaccurate. Of course, if we are raising questions about introspective accuracy, one side cannot simply claim that its report is the correct one. Which report is accurate, the naive response or the proposed correction? In support of the latter, the biology of the visual system explains why clarity decreases away from the fovea: spatial resolution drops off as one moves away from the fovea, the size of visual neurons’ receptive fields increases in the periphery leading to less acute discrimination, the cortex magnifies representations of central vision at the expense of processing in the periphery, and there is a psychophysically measurable drop in behavioral performance in discriminating peripheral detail. If we take visual consciousness to have a neural basis, then the nature of our experience should cohere with the biology. The loss of acuity makes sense given the corresponding differential distribution of neural representational resources. So, we have independent grounds to accept the probity of the correction. Schwitzgebel offers this example as one of a set of cases that illustrate the unreliability of “naive” introspection. Yet this case does not demonstrate that. Rather, it illustrates how difficult it is to get clear introspective reports. Let us grant that the corrective involving maintaining fixation is a case where subjects’ introspection is reliably accurate. Yet consider Schwitzgebel’s instructions: “Look

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around a bit.” That is, in inviting his subjects to generate a report that will later be assessed under conditions of fixation, his initial instructions effectively direct his subjects to do the opposite. Accordingly, it is not surprising that subjects return a different answer. If accurate introspection in this case depends on fixation, task instructions that get subjects to do the opposite promote inaccuracy. In contrast, I find that when I ask naive subjects to assess peripheral vision while keeping eyes fixed, all quickly return the verdict that acuity drops off. Indeed, a savvy subject might retort that her introspection was accurate: visual experience is spatially acute throughout the visual scene. After all, normal visual experience is temporally extended, served by multiple saccadic eye movements, one to three times a second. In extended experience, much of the visual world is spatially acute across time precisely because we foveate much of the visual scene over time. Wasn’t that the point of asking the subject to look around, to assess experience over time? Schwitzgebel considers fixations over time but only to explain how the subject is mistaken in concluding that everything is “all clear simultaneously” (2008, 255; my emphasis). Yet the savvy subject can aver that she is not getting it wrong relative to the task instruction which was this: “Look around a bit. Consider your visual experience as you do this.” When looking around a bit, why isn’t it accurate to say that one’s view is largely clear? Schwitzgebel correctly advocates for care in introspection, yet this enjoins us to more care in introspective tasks. There is too much easy going appeal to introspection in philosophical argument about the subjective. Such appeals are liable to unreliability given task instructions and loose conceptions of the task, leading to inadequate introspective returns. Indeed, when phenomenology is discussed in philosophy, typically the sample size is one, with a single theorist who reports specific phenomenology in their experience and assumes that others will report the same. Whether others would do so is an empirical question, one the philosopher does not further pursue. Consider what science would look like if we took the same attitude toward empirical experiments, with small sample sizes and no follow up experiments. If we are to properly assess the accuracy of introspective reports, let alone their theoretical significance, we need principles for assessing what are reliable and unreliable conditions for introspection, namely those conditions in which introspective data will be accurate. The Problem of Accuracy remains largely unaddressed: in what contexts is introspection accurate, and in what contexts is it not? The answer to this problem is to provide a theory of introspection as an action.

7.3 Introspecting as Mental Action Introspecting perceptual consciousness is guided by perceptual attention.

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I focus on intentional introspecting, a subject’s intentionally coming to render a judgment or report about her experience in light of how she takes things, given an intention to introspect a specific phenomenal target, say visual clarity. So, introspecting necessarily involves attention. Philosophers agree that we use introspective attention. Brie Gertler notes that by focusing your attention on the phenomenal quality of [an experience], you can come to know something about your current experience. Philosophers generally agree on this much. (Gertler 2012) William Lycan introspects introspection, reporting: When we attend to our own mental states, it feels like that is just what we are doing: focusing our internal attention on something that is there for us to discern. (Lycan 2004) Janet Levin (2006) ties attention to discrimination and the grounding of phenomenal demonstratives: If I’m having more than one experience at a time . . . then I can denote distinct neural-particulars, respectively, as “this,” “that,” and so on, as long as I can discriminate among these experiences and successively direct my introspective attention to them at that time (88). Many other philosophers endorse what we might call an inner spotlight model where we can directly attend to the phenomenal properties of experience (Chalmers 2003; Lycan 2004; Levin 2006; Papineau 2006, 2002; Carruthers 2011; Balog 2012; Gertler 2012; Horgan 2012). Yet no empirical evidence for the existence of the type of attention postulated is presented, and little is done to investigate the nature of introspective attention. This is a striking and puzzling lacuna. What follows fills this gap, drawing on the theory of attention (Chapter 2). Doesn’t introspection provide evidence regarding its character, as Lycan suggests? Whether it does is what is in question. Indeed, we have conflicting introspective reports regarding introspection. Harman observes: “Look at a tree and try to turn your attention to intrinsic features of your visual experience. I predict you will find that the only features there to turn your attention to will be features of the presented tree, including relational features of the tree ‘from here’ ” (1990, 39; see also Tye 1992; Dretske 1995). Introspection is not itself definitive about what it is directed at. How might we make progress? We have already examined one form of introspecting, namely the agent’s access to her mental action (Section 4.6). That account fits a proposal by Gareth Evans:

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[A] subject can gain knowledge of his internal informational states [his perceptual experiences] in a very simple way: by re-using precisely those skills of conceptualization that he uses to make judgements about the world. Here is how he can do it. He goes through exactly the same procedure as he would go through if he were trying to make a judgement about how it is at this place now . . . he may prefix this result with the operator “It seems to me as though. . . . ” (Evans 1982, 227–8)

Expanding the basic structure in Section 4.6 by drawing on Evans’ insight, I suggest an empirical hypothesis: the mind generates accurate introspective judgments regarding perceptual experience by drawing on the capacities deployed in generating accurate perceptual reports including perceptual attention. Consider the intentional action of making observational judgments or reports. An intention to render an accurate report will bias perceptual experience to guide the application of an appropriate observational concept. This depends on the guiding feature given in experience, say individuating features that enable appropriate categorization. Thus, one’s response is informed by perceptual attention to said feature. The coupling enables perceptual experience of some perceived instance of T to then correctly guide deploying the concept T. Accordingly, one reports that there is a T, say in visual experiencing it:

Figure 7.1 An object with feature T is visually experienced. The guiding feature T informs, through being seen, a conceptual response, the application of the visual concept T in a report: “There is a T.”

The experience of T provides guiding content for the report that there is a T. Visual experience of T constitutes visual attention to T. The same holds for reports informed by other types of perceptual experiences. To introspect that one sees T, the agent need only make a slight shift in conceptual response because she intends to introspect whether she sees T. As

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Evans suggested, to generate an introspective report that one sees T, the agent need only redeploy the action-relevant capacity used in visual reports that there is a T. This capacity is just her visually experiencing T linked to the concept of T as in the previous figure. The slight shift is in the conceptual response. Rather than reporting that there is a T (perceptual report), the agent reports that she sees T (introspective report). Note that the conceptual complex PERCEIVES T involves the perceptual concept T. This shows that the action of introspectively reporting that one perceives T draws on a concept also deployed in perceptually reporting that there is a T.

