The Fear of Snakes: Evolutionary and Psychobiological Perspectives on Our Innate Fear [1st ed. 2019] 978-981-13-7529-3, 978-981-13-7530-9

Table of contents :
Front Matter ....Pages i-xiv
Historical Transition of Psychological Theories of Fear: The View of Fear in Behaviorism (Nobuyuki Kawai)....Pages 1-18
Are Snakes Special in Human Fear Learning and Cognition? The Preparedness Theory of Phobia and the Fear Module Theory (Nobuyuki Kawai)....Pages 19-31
The Underlying Neuronal Circuits of Fear Learning and the Snake Detection Theory (SDT) (Nobuyuki Kawai)....Pages 33-58
Ontogeny and Phylogeny of Snake Fear (Nobuyuki Kawai)....Pages 59-71
Do Snakes Draw Attention More Strongly than Spiders or Other Animals? (Nobuyuki Kawai)....Pages 73-94
Other Types of Studies Showing that Snakes Hold Special Status in Threat Perception (Nobuyuki Kawai)....Pages 95-120
Searching for the Critical Features of Snakes (Nobuyuki Kawai)....Pages 121-153
Issues That Remain Unanswered (Nobuyuki Kawai)....Pages 155-180
Back Matter ....Pages 181-187

Citation preview

The Science of the Mind

Nobuyuki Kawai

The Fear of Snakes Evolutionary and Psychobiological Perspectives on Our Innate Fear

The Science of the Mind

Series Editor Tetsuro Matasuzawa Inuyama, Japan

More information about this series at http://www.springer.com/series/10149

Nobuyuki Kawai

The Fear of Snakes Evolutionary and Psychobiological Perspectives on Our Innate Fear

Nobuyuki Kawai Department of Cognitive and Psychological Sciences Nagoya University Furo-cho, Chikusa-ku, Nagoya, Japan

ISSN 2192-6646     ISSN 2192-6654 (electronic) The Science of the Mind ISBN 978-981-13-7529-3    ISBN 978-981-13-7530-9 (eBook) https://doi.org/10.1007/978-981-13-7530-9 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Snakes play an important role in religions, myths, and folklore around the world. They can be representatives of evil, as in Chap. 3 of Genesis in the Bible, where the snake seduced Eve and Adam into eating the fruit of the knowledge of good and evil within the Garden of Eden created by God, resulting in the original sin and expulsion from the Garden. In the Kojiki, the oldest historical writings of Japan, which tells of the formation and development of the country, there is the story of the defeat by Susanoo-no-Mikoto of a huge snake with eight heads and eight tails (Yamata no Orochi). The sword that came from the snake’s tail, the Kusanagi-no-Tsurugi (“Grasscutter Sword”), is still today handed down to a new Emperor in the ­enthronement ceremony as a testimony of Imperial Rank, as one of the Three Sacred Treasures of Japan. Because snakes are held in awe, there are certain myths in which a snake exists in proximity to a god. Asclepius, son of Apollo, carried a snake-entwined staff wherever he walked and could heal all diseases based on the medical wisdom provided by the snake—he could even raise the dead. His staff is now the symbol of the World Health Organization (WHO) and of the World Medical Association (WMA). The Pharaohs of Egypt wore a decorative snake on their head to demonstrate their authority, which derived from the gods. In India, where the snake is called Nāga, Shakyamuni was considered the descendant of Nāga, who protected the Buddha. The shimenawa, a sacred rope in Shinto shrines in Japan, marks the border between profane and holy spaces, and the twisted rope has a snake-like motif. No other animal appears in myths as much as snakes do. We can thus glimpse the importance of snakes to humans throughout history. I was raised at the side of the Fushimi Inari Taisha in Kyoto which is established in 711 A.D., the shrine most visited by tourists in Japan. In addition to the numerous smaller shrine buildings on the Fushimi Inari grounds, there are several tens of thousands of vermillion-colored torii, Shinto archways, which cover the entire hill. The broad grounds with their abundant natural spaces are home to numerous wild animals. I remember the great uproar I made as a child whenever I saw a snake. Looking back, I understand how snakes are special entities for children as well.

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The experimental methods described in this book show how primates and humans discover snakes quickly and efficiently. It was the snake detection theory (SDT) of Lynne Isbell that spurred my research. In the SDT, inasmuch as snakes were a chief danger to our ancestors for hundreds of thousands of years, the vision systems and the brains of primates and humans are hypothesized to have evolved so as to detect snakes speedily and efficiently. During these long years of their evolution, human ancestors had to focus their attention on snakes, and today, we, too, cannot escape our fascination with them. Whenever I publish one of my studies on snakes, it is introduced by websites, newspapers, and/or sometimes even television news, both in Japan and abroad—such is the strong interest that people have regarding our abilities to detect snakes. We researchers are just as fascinated by SDT as nonspecialists, and together, we have accumulated numerous research results regarding this theory. While Isbell’s papers and books present the foundation for this field, the present book provides a summary of psychological and neurophysiological experiments performed since then and discusses their findings as further considerations of her initial theory. Nevertheless, this book is not dedicated solely to snake detection. The first half discusses the history of psychological behaviorism and neobehaviorism, with their idea that no stimuli exist that can be solely determined by the innate processing abilities of living creatures, and the history of how that premise was finally overthrown. Chapter 2 presents a review of the history of psychology and describes how knowledge of progress made in psychological science is considered beneficial for the performance of novel research. By reflecting on the history of psychological science, the reader will gain an understanding of how current studies are situated in the flow from past research. The latter half of this book consists mainly of the presentation of individual research studies other researchers and I have performed and published, with a focus on the following three key items. First, compared with other animals, snakes especially draw the attention of primates and humans. Second, the ability of primates and humans to recognize snakes with particular efficiency has been confirmed by numerous different experimental methods. New research methods performed even after finishing the draft of this book have demonstrated that snakes capture attention even more than other reptiles. Third, this book discusses processing mechanisms within the brain for snake detection from a somewhat novel viewpoint. While subcortical visual pathways have hitherto been emphasized, research is introduced that suggests the possibility that visual areas in the cerebral cortex are involved in making snake recognition speedy and efficient. As in all science, the facts and ideas presented in this book may possibly be refuted in the future. Yet, such is the nature of science, as ever more precise facts are accumulated using improved methods, entailing revision of previously described facts and theories as described in Chap. 2. While this book shows that all humans have the ability to detect snakes speedily and efficiently, it is still unclear exactly how this is related to the fear of snakes (“ophidiophobia”). During the process of evolution, snakes were continuously a

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menace, meaning that there is nothing odd about snakes being an object of fear. In fact, snakes are feared at the highest levels. At the same time, many of my own friends and my daughter love snakes, even while there are those whose fear of snakes is so extreme that they cannot even say the name “snake.” While all persons share the same visual system that enables snake recognition, why are there individual differences in the extent of fear felt toward snakes? While the title of this book may give the impression that the answer will be found in its pages, unfortunately, that is not the case. There is no clear answer to this question yet. So far, it was thought that fear of snakes was independent of subjective fear as snake recognition did not follow any cortical pathways. Yet, if, as this book suggests, visual cortical areas are indeed involved in snake recognition, then perhaps, the “fear of snakes” stems from this. It is hoped that the curiosity of readers will be stimulated by reading this book, generating further numerous studies, with the anticipation that these will result in an explanation of why so many people are “afraid of snakes.” Nagoya, Aichi, Japan  Nobuyuki Kawai

Acknowledgment

My research career started with studies of aversive conditioning in rats. Our laboratory supervisor was Professor Hiroshi Imada, an old friend of Professor Martin E. Seligman, who proposed the concept of “preparedness,” and translator of a book by Professor Robert C. Bolles, who proposed the concept of innate species-specific defense reactions. Thus, our understanding then was that in conditioning and learning, all stimuli were not always learned in the same way. In my studies of conditioning using electrical shocks, I was not able to sense how each animal had their own specific stimuli and responses. Yet, in preliminary experiments performed with Professor J. Bruce Overmier concerning defensive burying in rats, I gained a clear idea that animals indeed had innate reaction mechanisms. Professor Hiroyuki Iso also gave me the chance to do experiments with rats on avoidance learning. Here, I experienced how rats could easily learn the desired response to identical shocks in one situation, even while they could not in another situation. These experiments led to my later research on avoidance learning in crayfish, which I describe briefly in this book. I might not ever have performed my research on snake recognition if not for these experiences I had doing experiments on aversive learning and avoidance learning. I am grateful for my fine teachers of that time, Hiroshi Imada, J. Bruce Overmier, and Hiroyuki Iso. Thereafter, I spent 3 years in postdoc work at the Primate Research Institute of Kyoto University. There, I engaged in research on cognition in chimpanzees, with Professor Tetsuro Matsuzawa. I was fascinated by research on primate cognition, because I felt as if I was able to see the evolution of human cognition. Thereafter, I have continued research on primate cognition. Moving on to Nagoya University, however, my main research focused on human cognition and emotions, where, for some time, I was still unaware that certain animals and specific stimuli had significance for living creatures, including humans. Then, however, I had the opportunity to be involved with my graduate student, Dr. Masahiro Shibasaki, who was interested in negative events for primates. We examined the SDT in investigating whether monkeys who had never seen a snake could quickly detect snakes. Thereafter, I gradually shifted to research involving snake recognition. My research with monkeys was conducted in collaboration and cooperation with Dr. Masahiro Shibasaki, ix

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Acknowledgment

Dr. Hiroki Koda, Professor Nobuo Masataka, Dr. Sachiko Hayakawa, Keiko Ishida, Akemi Kato, and Sumiharu Nagumo at the Primate Research Institute of Kyoto University. My studies with humans were joint research involving various persons, including my graduate student, Hongshen He, Huachen Qiu, and my postdoc student, Dr. Kenta Kubo, at Nagoya University. Professor Gordon M. Burghardt and Dr. Akira Mori brought the snakes for my precious experiences of direct experimentation using snakes. I learned much about the ecology of snakes from Gordon. A neuroanatomist, Dr. Noritaka Ichinohe, whom I have been worked with marmoset research critically read Chap. 3. In addition to support by the scholars, our studies were financially supported by grants from MEXT (21119511, 23119711, 18H06070) and JSPS KAKENHI (25285199, 15K13159, 16H02058). The studies of monkeys were conducted under the Cooperation Research Program of the Primate Research Institute of Kyoto University. Certainly, my research on snake recognition would not have developed this far without the theory of Professor Lynne A. Isbell. Although I have never met her, I am grateful for the contributions of her scholarship in my research. Further, Professors Edward Wasserman and J. Bruce Overmier gave me encouragement whenever I met them at academic meetings. I am deeply grateful for their mental support. As I described in the Preface, I grew up at the side of Inari Mountain in Kyoto, and my childhood friends there have shown interest in my research and still have provided me with their encouragement. I also want to express my gratitude to Wataru Kawashima and Satomi Oyabu. When my daughter, Waka, was just a child, she wonderfully expressed her fear of snakes when I showed her some cards I had prepared as shown in Fig. 4.1. I have always gotten a big response whenever I have shown the video thereof during a presentation. Now, however, she loves snakes. Moreover, my son, Kai, and wife, Namiko, have always given me their support in my research life. I have published five Japanese books as a single author, and this is the sixth but first English book, which I never imagine to write. Finally, I express my gratitude to Professor Tetsuro Matsuzawa, who guided me in my research with primates and who also provided me with this opportunity to write the present book.