Figure 7.2 The figure depicts intentional introspection of the visual experience of T. Note that the same visual experience that informs perceptual report of T (Figure 7.1) also constitutes attention that informs introspective report of seeing T. The intention to report one’s having a visual experience biases not just appropriate observational capacities, namely the concept T, but also introspection-relevant capacities, the concept SEE.

It is worth comparing this structure with that of the perceptual report in Figure 7.1. In both cases, the same visual experience of T constitutes visual attention to guide the report. That is, both experiences are guided by perceptual attention to T. Some might find it puzzling that the same form of attention can guide both perceptual and introspective report. Yet we deploy the same form of attention for many different responses. Consider all the different bodily movements that can be made based on the same form of perceptual attention: jabs, reaches, kicks, grasps. Similarly, many different mental responses can be made guided by the same perceptual attention: imagining, encoding, reasoning about, etc. Thus, seeing the location of an object, can guide a myriad bodily and mental responses to it. The same form of visual attention can serve a myriad responses, including introspective report. Crucially, on this view, talk of introspective attention does not identify

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a distinct type of attention that differs from perceptual attention (e.g. one internally rather than externally directed). Rather, it specifies a different deployment of perceptual attention, namely for introspective response. Introspective attention is, here, perceptual attention for introspective response in report or judgment. The result is an Evansian attention model of introspective judgment that involves the redeployment of basic attentional capacities exercised in observational judgment (cf. Byrne 2018). Unlike raw appeals to a form of introspective attention we do not understand, the model presented here draws on psychological capacities that we do understand.

7.4 Reliability Conditions for Simple Introspection Simple introspection draws solely on perceptual experience as constituting introspective attention, and its reliability is a function of the reliability of the components of perceptual judgment. Consider subjects who can rapidly categorize stimuli on the basis of attending to differentiating guiding features such as radiologists who identify tumors on an xray image (Section 5.3). Their ability to attend to a differentiating feature on a lung (so the guiding feature T in the previous figures) leads to the application of the relevant concept, say LESION/TUMOR, in radiological assessment. Skillful visual attention, developed over years of training, is an essential part of the explanation of expert judgments (Reingold and Sheridan 2011, 527). We are all conceptual experts in some domain, correctly applying observational concepts on the basis of perceptually attending to relevant features: recognizing faces, colors, shapes, birds, buildings, and so forth. The reliable linking of perceptual attention to appropriate conceptual application in perceptual judgment is commonplace. I shall connect this reliability to introspective reliability in simple introspection where the perceptual experience to be introspectively assessed is the sole source of information for the judgment. This is the limiting case of reliably accurate introspection. Introspection of perceptual experience is simple when it relies only on perceptual attention, and it is complex when it is not simple. Complex introspection relies on more than perceptual attention, in particular cognitive attention to memories or beliefs. Complex introspection is likely the norm in introspective behavior in cognitive science, yet it faces a number of pitfalls (Section 7.5). Simple introspection, in contrast, is perhaps more reliably accurate, but in what contexts? Conceptual expertise in perceptual judgment draws on attention to relevant guiding features to inform correct concept application. If attention informs such conceptual skill, then if introspection is built on such skill, the introspective judgment that I perceive or perceptually experience X will be similarly informed by perceptual attention to X. This common action-relevant capacity secures the

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perspicuity of the workings of simple introspection. The facet of consciousness I shall focus on in this section is captured by use of a generic concept of experience that is expressed in ordinary uses of verbs like “see” or “hear,” say in reports that one sees X or hears X. The concept SEE-X or HEAR-X—or indeed P-X where “P” picks out an ordinary sensory experience verb—are concepts of consciously experiencing X through the relevant sensory modality.² The key idea is that where an observational concept X can be deployed in perceptual judgment guided by attending to X, then perceptual judgment is possible in this sense: we can perceptually judge that there is an X or that something is X by perceptually attending to X. Further, we can experimentally assess such perceptual judgments in psychophysical experiments. This leads to the Psychophysical Reliability Condition for Simple Introspection: In contexts where perceptual judgments of X tend to be accurate in sensory modality S, then simple introspecting that one S-experiences X will also tend to be accurate. Thus, in contexts where one is reliably accurate in visually reporting the shape of an object, one will tend to be reliably accurate in introspectively reporting seeing that shape. In both cases, the report is based on visually attending to the shape. A standard reliable context will be where subjects with ease use the introspective report “I see X” as a perceptual report. That is, to report the presence of a visible stimulus, one can just as easily say, “I see an X” as say “There is an X.” This happens all the time: “Is there a tiger out there?” “I see one, yeah.” Typically, perceivers automatically use these phrases interchangeably: “There is an X,” “I see an X.” Each of these claims, when based on visually attending to X, will be true when the other is. Correlatively, as perceptual judgment becomes more difficult, so will introspective judgment. If it is difficult to ascertain that an X is present, it is correspondingly difficult to hold that one perceives X. Both visual and introspective judgment exemplify conceptual expertise guided by visual attention, with introspective judgment “piggybacking” on the capacities for visual judgment. Indeed, note that the observational concept X is central to both perceptual and introspective judgment. Accordingly, the subject’s skill in applying X based on perceptual attention to X provides the central guiding thread for perceptual reports that there is an X and introspective reports that one perceives-X. Simple introspection is built on perceptual recognition via the coupling of the perception of X to the response involving the concept X. To assess reliability, begin with veridical visual experience since it is the basis of much empirical work on vision. To probe basic visual capacities, experimenters create conditions where they manipulate the type of visible stimuli presented and assess behavioral response to these. Experimenters control visual responsiveness by calibrating behavioral performance. Focus on cases where the subject correctly

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reports on the stimulus. The reliability of simple introspection will be explained by the reliability of correlated observational judgments which in turn are grounded in the reliability of three concrete psychological points in the structure of action: 1. Perceptual experience. 2. Perceptual attention (as constituted by (1)). 3. Application of observational concepts. Crucially, each “node” can be manipulated. For example, (1) can be modulated by modifying stimulus properties, say increasing or decreasing its luminance or duration of presentation times; (2) can be modulated by adding or removing distracting stimuli or changing task instructions; and (3) can be modulated by increasing or decreasing cognitive load or requiring deployment of complicated or new concepts. On the current model, introspecting experience will be subject to the same pressures save a shift in (3), from applying the perceptual concept X to applying the complex, PERCEIVE-X. We then apply the theory of action to explain when and why such introspection is reliably accurate or inaccurate. I cannot provide principles for all relevant perceptual contexts. That is work for a comprehensive theory of introspection. Still, I will illustrate how the structure of action provides a framework for illuminating such cases. Consider a paradigm case (removing temporal indices to simplify exposition): Accuracy Principle for Simple Introspection in Veridical Seeing If in a given perceptual context Rvp, (1) the subject can see X; (2) the subject can visually attend to X, namely select it to aid task performance; and (3) the subject has a recognitional concept for X such that the subject can apply the concept X, given (1) and (2), with reliably accuracy and the subject has the concept see-X (or visually-experiencing-X) (4) there are no other interfering or enhancing psychological factors likely to disrupt psychophysical performance in visually judging that there is an X; then Rvp is a reliable context yielding accurate introspection about seeing X (see Peters and Lau 2015 for a test of the tight connection between seeing and introspecting). Assessing whether subjects satisfy conditions of the sort expressed in the antecedent is a routine part of assessing the adequacy of subjects in any psychophysical experiment. Intuitively, introspecting in this context is accurate because perception does its job of providing clear access to the world. The latter claim can be verified experimentally. Evidence for the antecedent is then provided by showing that perceptual judgment regarding X is demonstrably accurate in Rvp through

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measuring performance in perceptual report. In testing for perceptual reliability, we provide evidence for introspective reliability. It is not surprising that in such contexts, the introspective report “I see X” is effortlessly used as a perceptual report. Where the perceptual report is correct, one in fact sees X. Hence, the introspective report, “I see X” will also be accurate. There are other contexts R where performance will generate chance or inaccurate reports. Such variability in performance is often found in perceptual performance under different experimental conditions and across different experimental subjects. To cope with perceptual variability, the science of perception has identified and developed experimental methods and paradigms that yield reliably accurate data, extracting signal from noise. These approaches have been codified over time in experimental practice. Unfortunately, there are no corresponding methods for introspection. Introspective accuracy will track accuracy in perceptual judgment, and that is a function of each component: perception, attention, and conceptual capacity. One case of undercutting introspection is worth noting: it is sufficient to reduce simple introspection to mere guessing by abolishing appropriate perceptual attention. Appropriate attention entails coupling experience to judgment, and abolishing it means that any judgment about the resulting unattended experience will be unguided, no better than a guess. Consider by analogy if a friend holds an unknown object behind your head and you are asked to report its color. Since you are unable to perceptually attend to it, observational judgment is reduced to guessing. So long as you do not draw on any other information regarding it, this is a condition for chance performance in color judgment. Reduction to guessing is instantiated in the widely debated issue of attention as a gate on consciousness and shows how the debate has been unproductive because introspection was assumed to provide relevant data. Yet the very possibility of reliable simple introspection is eliminated by experimental design. Consider the widely held thesis of inattentional blindness: when one is not visually attending to Y, one is not visually conscious of Y (Mack and Rock 1998) (for philosophical discussion, see among others, Montemayor and Haladjian 2015; Jennings 2020). Against this, some claim that experience overflows attention. We can be visually conscious of Y without visual attention to it (Block 2008). To overflow theorists, subjects’ introspective reports reveal phenomenology outside of attention. Such data provides initial support for overflow: it visually seems to me as if Y even though my attention is elsewhere. Block makes this claim in his discussion of Sperling’s total report paradigm (2007, 487), a position he also attributes to Bernard Baars (1988, 15; see also Burge 2007, 501). By definition, overflow of a feature Y means that perceptual attention is not directed to Y. This sets a test condition for experimental support for overflow: to demonstrate that Y overflows attention, we must not be attending to Y while also showing that Y is experienced. Assume that the test condition holds: the subject is

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not visually attending to Y. Yet recall that the tradition assumes that introspecting deploys introspective attention, whatever that comes to. If visual attention to Y is eliminated, then so is the possibility of simple introspection in respect of seeing Y. If we are to introspect seeing Y, this requires that we attend to Y but doing so violates the test condition. So, if the overflow theorist’s claim that they see Y is accurate, then they are attending to Y, contrary to the test condition. Thus, introspection, if accurate, cannot be used to support overflow. At the same time, when theorists are not attending to Y, their claims that they see Y are guesses. They lack the attentional/informational connection needed for accurate introspection. This argument draws on the following claim: Unreliability for Simple Introspection in Perception If in a given perceptual context Up, a subject is not perceptually attending to X then in Up, judgments about X or seeing X are guesses (barring reliance on other resources). Matters are, however, no better for overflow’s opponents. They endorse gatekeeping: if one is conscious of Y, say sees Y, then one attends to Y. Gatekeepers castigate overflow theorists for endorsing an empirically untestable position. Overflow theorists are said to fall prey to an analog of the refrigerator light illusion where a subject takes the refrigerator light to be on even when the door is closed. This charge sticks only because we know that the light goes off when the refrigerator door is closed since we know how refrigerators work. Accordingly, the charge begs the question in the case of consciousness because what is in question is whether attention is necessary for consciousness. Unlike refrigerators and their lights, we don’t know what the relation is between attention and the “light of consciousness.” Ironically, the gatekeeping theorist also faces the charge that their theory is no less empirically problematic if the overflow position is. The gatekeeping proponent must show that phenomenology is absent when attention is removed. Yet the very absence of attention makes discernment of phenomenology’s absence also a matter of guessing. After all, if you can’t visually attend to the object behind your head, you can’t tell if it is not blue. So, in claiming that there is no phenomenology without attention, gatekeeping theorists either beg the question or deploy equally unreliable introspection as the overflow theorist. Overflow and gatekeeping theorists are in the same empirical boat. If one position is untestable, so is the other, and for the same reason. Introspection requires attention, but the debate requires that the presence or absence of consciousness be tested when attention is removed, precisely a condition where introspection is reduced to guessing. There is much more to say about simple introspection. My goal has been to introduce the structure of simple introspecting as mental action and to link

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questions about reliability and accuracy to that structure. The structure of simple introspecting as a mental action provides a template for a principled assessment of a central source of evidence and data for the theory of consciousness. I have applied these ideas to show a general problem in the debate about whether attention gates consciousness. Without a realistic psychological structure, we hamper our empirical understanding of introspection as a psychological process and our assessment of introspective data relevant to theories of consciousness.

7.5 Complex Introspection and Blur Complex introspection, typically used in philosophy, can be reliable, but it is challenged by multiple sources of noise. Simple introspection is restrictive and not often used in philosophical theorizing about experience since the phenomenology it reveals is mundane. The phenomenology philosophers discuss is more complicated than revealed in simple introspection. Accordingly, philosophers must draw on complex introspection in that they tap into additional resources beyond perceptual experience to inform introspective judgment. Complex introspection often relies on two sources of information, say experience and memory. Where memory informs judgment, then we have cognitive selection for task, one’s mnemonically taking things contributes to the guidance of conceptual response. In complex introspection, perceptual and cognitive attention jointly inform introspective judgment. This expands the resources for introspection. Still, while additional resources can help, they can also hurt, say, due to distorted memory, theory-ladenness, and expectation. This raises substantial problems for the usefulness of introspective judgments as the remaining sections illustrate.³ The concepts of consciousness that we have considered thus far are built on observational concepts invoked in perceptual judgment. Still, most of the concepts philosophers deploy in complex introspection are not similarly perspicuous. Consider Uriah Kriegel’s striking description of self-consciousness in perceptual experience: The reason we recognize [self-consciousness in experience] is that we experience it. When I offered the phenomenological characterization of the non-reflective mode of self-awareness humming in the background of our mind, you knew exactly what I was talking about. We did not posit this self-awareness on purely theoretical grounds. Rather, we pointed to it as a familiar element in the human mental life, an element every conscious person is permanently experiencing. (Kriegel 2003, 122)