Contents

1 Historical Transition of Psychological Theories of Fear: The View of Fear in Behaviorism�����������������������������������������������������������    1 1.1 What Do People Fear the Most? ������������������������������������������������������    1 1.2 Behaviorism��������������������������������������������������������������������������������������    2 1.3 Psychological Fear from a Behaviorist Perspective��������������������������    3 1.4 Phobias as the Result of Conditioning����������������������������������������������    4 1.5 Conflicting Phenomena and the Beginning of a Grand Theory’s Dissolution������������������������������������������������������    6 1.6 The Premise of Equipotentiality of Learning������������������������������������   10 1.7 Biological Readiness in Taste Aversion Learning����������������������������   10 1.8 Failure of Avoidance Learning in Rats����������������������������������������������   11 1.9 Constraints on Avoidance Learning in Crayfish ������������������������������   12 1.10 The Preparedness Theory of Phobias������������������������������������������������   14 References��������������������������������������������������������������������������������������������������   17 2 Are Snakes Special in Human Fear Learning and Cognition? The Preparedness Theory of Phobia and the Fear Module Theory����   19 2.1 The Neo-conditioning Model of Fear ����������������������������������������������   21 2.2 Selectivity of Stimuli in Classical Conditioning of Fear in Humans����������������������������������������������������������������������������   22 2.3 The Encapsulation Theory of Fear����������������������������������������������������   23 2.4 Unconscious Control of Responses to Fear-Related Stimuli Presented Through Backward Masking ������������������������������   24 2.5 Illusory Correlations Between Fear-Related Stimuli and Aversive Events��������������������������������������������������������������������������   25 2.6 Resistance to Extinction��������������������������������������������������������������������   25 2.7 The Development of the Fear Module Theory����������������������������������   26 2.8 Most Primates Fear Snakes��������������������������������������������������������������   27 2.9 The Evolution of Fear Modules Related to Snakes��������������������������   27 References��������������������������������������������������������������������������������������������������   28

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3 The Underlying Neuronal Circuits of Fear Learning and the Snake Detection Theory (SDT) ������������������������������������������������   33 3.1 The Amygdala as the Brain’s Fear Center����������������������������������������   33 3.2 The Amygdaloid Complex����������������������������������������������������������������   34 3.3 The “Low Road” and “High Road” to the Amygdala����������������������   35 3.4 The Amygdala May Not Be the Brain’s “Fear” Center��������������������   37 3.5 New Theory of Innate Threats Not Related to Learning������������������   37 3.6 The Snake Detection Theory (SDT) ������������������������������������������������   38 3.7 Emergence of Primate Ancestor Species and Their Predators ��������������������������������������������������������������������������   39 3.8 Uniqueness of SDT ��������������������������������������������������������������������������   41 3.9 The Connections of the Fear Module������������������������������������������������   42 3.10 The K Pathway����������������������������������������������������������������������������������   43 3.11 The Superior Colliculus (SC)������������������������������������������������������������   43 3.12 The Lateral Posterior Nucleus (LP)-Pulvinar Complex��������������������   44 3.13 The Amygdaloid Complex����������������������������������������������������������������   45 3.14 The Locus Coeruleus (LC)����������������������������������������������������������������   45 3.15 The Connections of the LP-Pulvinar Visual System������������������������   45 3.16 What Are the Functions of the Fear Module?����������������������������������   46 3.17 Snake-Detecting Neurons?����������������������������������������������������������������   51 3.18 Predictions Using SDT ��������������������������������������������������������������������   52 References��������������������������������������������������������������������������������������������������   53 4 Ontogeny and Phylogeny of Snake Fear������������������������������������������������   59 4.1 Is Fear of Snakes Learned? ��������������������������������������������������������������   59 4.2 Are Infants Afraid of Snakes?����������������������������������������������������������   61 4.3 Laboratory-Raised Monkeys Do Not Show Fear Responses Toward Snakes����������������������������������������������������������������   62 4.4 Attention Bias Toward Snakes in Infants and Adults������������������������   63 4.5 Attention Toward Threatening Stimuli Is Regulated by Experience������������������������������������������������������������������������������������   66 4.6 Monkeys Quickly Detect Snakes������������������������������������������������������   67 References��������������������������������������������������������������������������������������������������   69 5 Do Snakes Draw Attention More Strongly than Spiders or Other Animals?������������������������������������������������������������������������������������   73 5.1 Visual Search Tasks with Adults: Animals Draw Attention More Strongly than Non-animal Stimuli��������������������������   74 5.2 Snakes Are Detected More Quickly than Other Threatening Animals������������������������������������������������������������������������   74 5.3 Are Spiders Dangerous to Humans and Monkeys?��������������������������   76 5.4 Attentional Capture and Hold in Visual Search Tasks����������������������   77 5.5 How Much Do Snakes and Spiders Hold Attention?������������������������   78 5.6 Snakes Draw Attention Automatically Even When Perceptual Load Is High, But Spiders Do Not����������������������������������������������������   83