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Other complicated concepts of consciousness include transparency (Harman 1990; Tye 1992); presentation (Sturgeon 2000); sense of agency in movement (Marcel 2003; Horgan, Tienson, and Graham 2003; de Vignemont and Fourneret 2004; Bayne 2011); mineness in somatosensory experience (Ehrsson 2009); phenomenal unity (Bayne and Chalmers 2003); boundedness of the perceptual field (Martin 1992; Soteriou 2013); foreground and background (Watzl 2011) or priority (Watzl 2017) structure in perceptual attention; attentional highlighting (Campbell 2002); among others. Are the judgments purporting to identify these phenomenal features reliable? Scientists are at pains to rule out improperly biased data. To rule out inappropriate theory-ladenness in philosophical introspection, we need rigorous methods and controls. The standards we deploy in dealing with introspective reports should be no less rigorous than those deployed by scientists. Yet there are substantial challenges. Let me illustrate these hurdles and possible solutions in a case where introspection has been assumed to be accurate, the detection and discrimination of phenomenal blur in visual experience. I argue that there is one context where complex introspection of blur is reliably accurate, namely foveal blur, and another where it is likely not, at least as introspection is currently practiced, namely peripheral blur. Since phenomenal blur is not out there in the world, we introspect it not by seeing it (cf. blur as a photographic property which can be seen). If phenomenal blur (henceforth, just “blur”) is introspectable, it is not detected by simple introspection. Rather, it depends on complex introspection. Blur has been one data point in debates about representationalism and relationalism regarding perceptual experience. Philosophers writing on peripheral vision have asserted, on introspective grounds, that peripheral vision is not blurry (Pace 2007; Smith 2008; Allen 2013; French 2014). The introspective reports are puzzling, for peripheral vision seems blurry to me. In informal polling, I find introspective judgments to be divided: a number of people find peripheral vision to be blurry while others do not (a few are unsure). For example, in a recent lecture to a psychology department, I found 50% of the audience (20 out of 40) to agree that peripheral vision is blurry. Two of my optometrists also take peripheral vision to be blurry. Introspection of peripheral blur is clearly not univocal across subjects, and that should give us pause. Indeed, this variability is among the data to be explained. After all, given a common biological basis explaining the loss of peripheral visual acuity, we might expect that sameness or similarity of biology will ground sameness of peripheral visual phenomenology. A key step in validating the reliability of complex introspection here is to explicate the relevant phenomenal concept. This is a complex matter. I propose an empirical hypothesis about applying the concept BLUR. Consider being at the optometrist’s office where one is presented sequentially with two lenses before the eye while looking at a Snellen chart (rows of letters), and one reflects on the

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clarity of experience: “Which lens provides for clearer (less blurry) vision?” asks the doctor. Detecting blur at the optometrist’s office draws on two forms of attention, perceptual attention to (say) the letter “E” on the chart given one lens power, and cognitive attention to, namely memory of, past perceptual experience of the same “E” given another lens power. That is, we compare the current letter experienced with one lens against the maintained memory of the letter experienced with another lens. When I, who am horribly myopic, make judgments about blur (or, correlatively, clarity), my strategy, made salient in the difficult cases, is to attend to part of the letter, say the spacing between the horizontal lines in the “E” during the lens presentations. The comparison focuses on a perceived difference, namely my visually accessing a space between two of the horizontal lines in the “E.” In one case, I judge that I cannot see the space but in the current case, with a stronger lens, I judge that I can and thus, in comparison, recognize that one lens provides for clearer (less blurry) vision. Blur judgments are relational: A is blurrier than B. Given my reliance on working memory and present perception, my judgment of BLUR is a case of complex introspection. It relies on cognitive attention in addition to perceptual attention. We can now use BLUR application to provide a hypothesis about BLUR acquisition. A child whose vision is blurred often is not aware of this. Rather, parents or educators first notice the child’s squinting or sudden difficulty in reading. As in optometry, demonstrating blur to the child will draw on getting them to recognize an acuity difference by contrast. A colleague reports that he did not realize that his vision was blurry until his batting average in baseball in high school dropped substantially. Ultimately, he was sent to the optometrist whereupon he was surprised to discover, by the contrastive method noted above, that he had become very myopic. Until then, he was unaware of his myopia. The contrastive method can demonstrate that even foveal vision is blurry in that lenses can improve vision in those with good sight (e.g. changing vision from 20/20 to 25/20). Acquisition of BLUR involves detecting a contrast through appropriate perceptual and cognitive attention. We later apply BLUR by noting similar contrasts, and with expertise in such comparison, application of BLUR becomes automatic. These claims are empirical hypotheses. That said, the success of optometry provides evidence of complex introspective accuracy regarding blur. The clinical paradigm involving switching two lenses while a patient observes symbols on Snellen or Landolt charts is a working memory task comparing visual experiences at two adjacent points in time. Standard optometric conditions constitute a reliable context R for introspection of blur. Introspective accuracy in this case, however, is tied to foveal vision which provides the standard for clear, non-blurry, vision. That is what is principally tested for in an eye exam. Crucially, it does not follow from reliability regarding foveal blur in carefully titrated optometric

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conditions that one will be accurate regarding peripheral blur in an armchair. These are two different contexts. My method for detecting blur at the fovea by focusing on the distance between two lines on the letter “E” will not work for peripheral vision, at least under normal presentation conditions (for discussion, see Rosenholtz 2016). To gather more informative data about blur, we must take an experimentalist’s approach to introspection, imposing controls on how we collect introspective reports across a large number of subjects. To guide us, we use the theory of complex introspection which emphasizes that introspective accuracy depends on introspection’s structure as action, hence on the function of perception, perceptual and cognitive attention, and the relevant concepts. We pinpoint sources of potential variability (noise) that might shift whether one judges that vision is blurry or not. Here are some factors that can affect introspective judgment of blur in peripheral vision: Factors affecting response tied to perception and perceptual attention (A) Variation in the part of the peripheral visual field one attends to during introspection since visual acuity varies with distance from fixation (fovea); (B) Variation in the type of stimuli in the periphery experienced; (C) Variation in the ability of subjects to covertly attend reliably to the periphery (some find this difficult); (D) Visual crowding effects where tightly packed stimuli disrupt feature integration and attention. This is a different phenomenon than changes in acuity and must be avoided when probing acuity (Whitney and Levi 2011). Factors tied to conceptual capacity and application conditions (E) Variation in learning history in acquiring BLUR (what exemplars were focused on); (F) Differences in consistency in correctly applying BLUR across different visual contexts; (G) Level of myopia at the fovea, since that provides a standard for non-blurry vision and affects acquisition and application conditions of BLUR; Factors tied to cognitive attention (H) Different abilities in making comparisons between memory and perception as postulated (working memory capacity effects, tied to attention; see Chapter 3); (I) Different memory representations of relevant exemplars for blurry or clear vision. (J) Different antecedent theoretical beliefs about such cases (theory-ladenness); (K) Cognitive load during test and working memory capacity.