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5.7 Cultural Differences in the Fear of Spiders��������������������������������������   85 5.8 Is Fear of Spiders Learned?��������������������������������������������������������������   86 5.9 Monkeys Quickly Find Pictures of Snakes, But Not Pictures of Spiders, Among Pictures of Non-threatening Animals ��������������������������������������������������������������������������������������������   88 References��������������������������������������������������������������������������������������������������   90 6 Other Types of Studies Showing that Snakes Hold Special Status in Threat Perception��������������������������������������������������������������������   95 6.1 Problems with Visual Search Experiments ��������������������������������������   96 6.2 Snakes Evoke a Greater Early Posterior Negativity in Event-Related Potentials than Do Spiders������������������������������������   98 6.3 Brief Exposure (More Than 30 ms) to Snake Images Evokes Enhanced EPN Amplitudes��������������������������������������������������  102 6.4 Humans Recognize Snakes’ Camouflage More Accurately Than Other Animals’������������������������������������������������������  104 6.5 Spiders Are Not Recognized More Accurately Than Other Insects����������������������������������������������������������������������������  107 6.6 Spatial Frequency Analyses of the RISE Stimuli ����������������������������  112 References��������������������������������������������������������������������������������������������������  117 7 Searching for the Critical Features of Snakes ��������������������������������������  121 7.1 Perceptual Template��������������������������������������������������������������������������  123 7.2 Coloration ����������������������������������������������������������������������������������������  123 7.3 Elongated Limbless Body Shape������������������������������������������������������  124 7.4 Curvilinear Body Shape��������������������������������������������������������������������  125 7.5 Body Shape and Posture Unique to Snakes��������������������������������������  126 7.6 Research Showing That Specific Spatial Frequency Components Elicit Threat and Discomfort ��������������������������������������  130 7.7 Studies Showing That Specific Spatial-Frequency Components Are Not Relevant for Threat and Snake Detection������  133 7.8 Only a Portion of the Snake Body Is Sufficient for Recognition and Focusing of Attention��������������������������������������  138 7.9 Scales Are a Key Cue for Snake Detection��������������������������������������  140 References��������������������������������������������������������������������������������������������������  149 8 Issues That Remain Unanswered������������������������������������������������������������  155 8.1 Individual Differences in Fear of Snakes������������������������������������������  156 8.2 Sensitivity to Fear and Genetic Polymorphism��������������������������������  157 8.3 Correlation Between Individual Differences in Fear Response in Monkeys and Genetic Polymorphism��������������������������  159 8.4 Is Recognition of Snakes Processed in the Subcortical Areas?��������  162 8.5 Do the Subcortical Pathways Process Information Earlier Than the Cortex? ������������������������������������������������������������������  163 8.6 Cortical Regions That May Be Involved in Snake Recognition ������  169

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8.7 Is Snake Recognition Processed by a Special Visual Pathway? ������  172 8.8 Tentative Proposal Regarding the Snake Recognition Neural Circuit������������������������������������������������������������������������������������  173 8.9 Conclusion: From the Studies of Innate Defensive Mechanisms to Studies of Subjective Fear of Snakes����������������������  175 References��������������������������������������������������������������������������������������������������  176 ������������������������������������������������������������������������������������������������������������������ 181

Chapter 1

Historical Transition of Psychological Theories of Fear: The View of Fear in Behaviorism

Abstract  This chapter will discuss changes in psychological theories regarding concepts of how fear develops. In the early period of psychology, the psychology of learning exerted a strong influence, and it was thought that most behaviors and mental processes were acquired through experience; thus, phobias and fears of specific animals and places were believed to be acquired through learning. Once fear is felt toward a particular animal on one occasion, an individual becomes afraid of that animal through classical conditioning. Mowrer used a two-process model to explain that the avoidance response is maintained even if the animal does not cause any further fear in the individual. His theory was widely accepted not only by experimental psychologists but also by clinical psychologists. However, the results of numerous experiments have proven that the premise of equipotentiality, which holds that any stimulus and response can be learned, is invalid. A rat can associate a taste with sensations of illness when these stimuli are paired just once; however, it is difficult for it to associate audiovisual stimuli with sensations of illness even when these stimuli are paired several dozen times. Rats quickly learn to press a lever to obtain food, and they quickly learn to flee to the next room to turn off an electric shock; however, they cannot learn to stop the delivery of an electric shock beforehand by pressing a lever. Seligman asserts that the evolutionary processes of all animals have a “preparedness” for their natural environments, and on this basis, it is easy for them to learn some things and hard for them to learn others.