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To take one example, if your judgment about blur is based on attending to books tightly packed on a shelf at the periphery of your visual field, visual crowding, which involves a failure of feature integration, yields a confounding factor (see Freeman and Simoncelli 2011; Whitney and Levi 2011). One might say (introspectively!) that crowding is jumbled, not blurry (see examples of metamers in Freeman and Simoncelli 2011; also Rosenholtz 2016, 2020). In that case, the “introspective experiment” would not be well controlled. Since natural scenes, being often visually cluttered, are likely to induce crowding, say what is visible from the armchair, one possibility is that the judgments philosophers have provided that peripheral vision is not blurry are confounded by crowding. Adequate data collection requires that any assessment of peripheral blur is done in an uncrowded display. There are many sources of variability which can influence introspective judgment of blur and would explain why the introspective data is noisy. Until we control for such variability, data collection from introspection will fall short of rigorous standards. How can we do better in respect of blur? Here is an initial proposal. First, collect introspective data under uniform conditions with subjects who are performing the same task. We must work with subjects with similar acuity under consistent viewing conditions. The judgments of someone severely myopic might be different from those of someone with 20/20 vision. Subjects should also have similar capacities for covert visual attention in the periphery. The same type of non-crowded stimuli in the periphery should be placed at the same general location in the visual field in each test and with appropriate scaling (Anstis, 1974). Simple introspection on objects in the periphery (or simple perceptual judgments) can probe peripheral visual acuity. In respect of the concept BLUR, we must demonstrate uniform conceptual behavior at the fovea in conditions of induced foveal blur and in “clear” vision. We should clarify the application conditions of the concept and match conceptual performance across foveal conditions (i.e. judgment). The relevant conceptual response in judgment can then be “walked” from the fovea toward the periphery. To reduce “cognitive noise” we need to probe the cognitive contributions to complex introspection: the application conditions for BLUR, the ability to use memory to inform blur judgments, and pretheoretical beliefs about related aspects of vision and consciousness. That is a lot of work, but good data collection always is. No wonder philosophers are not inclined to do this. We are not trained for it. Yet in the absence of doing controlled data collection, we are not in a position to treat a handful of introspective judgments generated in uncontrolled reflection from different armchairs to be accurate. This is not to say that those philosophers I have cited are wrong. The problem is that we do not have an adequate basis to decide this. Nor do they. With more stringent controls, we can obtain consistent judgments if they can be found (signal versus noise). A uniform answer extracted by this more

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rigorous approach might confirm or undercut the different philosophical claims about peripheral vision and blur. I do not claim that such an approach is easy to implement nor do I know whether the attempt to reduce variability in response will be successful. This is not to be decided from the armchair. The same issues arise for the complicated phenomenal features noted earlier. Can we reliably introspect self-consciousness buzzing in the background, the transparency of experience, the highlighting and foregrounding associated with attention, and so forth in a way that does not simply reflect our theoretical commitments toward consciousness? I suspect that conscious subjects will differ in their introspective judgments regarding these features (indeed, see Martin 2002 for discussion of historical disagreements regarding transparency). Variability suggests the presence of noise and imprecision of signal, raising the possibility that the data that we rely on to theorize about consciousness does not give an accurate readout of subjectivity. The only plausible option is to scrutinize how we collect data and devise better ways to do so. This is possible only if we have models of introspection. Until then, I suggest that agnosticism about introspective reliability, at least in complex introspection, should be our practical attitude. Recognition of complex introspection contrasts with an assumption that introspection is direct: Subjects have unvarnished attentional access to the phenomenal features of their experience. I am not sure there is good evidence for this form of attention, and we should not assume that there is such a capacity in the absence of confirmatory evidence. In contrast, I have provided a concrete model that draws on recognized forms of perceptual attention in simple introspection and of perceptual and cognitive attention in complex introspection. My materials are biologically real processes that illuminate what we do when we introspect perceptual experience.

7.6 Introspection and Bad Cases It is not clear that introspection can adjudicate metaphysical debates about perceptual consciousness. Can perceptual and introspective accuracy come apart? Can introspection sometimes do better? This is perhaps an odd question to ask since a presumption has been that introspection always does better. After all, even if introspection is not infallible, it comes close. How things seem is how they (phenomenally) are. Yet once we treat introspection seriously as a psychological capacity, this is far from clear. Consider a veridical visual experience and a “qualitatively matched” visual hallucination, the good and bad cases respectively. Attempts to distinguish which of the two cases one is in by simple introspection will be at chance. This

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is what drives the epistemic conundrum of knowledge about the external world. Still, are we not on solid footing if we just say, “It (phenomenally) seems to me as if X (e.g. it seems as if there is a dagger)”? The solidity of the footing can be justified from the model of simple introspection. The model can be used to argue that introspective judgments about how things seem are accurate when perceptual judgments are not. After all, in hallucination, nothing in the world corresponds to what we experience, yet we are, as is introspectively apparent, still having that very experience. Introspective accuracy can be disconnected from perceptual accuracy. Common factor approaches to perceptual consciousness treat the good and bad cases as involving the same visual phenomenology. Consider the representationalist construal of the common factor, namely visual experiences with the same representational content across the two cases such as their representing something to be red. In one case, the subject actually sees something red, but in the other, they merely hallucinate something red. On the representationalist elaboration of my model, attention in visual judgment enables visual content to inform a judgment that something is red. In simple introspection, visual content also informs introspective judgment that, speaking cautiously, one is seemingly seeing red. It is awkward to speak of attention in hallucination since “attention” is a success term, but set usage aside. Attention gets a foothold because a specific experiential content of a visual input guides judgment in solving the Selection Problem. This visual input is the common factor across good and bad cases. Accordingly, when introspecting veridical and hallucinatory experience, visual representational content guides the application of the same phenomenological concept in both cases via the capacity to apply the requisite observational concept, RED: it visually seems to me red. Thus, perceptual and introspective reliability can come apart in that perception is not reliably accurate in hallucination but introspection is. When the subject renders a perceptual report in hallucination, the reliability of the attentional link between experiential content of red and the corresponding judgment that there is something red is offset by the unreliability of perception which is, by hypothesis, disconnected from the world. Resulting perceptual judgments based on hallucinations are reliably wrong. In introspective judgment, however, the unreliability of perception need not offset the reliability of attention. The content of the hallucination links to introspective judgment, and arguably, that judgment about visual experience will be correct, so long as one uses more cautious concepts: “It visually seems to me as if X” rather than “I see X.” Yet more needs to be said. The issue of introspective reliability is not independent of the metaphysics of perceptual experience. Some relationalists take visual phenomenology as explained by appeal to the visible properties instantiated by the objects that the subject is aware of when the subject sees them. Hallucination, which lacks awareness of instantiated visible properties, accordingly lacks phenomenal character. So, if a subject claims via introspection that “qualitatively matched” hallucinations have phenomenal character, they are