1.1  What Do People Fear the Most? All people are afraid of something: death, heights, loud sounds, and frightening animals. It may be that only certain people are afraid of particular objects; however, there are also things that are commonly feared by many people. An Internet survey (Bryner 2011) showed that the top ten most feared things were the following in order from tenth place to first place: dentists, dogs, frightful flight, thunder and lightning, darkness, extreme heights, other people, scary places, small crawling creatures, and slithering snakes. © Springer Nature Singapore Pte Ltd. 2019 N. Kawai, The Fear of Snakes, The Science of the Mind, https://doi.org/10.1007/978-981-13-7530-9_1

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1  Historical Transition of Psychological Theories of Fear: The View of Fear…

In some cases, a person is so afraid of a thing that it makes daily life difficult. A phobia is an excessive and irrational fear of an object, place, or situation. Examples of phobias include fear of open spaces (agoraphobia), fear of closed spaces (claustrophobia), fear of heights (altophobia), fear of public speaking (social phobia), fear of spiders (arachnophobia), and fear of snakes (ophidiophobia). In some of these cases, the fear is justified. Heights and poisonous snakes can both be life-­threatening. However, if a person is afraid of open spaces or feels extreme fear of heights, snakes, or spiders despite recognizing that they are not dangerous, this is classified as a phobia. The subjects of phobias are not random. The fact that many people have common fears of the same things suggests that people have an innate tendency to fear those things. At present, most researchers agree that not all people are afraid of the same things. For instance, some people are afraid of heights or open spaces, while others feel fear toward specific things or specific types of animals (Lumsden and Wilson 1982; Seligman 1971). However, because experience was emphasized in the early period of psychology, it was believed that most psychological states such as behavioral tendencies, anxiety, etc. were acquired by learning through experience. This idea had a significant influence, and some cognitive-behavioral therapists still believe that “people who are afraid of dogs feel fear because they were bitten or saw someone else being bitten by one.” Do people who are afraid of snakes, the most common fear, also acquire their fear because they were bitten or saw someone else being bitten by one? My wife was bitten while a neighbor was walking his dog in front of our home, but she is not afraid of dogs. However, my wife is afraid of snakes, although she has never been bitten by one. Why does this type of phenomenon occur? One simple explanation is that people innately fear certain things. Psychology has long resisted this simple explanation. This is due to the significant influence of behaviorism, which holds that people are born with their minds as blank slates, which are then written on by experience. Due to the influence of this idea, it was believed that fears of particular things and even clinical phobias were acquired through experience. In this chapter, to trace changes in ideas regarding the fear of particular things, I will present an outline of early psychological research and concepts.

1.2  Behaviorism In the early days of psychology especially in the United States, behaviorism exerted a significant influence. The essence of behaviorism is expressed in the following powerful words by Watson. The study of the mind is the province of philosophy; it is the realm of speculation and endless word games. The mind has no place in psychology. A science of psychology must be based on objective phenomena and the ultimate explanation must be found in the central nervous system. (Watson 1914)

1.3  Psychological Fear from a Behaviorist Perspective

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Behaviorism held that measurable responses and objective phenomena were the only appropriate subjects of psychological study. In addition, due to the long-­ standing influence of empiricism and the success of conditioning experiments by Thorndike and Pavlov, it was believed that because nearly all human behavior, concepts, and thinking were shaped by learning, the appropriate subjects of psychological study were these objective phenomena and the learning that provided their foundation. Philosophers from Locke’s time onward held that people learned through experience and that this learning established associations between concepts. For instance, when people see white, they have a tendency to think of black. The reason is that sensations of white and sensations of black are associated through prior experience, and, thus, mental associations are formed between these concepts. Obviously, only humans think of white when they see black and of black when they see white. It is unclear how this association between concepts (or thought process) occurs; however, British empiricist philosophers believed that it occurred in a deterministic, mechanical way. However, Thorndike’s experiment with cats showed that the concept of habits, which had previously been regarded as taking shape automatically and mechanically, could be acquired even by animals, and the process could be objectively traced. In his experiment, cats learned to escape from a puzzle box by pulling a string and pressing a pedal. Of course, the cats did not associate the concept of pulling the string with the concept of the food outside the box in which they were placed. It would seem that if the cats held the concept of pulling the string when placed in the box, the time it took for them to escape from the box would at first be random. Then, at some point it would suddenly improve (the “aha!” phenomenon), after which it would transition to a fairly brief span of time. However, the time it took for the cats to escape shortened as the trials were repeated. This proved that habits and learning behavior, once thought to be formed automatically, had a particular formation process and particular factors that caused their formation, which could be discovered by investigating the environment (the stimulus) and the effects (the results). For instance, suppose that you are shown the numbers 8 and 9. If you are told that this is a math problem, you may answer 17, or you may answer 72. The answer −1 would also be correct. If you answered with the number 17, this was reinforced, when subsequently presented with the numbers 4 and 6, you would likely answer 10 rather than 24 or −2. It was demonstrated that such unconsciously formed associations between concepts could be discovered through experiments.

1.3  Psychological Fear from a Behaviorist Perspective Watson was not the originator of behaviorism, but he is viewed as its leading figure. His best-known achievements are the speech he gave upon his inaugural speech as president of the American Psychological Association and the Little Albert experiment.

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In the ill-reputed Little Albert experiment for which he became known, fear was instilled in a baby through classical conditioning (Watson and Rayner 1920). Albert, the experiment’s subject, was a healthy, normal baby. The conditioned stimulus (CS) was a rat, and Albert’s initial response to it was curiosity. When he saw the rat, he reached out his hand to touch it. The unconditioned stimulus (US) was a sound made by hitting a heavy steel bar with a hammer. Because this was a very frightening sound, the baby’s unconditioned response (UR) involved showing surprise, crying, and running away. When the rat and the frightening sound were paired three times, simply seeing the rat was enough to induce various fearful, defensive behaviors. After the sixth conditioning, when Albert saw a rat, he exhibited a strong emotional reaction. Subsequently, Watson and his colleague tested whether generalization would occur in response to a white rabbit, a dog, and a fur coat. Albert showed a strong emotional response to all of these. When a test was performed several days later under different conditions, Albert’s emotional response had markedly lessened, but it was still apparent. In one trial with additional conditioning, the new situation induced remarkable fear. This study showed the basic principles behind fear conditioning (generalization, the context effect, extinction, and relearning). In his paper, Watson discussed such issues as methods of removing Albert’s fear conditioning, concluding that the fear would continue over a relatively long span of time; however, Albert left the nursery without receiving any treatment to remove the fear. The experiment clearly showed that phobias are acquired through learning. It was believed that classical conditioning played a significant role in the acquisition of phobias (which are “unreasonable” in that the individual understands that there is no danger) (Levine and Sandeen 1985). Watson believed, as did British empiricists, that people were “tabula rasa” or blank slates at birth and that all experiences subsequently had to be written on that blank slate. Watson (1930) made the following well-known statement: “Give me a dozen healthy infants, well-formed, and my own specified world to bring them up in and I’ll guarantee to take any one at random and train him to become any type of specialist I might select—doctor, lawyer, artist, merchant-chief and, yes, even beggarman and thief, regardless of his talents, penchants, tendencies, abilities, vocations, and race of his ancestors.” This expresses his view that most of our characteristics and behavioral tendencies are cultivated by experiences after we are born.

1.4  Phobias as the Result of Conditioning At first glance, Watson’s phobias appear to provide a good explanation as to why people are afraid of particular things. For instance, I may be afraid of dogs because I had an experience of a dog barking at me and chasing me as a child. Since then, however, I have encountered many dogs that did not bark. My stepfather had a dog, and it would cling to me playfully whenever I went to his house. It never once bit me, but I still feel fear when dogs come near. From a behaviorist point of view, even if one learns a fear of dogs in childhood, if there are no further frightening

1.4  Phobias as the Result of Conditioning

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experiences with dogs, the fear should extinguish. Why does fear fail to extinguish, persisting even if it is initially acquired through classical conditioning? Mowrer (1947) proposed a two-process model of avoidance learning to explain the development and persistence of phobias. Mowrer’s two-process model combines the principles of classical conditioning and operant conditioning, so it is referred to as either the two-process model or the two-factor model. Here, let us look back on experiments involving avoidance learning as a model of phobias. Avoidance learning experiments are typically performed using a device called a shuttle box with rats as the subjects. This is an experimental apparatus used for escape/avoidance learning, consisting of two compartments similarly constructed from floor grids charged with electric current so that rats can shuttle between two rooms to escape electric shocks (US) delivered following signals presented at fixed times (CS) or to avoid electric shocks by anticipating their arrival during the signal. When rats are placed in this situation, at first, not understanding, they show no reaction to the signal. They are shocked, writhe around, and then stop the electric shock by moving to the next compartment (escape). However, after experiencing this just a few times, the rats easily learn to move to the next room and prevent (avoid) the electric shock when the signal is presented. Once this learning is established, the rats rarely receive electric shocks, so they do not experience fear and continue their avoidance behavior. If this avoidance behavior was supported only by classical conditioning through the initial signal and electric shock, their fear of the signal would ultimately extinguish. Mowrer believed that this avoidance behavior was acquired and maintained through two processes. The first process is the classical conditioning of fear (anxiety). At the early stage of learning, rats receive an electric shock (US) following a (for instance, 10-s) signal (CS), that is, the pairing of the CS and US of the electric shock. As a result, classical conditioning of fear forms in response to the signal (CS). Next, because fear is a powerful motivator, when it arises, the rats undertake a trial and error process to eliminate it. Then, they learn the avoidance response of moving to the next compartment during the CS. Thus, the CS stops; they do not receive the electric shock and are released from fear. This is the second process. In this process, fear acts as a motivator, and operant conditioning takes place with the reward (reinforcement) being the reduction of fear through escape/avoidance behavior. If the fear is the result of classical conditioning in response to the signal, stopping of the signal should be accompanied by a reduction in fear (the motivator). Therefore, one would assume that the stopping of the signal would serve to reinforce the avoidance response. Thus, Mowrer and his colleagues trained three groups of rats under an avoidance learning situation in the shuttle box (Mowrer and Lamoreaux 1942). The relationships between the avoidance response and the stopping of the signal differed between the three groups. In the first group, trace conditioning was performed, and signals with very short durations were shown and then eliminated several seconds prior to the electric shock for which they provided advance notice. That is, it was possible to avoid the electric shock through an ­avoidance response after the signal was given; however, by this time, the brief signal