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wrong on some relationalist views (Fish 2009). In this case, introspective reliability does not come apart from perceptual reliability. Representationalists find this specific relationalist account of hallucination absurd (it is not obligatory for relationalists to deny phenomenology in hallucination). Their response is partly driven by confidence that introspection clearly (infallibly) reveals phenomenology in hallucination. How could one possibly be wrong about how things (phenomenally) seem while hallucinating? Yet this standard response about introspection begs the current question about introspective reliability in cases of perceptual unreliability. What is at issue is not that introspecting subjects will say and firmly believe that hallucination has phenomenology. The relationalist can agree to that. Rather, the question is whether these judgments are accurate. That is not obviously so. Relationalists working with the attention theory predict the same introspective judgments that representationalists predict: when a subject hallucinates red, the subject will say on the basis of introspection, “I see red” or more cautiously, “I am visually experiencing red.” The “qualitatively matched” scenarios, the good and bad cases, must be specified in a non-question begging way. Accordingly, the matching can only be specified at the level of introspective response in light of the exercise of an action-relevant capacity, one typically exercised in normal perception of red to inform judgment, say a capacity to attend, to respond to a current visual input in action space. In speaking of this basic action-relevant capacity as simply an attentional capacity, one remains correctly neutral to a metaphysics of consciousness. Thus, even if the relationalist concedes the presence of a common factor, the action capacity normally deployed in making a perceptual judgment, the requirement for non-question begging conceptualization of this factor correctly leaves the metaphysical question about sensory phenomenology open. In the causally matching cases we have been considering, matching is to be secured not by deploying a robust conception of phenomenological sameness— this would beg the question—but by appeal to a common action-relevant capacity and its underlying brain basis. This shared basis predicts the same behavioral effects across the good and bad cases, including introspective judgments and expressions of certainty about phenomenology. This means that questions about the reliability of introspective judgments regarding hallucination, and whether hallucination has phenomenal character, cannot be settled by introspection alone.⁴ Bare appeals to introspection lead to one side begging questions against the other. Surprisingly, introspection cannot be a simple way to turn the dial in this oft-discussed case because how we think about introspection in the bad case depends on our metaphysics of perception. This leads to a conundrum of how to make progress on resolving the metaphysics of consciousness. There are, of course, a variety of factors that push theorists in one or another direction, and other factors might be treated as decisive. I am inclined to relationalist approaches, but it faces strong challenges. Then again, so do all theories of

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conscious experience. What is important to take stock of is precisely how much introspection can help. We have too long ignored the crucial fact that introspection is often limited but more importantly, we have failed to thoroughly investigate exactly what those limits are. As a result, our practical stance to introspection has effectively taken it to be supernatural. Detailed investigation is needed to ascertain how precisely it can help. Until we execute this neglected task, the introspective foundations of the theory of consciousness are undesirably shaky.

7.7 Taking Stock Introspection has played a central role in the history of philosophical and psychological research. It is honored and despised. There is no doubt, however, that in respect of subjective experience, it provides an important means of generating relevant data to test hypotheses about consciousness. If it is to play this central role, then we must ascertain whether its use in a given research context is reliably accurate. Much recent work uses introspection on the assumption that it is reliably accurate, but I have argued that, in core cases, this assumption is not warranted. Introspection is not supernatural, skimming far above the waters of noise. It would be better if we took introspection as an act of measuring consciousness. Ascertaining when introspection is successful would require taking seriously the challenge of noise, a challenge that faces the deployment of any detection and discrimination capacity. We have good models for how to adequately assess perceptual detection and discrimination from cognitive science, and that rigorous approach should be duplicated when considering introspection. This chapter begins such a project deploying a detailed theory of action to delineate the structure of introspecting perceptual experience, especially the central role of attention. The adequacy of large swathes of data regarding perceptual experience depends on our doing better in collecting and assessing introspective data. I believe that we can, but we are only at the beginnings of executing such a project. The crucial first step is to investigate the nature of introspecting the mind’s movements as itself a movement of mind.

Notes 1. Recent empirical discussions of peripheral vision: There is a lot of discussion on these issues (e.g. Cohen and Dennett 2011; Cohen, Dennett, and Kanwisher 2016). A powerful demonstration of the apparent limits of peripheral vision and color perception is given by Michael Cohen and co-workers (Cohen, Botch, and Robertson 2020). They deftly used virtual reality and real-time eye tracking to desaturate color in large portions of the visual field around the area of fixation during normal visual exploration in virtual reality.

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Strikingly in the most extreme case where only a disk with a radius of 10 visual degrees centered on fixation was chromatically colored (thus corresponding to only 5% of the visual field as chromatically colored), 30% of subjects failed to notice desaturation in the rest of the field despite moving their eyes to look at the scene. A very helpful resource regarding peripheral vision is Ruth Rosenholtz’s discussions (2016, 2020). 2. On the need for a developmental psychology of acquiring concepts of consciousness: We need more work on how we learn to think about consciousness, a developmental account. The challenge is to do so without running afoul of circularity. In striking work, John Flavell and colleagues (Flavell, Flavell, and Green 1983; Flavell, Green, and Flavell 1986) have probed the acquisition of the appearance/reality distinction in children (e.g. seems versus is), but a prerequisite of these studies is children acquiring the concept looks, the very type of concept whose acquisition we want to understand. See Section 7.5 for my hypothesis about how the concept of blur is acquired. 3. Complex introspection and overflow—some challenges: Simple introspection is powerless to support overflow since what is at issue is whether one sees X, where X is outside of attention. If overflow is to derive introspective support, it must be through complex introspection since there is no relevant perceptual channel to inform introspective judgment given the test condition. Consider then experiments like George Sperling’s (1960) total report paradigm where introspection seems to identify phenomenology outside of attention. Subjects in that paradigm can only attend to a subset of 12 letters that are briefly flashed but introspectively report seeing more than the three to five letters they verbally report. Is this report accurate? To obtain sufficient data, the total report tasks are repeated across many trials (see methods; Sperling 1960), and subjects learn about the structure of the stimulus array. They form true beliefs that the arrays contain letters. When one sees a Sperling array, one attends to a subset of the letters but also to the gist (Oliva 2005). Aude Oliva speaks of perceptual gist which can be understood as an abstract global-statistical property of the array that lacks fine details: “Common to these representations is their holistic nature: the structure of the scene is inferred, with no need to represent the shape or meaning of the objects” (252). This property can be extracted within 100 ms of stimulus onset. The gist of Sperling’s array is of a structure with elements whose shape or meanings are not represented. Yet awareness of gist does not support overflow since one can report it, so it is attended to. As complex introspecting is not restricted to perceptual attention, we can look to the agent’s other attitudes as additional sources supporting introspection. For example, reliable complex perceptual judgment will draw on accurate beliefs about the array to supplement limitations in what is perceptually accessible. Thus, a complex perceptual judgment that the array contains more letters than reported is reliably accurate because one has learned about the nature of the array from the experimenter and applies this justified true belief to inform judgement. This is to conjoin experiential content with belief to support the expansion of knowledge. In this case, information about the experimental setup allows us to extend perceptual judgment to the contents of the array outside of what one perceptually attends to in each trial.

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Can the belief that the array is composed of letters support complex introspective judgment? There are two salient beliefs, but neither will support overflow. The first is the most plausible and likely explains most naive claims of seeing all the letters in the array, namely that subjects have come to learn through task instructions that the array consists of letters. This belief is correct, but it does not support the claim that one sees those letters outside of attention. It does not follow from the fact that the entire array is composed of letters that one sees all the letters. That is what is in question. A second belief is available, namely the belief in overflow itself, that experience captures more than can be attended to. This belief, of course, begs the question at issue, and a complex introspective judgment drawing on it is inappropriately theory-ladened (think of two opposing fans disputing a penalty call in football (soccer), dissecting every slow motion still). As it stands, neither simple nor complex introspection provides any support for overflow. 4. Other problem cases in the metaphysics of consciousness: The text describes the case of a perfectly matching hallucination, but I have already mentioned the common hallucination of feeling a cell phone buzzing in one’s pocket when no cell phone is there. The latter hallucination can occur in the midst of veridical perception in touch, and many experienced hallucinations are “mixed,” embedded in veridical experience. For introspection of these mixed cases, the same action structure will apply as in the perfectly matched hallucination: a perceptual capacity is triggered that drives a response, a form of attentional capture which can also lead to introspective judgment. Thus, when I hallucinate my cell phone buzzing, a tactile capacity to sense such stimulation is aberrantly triggered even as other tactile capacities are appropriately triggered. A similar story can be told for cases of illusions regarding perceptible properties where we are not hallucinating objects but simply getting the properties of a perceived object wrong. Of interest will be how the aberrant activation of certain input capacities interact with the normal activity of other input capacities that lead to more complex introspective judgments. My point is that the processes leading to those judgments should be investigated (see my discussion of the rubber hand illusion and the phenomenology of ownership; Wu 2023). I don’t take any of these points to be the last word on introspection, far from it. Rather, the advantage of placing introspection in the theory of action and of attention is that we avail ourselves of concrete psychological materials to draw on in providing a theory of introspection (I am grateful to a referee for prompting these comments).