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had already disappeared, so the avoidance response prevented the electric shock but did not have the effect of stopping the signal. In the second group, the signal was very long and continued for a while even after the electric shock; therefore, the signal continued for a few seconds even if an avoidance (escape) response occurred. That is, it was possible for the response to prevent the electric shock, but not to stop the signal. In the third group, the signal was given in the normal manner, and it was possible to stop the signal and avoid the electric shock through an avoidance response. Because the first and second groups failed almost completely to learn the avoidance/escape response, the results showed that the notion that avoidance behavior is mediated by classically conditioned fear is correct. Specifically, it seems that Mowrer’s theory is supported by an explanation of avoidance learning as a combination of initial classical conditioning of fear toward the electric shock and the paired signal and further operant conditioning by the fear the signal elicits. If the signal that is the source of the fear cannot be stopped through the avoidance response, the motivator (fear) cannot be reduced, so the behavior is not reinforced. This was also thought to be extremely persuasive as an explanation for the symptoms of human anxiety disorders and obsessive-compulsive disorder. For instance, when an individual with social anxiety decides to avoid participating in a large social event, his or her anxiety lessens markedly. Unpleasant anxiety symptoms are eliminated through the avoidance, so the avoidance behavior is reinforced. Similarly, suppose that this person participates in the party despite feeling anxious and therefore experiences a panic attack. If this person is able to quickly leave the party, the panic quickly ends. The escape behavior is then reinforced by the lessening of the panic symptoms. Mowrer’s theory has been put into practice by numerous clinicians and achieved some success in reducing symptoms of obsessive-compulsive disorder (Emmelkamp 1982; Hodgson and Rachman 1972). It was believed that studies on fear acquisition using animal subjects could be applied to psychopathology and that fear observed in animals was functionally equivalent to a phobia (Wolpe 1976).

1.5  C  onflicting Phenomena and the Beginning of a Grand Theory’s Dissolution For quite some time, it was hypothesized that learning psychologists could use any type of stimulus as the CS and the US could be any type of stimulus that caused a clear reaction in the organism. Statements by Pavlov also indicate support for this notion. Any natural phenomenon chosen at will may be converted into a conditioned stimulus … any visual stimulus, any desired sound, any odor, and the stimulation of any part of the skin. (Pavlov 1928, p. 86)

The same is the case for the US.

1.5  Conflicting Phenomena and the Beginning of a Grand Theory’s Dissolution

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It is obvious that the reflex activity of any effector organ can be chosen for the purpose of investigation, since signaling stimuli can get linked up with any of the inborn reflexes. (Pavlov 1928, p. 17)

However, Watson (1925) in fact believed that there were latent links between some types of stimuli and responses. Instinct, which is the foundation of learning behavior, was discussed by Watson at length. According to Watson, instinct is merely an S-R link. There are cases in which this is a simple S-R link and those in which it is a complex S-R link; regardless, the two have a very strong link before pairing occurs in reality. Watson believed that all that separates people from animals is that people have more extensive latent S-R links. Thus, people can perform a wider range of behaviors than animals and are sensitive enough to distinguish between even minor differences in stimuli. Watson (1916) did not distinguish between behaviors that change with classical conditioning (defensive responses such as pulling the feet in and emotional responses such as changes in heart rate observed simultaneously). Thus, he is believed to have regarded all behaviors and emotions as the result of learning through experience. However, he implicitly acknowledged that certain stimuli have the latent potential to incite emotional responses. Here, let us reexamine his well-known Little Albert experiment. In this experiment, a frightening sound was used as the US and a white rat as the CS. However, Bregman (1934), who performed a replication study, succeeded in forming similar fear conditioning with an animal as the CS but was unable to establish fear conditioning with an inanimate object such as a brick or a bottle as the CS. At the time, it was believed that infants were not suitable participants for conditioning experiments, and Watson’s experiment succeeded in spite of this because the CS was an animal. Thus, conditioning could not be established in infants if the CS was not an animal, and fear was not acquired if it was, for instance, a wooden animal (Jones 1924). However, our experiment using a fetal chimpanzee, the species most closely related to human beings, indicates that the CS does not have to be an animal to establish fear conditioning in infancy. We presented two pure tones (500, 1000 Hz) to a 201-day-old chimpanzee fetus from outside the womb, pairing one tone (500  Hz) with a vibroacoustic stimulus (VAS) (the fetus writhed around in the womb as the UR in response to a vibrational stimulus of 80 Hz/110 Gal) and providing no paired stimulus with the other tone (1000 Hz). The fetus was assumed to be able to hear these tones. After being born on the 233rd day of gestation, the two types of stimuli were randomly presented three times each in tests conducted at 1 day, 33 days, and 58 days after birth. The subject exhibited strong reactions to the stimulus paired with the vibrational stimulus but did not react to the other stimulus, clarifying that differentiated conditioning had been established. In addition, when unconditioned chimpanzees were made to listen to these sounds at 1  month and 2 months of age, they exhibited no response (Kawai et al. 2004b; Kawai 2006). In other words, fetal chimpanzees, the closest animal relative of human beings, can learn to associate non-animal stimuli (pure tones) with unpleasant stimuli. This suggests that human infants are also capable of simple learning. At just 2–3 days after birth, a human infant prefers the voice of its own mother to that of a

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female stranger (DeCasper and Fifer 1980). While it is possible that the baby prefers his or her mother’s voice because of postnatal experiences, even when the baby is prevented to the greatest possible extent from hearing his or her mother’s voice after birth, he or she shows a preference toward his or her mother’s voice and shows no preference for his or her father’s voice (DeCasper and Prescott 1984), suggesting the possibility that newly born infants have heard and learned their mothers’ voices in the womb. Because they already prefer their mothers’ voices at 2–3 days after birth, at the very least, it can be said that newly born infants are not blank slates (Figs. 1.1, 1.2, and 1.3). We also investigated whether human fetuses were capable to form a simple learning (i.e., habituation) to VAS. Fetuses showed habituation from at least 32 weeks of gestation (Morokuma et al. 2004). Thorndike, a key figure in behaviorism, acknowledged the concept of “readiness,” such that the thresholds at which particular stimuli cause particular reactions differ (Thorndike 1913). For instance, the sound of a person scraping their nails on glass is more likely to be perceived as aversive than other high-frequency sounds, because most people have a readiness for the former. He referred to sensitivity as readiness and believed that higher readiness meant a lower threshold for transmission between nerve cells causing reactions (including thoughts) to particular situations. He did not believe that people’s minds started out as completely “blank slates.”