Epilogue Understanding action begins with understanding its structure. That structure emerges from solving the Selection Problem, and is constituted by an input guiding an output response. The solution requires bias, a canonical form being the agent’s intention to act. Where the agent acts with an intention, the agent’s intentionally acting is constituted by the agent’s responding, guided by how she is taking things, given her intending to act. In explicating action’s psychological structure, I have focused on mental action: attending, remembering, reasoning, and introspecting, the latter three also ways of attending. Part III of the book emphasizes how our understanding of specific forms of agency is advanced by drawing on a detailed biological theory of action. In particular, I have argued that this theory must recognize the necessary role of attention in guidance and explicate the nature of control in intention which simultaneously delimits the scope of automaticity. The structure, identified in Chapter 1, is fleshed out in the theory of attention in Chapter 2 and the theory of intention as practical memory and as remembering in Chapters 3 and 4 respectively. With those in hand, we gather new insights regarding biased behavior of ethical and epistemic concern (Chapter 5), reasoning in deduction (Chapter 6), and introspection of perceptual experience (Chapter 7). The structure can be extended to any action with, I believe, similar illumination. It applies not just to “things we do in the head,” but to action tout court. That said, the application of the structure to bodily action will unearth complications due to motor control and the body’s engagement with and alteration of the world. This and other issues raise an additional set of questions, so let me end by posing some things I hope to address and hope others will address as well, drawing on the theory presented in the previous pages: • Can we systematize different sources of automatic biases in a theoretically fruitful way? How? • How might we explain other higher order forms of control on the basis of basic control in intention, say in regulating multiple intentions? How does the concept of being active in intention enrich such investigation? Does it apply to higher order attitudes other than intention? • Can we further understand the transition between prior intentions to intentions-in-action through the relation between prospective memory and working memory? How will this work? Movements of the Mind: A Theory of Attention, Intention and Action. Wayne Wu, Oxford University Press. © Wayne Wu 2023. DOI: 10.1093/oso/9780192866899.003.0009

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• How might the debate about cognitivism and non-cognitivism regarding intention be transformed by investigating the necessarily mnemonic feature of intention? • If research on working memory is, often, research on intention, what implications are there for the content of intention? Is it purely propositional? Might it be non-propositional? Non-conceptual? • How might practical amnesia and its relation to agency aid our understanding of memory disorders and the subject’s relation to herself as agent? • If our distinctive access to mental action is dependent on intention, can this account be extended to the bodily and worldly aspects of bodily action? Can perception be drawn on without undercutting the idea that our access to action is importantly non-perceptual? • Is access to action in intention sufficient for knowledge? Does intention itself, as memory, constitute knowledge? If so, what kind? • How shall we extend the account of negative automatic bias in visual attention to bias in memory and cognition and to bodily action? • Can there be a substantive theory of normative practice that focuses on transformation of bias? What would that theory look like? • What new experimental paradigms might we construct to test the influences on biased attention to help us understand the nature of such bias? • Can we leverage understanding of the biology of bias to identify better ways to mitigate negative biases? • What role does attention play in other forms of reasoning, say inductive or probabilistic reasoning as well as reasoning non-propositionally, say with sensory imagery? • How does practical knowledge in learning illuminate other conceptions of know-how? • If the psychology of introspection is as noted, how might this change the theory of consciousness, say the identification of conscious phenomena as targets of explanation? How might we validate complex introspection? • For all the complicated phenomenology that scientists and philosophers purport to uncover, can we rigorously assess introspective accuracy for each case and so validate the data? If not, how would this change the theory of consciousness? • What is the relation between introspection and metacognition? The list is not, of course, exhaustive. What I hope to have shown, and inspired, is recognition that in tackling these questions, philosophical and empirical psychology can mutually inform and productively collaborate in constructing a biology of agency that involves, essentially, philosophical reflection. Ok, shall we begin?

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Index For the benefit of digital users, indexed terms that span two pages (e.g., 52–53) may, on occasion, appear on only one of those pages. Action Space 7, 20, 23–7, 35, 45–7, 61–2, 79–80, 84–5, 95, 97, 122, 129, 163–4, 166, 176–7, 180–1, 187, 189, 194, 203 Buridan 22, 33, 44 causal 24–5, 46–7 doxastic 24–5, 163–4 normative 24–5, 122 Action control in 27–33 emotion driven 33–4 intentional 26–8, 35–6, 49–50, 52, 66, 69, 82–5, 105, 128, 132–3, 181, 213 knowing how see knowledge-how learning 24, 30, 46–8, 60n7, 78, 122, 160–1, 164–5, 182, 191–5, 198–204 nonobservational access to 144–7 passive 30, 32, 34–6, 49, 75–6, 79–80, 178 skilled 28, 30, 48, 50–1, 157–9, 169–73, 175, 178–9, 181–3, 198–201 slips 118–19, 125 Action capacities 35–6, 46–8, 77, 85, 119, 122, 131, 178, 192, 198–9, 201–2, 204 Action-relevant capacities 35, 52, 77, 79, 85, 107, 192 Alief 58n4 Allport, Alan 69–70 Anderson, Brian 189 Anscombe, Elizabeth 29–31, 128 Antisaccade task 118–19 Antony, Louise 192 Arousal 111, 114 Attention and divisive normalization 43–5 as cause in cognitive science 72–5 automatic 80–1 biased competition account of 41–3, 73–5, 78–9 bottom-up 35–6, 117 character 180–1 covert 76–7, 79, 81–2, 86, 96, 187 control in 49–51 guidance as see Guidance as attention introspective 212 Jamesian Condition on 68

norms 179–81 overt 61–2, 66–7, 77–8, 81, 169–71 premotor theory of attention 76 preparatory 82, 111 spatial cuing paradigm 108, 132 sustained 115–22 value and reward 160–2 Automaticity defined 30 gradualism 29, 32–3, 48 necessary in action 31–2 paradox of 28–30 Behavior Space 20–3, 25, 46–7, 80, 122, 166 Bias 19, 78–9 as cognitive integration 42–6 biology of 36–42 emotion as 33–4, 36, 78, 83–4 functional role of 26 in biased competition 72–5 intention as see intention as bias necessity of 33–6 reward and value 160–1 Biased competition (see also Divisive normalization) 41–3, 73–5, 78–9 Boghossian, Paul 195–6 Bratman, Michael 115 Buehler, Denis 87–8n4, 124n3 Burnston, Daniel 59n5 Blur 220–5 Byrne, Ruth 186–7 Carrasco, Marisa 76 Carroll, Lewis 196, 203 Causal deviance 83–4, 89n5, 130, 185, 188–9 Chelazzi, Leonardo 38–42 Chisholm, Roderick 83–4 Christophel, Thomas 111–13 Chunks (see also working memory) 98, 199–200 Cocktail party effect 76–7, 79–80, 116, 161–2 Cognitive integration 42–6, 52, 78, 110, 116–17, 119, 199, 201–2 defined 43

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Cohen, Jonathan 28–9, 53, 64–5, 74 Continuity of Practical Memory 126–9, 136–7, 139, 143, 145–7, 197–8 Conway, Andrew 104, 116–17 Control 27–33, 49–51, 84–5, 104–10, 121–2, 136, 146–7 and knowledge 201–4 in the central executive 101–2 balance with automaticity 46–8, 176–8, 183 defined 29 Fridland, Ellen on 56n1 gradualism see automaticity gradualism in motor control 51 in thinking 141–4 in parallel distributed processing 53–6 in rules of reasoning 185, 191–8 shifts in 46–8 versus automaticity 130–1 versus Frankfurtian guidance 50–1 Coupling (of input to output) 20, 22, 27, 36, 38, 46–7, 61–2, 70, 75–6, 79–80, 85, 95, 118–19, 213, 218 Cowan, Nelson 98, 100, 102–3, 105 Davidson, Donald 49–50, 52, 83–4 Deduction affirming the consequent 186 denying the antecedent 186 modus ponens 185–91, 193–9, 201–3 Mental Model Theory of 186–91 probabilistic theories of 206n1 suppression effects 186 Delaney, Peter F. 200 Deviant causal chains 83–4 Desimone, Robert 73, 106–7 Diamond, Adele 99–100 Distraction 21–2, 125 by intention see intention as basis of distraction in inattentional blindness 95–117 versus steadfastness 115–22 Divisive normalization 43–5 Duncan, John 73, 106–7 Engle, Randall 99, 104 Emotion 33–4 Empirical sufficient condition 68–9 Ericsson, K. Anders 200 Evans, Gareth 212–15 Evans, Jonathan 206n1 Executive control 94–5, 101–2, 116 Fernández, Guillén 200–1 Frankfurt, Harry 50–1

Gendler, Tamar 58n4 Gertler, Brie 212 Goal neglect 119–21 Griffin, Ivan 132 Groos, Karl 73 Guidance as attention 62–4, 69–70, 81 in action 19–20, 27, 32, 36, 49–50, 52–3, 82, 84–5, 190 missing in deviant causal chains 84, 188–9 Guiding features 26, 36, 85, 175, 185–7, 192, 197 Hallucination 20–1, 35, 225–8 Harman, Gilbert 212 Hawley, Katherine 60n7 Heeger, David 43, 45–6 Holism of Practical Significance 203 Hornsby, Jennifer 52, 59n6, 136 Hursthouse, Rosalind 33 Informational transaction 42–3 Intention and cognitive integration 42–6 as prospective memory (see prospective memory) as bias 26–7 direct 46–8 distal 93–4, 114–15 distraction, as basis of 107 indirect 48, 131 induced distraction 107–9 prior 93–4, 109 -in-action 93, 127, 129, 135, 145, 147–8 Interface Problem 59n5 Introspection complex 215, 220–5, 230n3 reliability conditions 217 simple 215–21, 224–6 task, as a 209–11 unreliability conditions 219 James, William 66 account of attention 70 Jamesian Condition 68–9, 72–3, 75, 86 Jennings, Carolyn Dicey 74–5 Johnson, Gabbrielle 192 Johnson-Laird, Phillip 186 Jonides, John 111–12 Kahneman, Daniel 57n2 Knowledge-how 48, 185, 194–5, 198, 200, 204 Koch, Christof 78 Kriegel, Uriah 220

 Learning 30, 46–8, 60n7, 122, 158–61, 198–204 in reasoning 164, 185, 191–5, 197 to attend 172–3, 181–3 schemas 194–5 Levin, Janet 212 Lycan, William 212 Mackworth, Norman 120–1 Marr, David 67, 70, 73, 162 Miller, George (E. Galanter and K. Pribram) 94, 98, 106–7 Mind wandering 36, 138–9, 178, 184n1 Morris, Richard 200–1 Myers, Nicholas 134 Navalpakkam, Vidhya 160 Neumann, Odmar 69–70 Nobre, Kia 132 Nonobservational access to action 144–7 versus inferential accounts 153n6 Oberauer, Klaus 99, 103 Over, David 206n1 Palmeri, Thomas 29 Parallel distributed processing 53–6 Passive action 30, 32, 36, 49, 79–80 in attending 35, 75–6, 79–80 in emotion 34, 36 as mind wandering 178, 184n1 versus perceiving 79–80 Paul, Sarah 146, 153n6 Peacocke, Christopher 176–7 Poldrack, Russell 73 Postle, Bradley 111–12 Practical Reasoning fine tuning 129–44 in the practical schema 129–32, 134, 138–44 Priority map (see also salience map) 78–9, 159–60 Prospective memory 93–4, 109, 147–8 Receptive Field 39–41, 44–5, 72–3 Reflex as passive 32 pure 21–2, 56n1 spinal cord mediated 21, 49–50 Reynolds, John 43 Rules as bias 196–7 and knowledge 198–201, 203 in learning 191–5, 197 in queue moderation 182–3, 200, 202 in reasoning and deducing 185–6, 191–6 in scanning in radiology 196–7, 200 Ryle, Gilbert 192, 195, 199, 201–2

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Salience 35–6, 78–9, 159–60, 162, 165, 170–2, 175, 179–80 Salience map (see also priority map) 78–9, 162, 165, 170–2 Schema 194–5, 199–204 Selection Problem 19–27, 33, 39–42, 70–2, 75, 106, 122, 167 Shepherd, Joshua 89n5 Simple Connection (between automaticity and control) 29–30, 32–3 Sperling, George 230n3 Spikes (action potentials) 39–41, 72–3 Span tasks complex 104–5, 107, 110, 119 simple 95, 104 Steadfastness 115–22, 141, 143 Steward, Helen 52, 109 Taking things 20–1, 27, 36, 50–1, 53, 65–85, 220 Task switching 56 Towal, R. Blythe 160 Turner, Marilyn 104 Underdetermination of theory by data 162–4 Unsworth, Nash 119 Van Kerkoerle, Timo 113 Vigilance 45–6, 61, 64–5, 87n2, 103, 120–1, 125, 130, 140, 147–8, 150n1, 153n7, 180–1, cognitive 165–6, 177 in deduction 188–9, 191, 202 Vigilance decrement 120–1 Visual agnosia 39 Visual areas V1, primary visual cortex 112–13 V4 39–40 ventral stream 39, 42–3 Watzl, Sebastian 74–5 Working Memory 98–110 capacity Limits 48, 98, 103, 143 central executive 100–7, 110, 123 chunks 98, 199–200 Baddeley-Hitch model of 98, 100–4, 106 Cowan model of 102–4, 106 influence on attention 102, 105–9 long-term memory, relation to 97, 102–3, 200 retro-cue paradigm 132–5 sensory recruitment account 111–13 Xu, Yaoda 112 Yarbus, Alfred 37–8, 41–2, 45–6, 61–2, 77–8, 81, 130–1, 137–8, 168