Fig. 1.1  The pregnant chimpanzee and an experimenter to whom she was well habituated during the conditioning treatment with speaker and stimulator placed on her lower abdomen (Kawai 2006; Kawai et al. 2004b)

1.5  Conflicting Phenomena and the Beginning of a Grand Theory’s Dissolution

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Fig. 1.2  The experimenters watched an ultrasonic image of the feral chimpanzee from outside the experimental booth, in which the other experimenter placed devices on the pregnant chimpanzee’s lower abdomen (Kawai 2006; Kawai et al. 2004b)

Fig. 1.3  Successive frames from digital video recordings and the graphically subtracted images (Kawai 2006; Kawai et al. 2004b)

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1.6  The Premise of Equipotentiality of Learning Psychologists were somewhat aware that the belief of nurturists (e.g., Kuo 1921) that all behaviors were learned and none were innate was incorrect; however, this became more apparent in the late 1960s, when numerous “constraints on learning” were discovered. Previously, learning psychologists had accepted the premise of equipotentiality, or the hypothesis that all stimuli can be conditioned in the same fashion. However, this does not indicate that all stimuli and responses can be learned at the same speed. Pavlov himself acknowledged that the speed of conditioning differed depending on the CS. For example, the CR should be acquired more rapidly for bright light than for dim light. However, the premise of equipotentiality hypothesizes that in spite of differences in the ease of conditioning of various stimuli (responses), a stimulus (response) that is difficult to condition in a particular context should remain difficult to condition in other contexts. For instance, if numerous trials are required for the CS of dim light to condition salivation, then this stimulus should similarly require numerous trials in an experiment on eyeblink conditioning. This premise of equipotentiality requires that a particular stimulus should be similarly effective (or ineffective) as a CS in any context. However, conditioning is not established in the same way for all stimuli.

1.7  Biological Readiness in Taste Aversion Learning There are biological constraints on learning, and conditioning is not established for particular combinations of stimuli and responses—that is, selective association exists—as first demonstrated by the study on taste aversion learning by Garcia and colleague (Garcia and Koelling 1966). This study cast doubt on the equipotentiality premise, not because conditioning occurred with a single pairing or with a 24-h CS-US interval but because “associations were not formed” for particular combinations of stimuli (Etscorn and Stephens 1973). In this experiment, rats were given sugar water as gustatory stimuli and audiovisual stimuli (flashing lights and clicking sounds) were presented. For all rats, these compound stimuli of gustatory and audiovisual sensations were paired with an aversive phenomenon. In some groups the aversive phenomenon was illness, while in other groups, it was an electric shock. Two of four groups of rats were made ill by radiation emitted following these stimuli. The remaining two groups of rats received electric shocks through their feet. To confirm the strength of the association between the two stimuli (gustatory and audiovisual sensations) and the two aversive phenomena, an extinction test was

1.8  Failure of Avoidance Learning in Rats

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conducted wherein the gustatory and audiovisual stimuli were presented separately. The rats that felt ill drank nearly no sugar water, in contrast to their behavior prior to conditioning (taste aversion learning); however, there was nearly no change in their sugar water intake amount when the audiovisual stimulus was presented. In contrast, the opposite pattern was observed in the rats who received the electric shock. The sugar water intake amount of these rats was roughly the same as the previously measured value; however, the intake amount when the audiovisual stimulus was presented was less than 20% of that prior to the conditioning. Based on this experiment, it is not possible to conclude whether the gustatory or audiovisual stimulus was more effective or whether the electric shocks or sensations of illness were more aversive phenomena. The gustatory stimulus was more effective when the aversive phenomenon was a toxin, and the audiovisual stimulus was more effective when the aversive phenomenon was a shock. Garcia and colleague claim that due to their biological characteristics, when rats feel ill after eating food, they have an innate tendency to associate these phenomena; however, similar associations are not formed with auditory and visual stimuli that were present when they ate the food. For the same reason, rats tend to associate painful events such as electric shocks with external audiovisual stimuli rather than gustatory stimuli. Rats may have a strong tendency to associate gustatory stimuli with feelings of illness; however, this is not always the case for other animals. In one study, rats and quail were made ill following ingestion of either flavored water, colored water, or water that was both colored and flavored, and the behavior of the animals was compared (Wilcoxon et al. 1971). As expected, the rats showed an aversion for the flavored water, but not the colored water. In contrast, the quail developed an aversion to both the flavored and colored water but showed greater aversion to the colored water. These findings were interpreted as relating to how rats and quail obtain food in their natural environments. Rats have excellent gustatory and olfactory senses; however, their visual sense is relatively weak, and they normally search for food at night. Quail obtain their food during the day, and they use their outstanding visual sense to acquire sustenance. These results can be interpreted as showing that the stimulus that is most important to an animal when it searches for food is also the most likely to be associated with feeling ill. These results show that there is no simple, general stimulus for associative learning and that the ease of learning differs depending on the evolutionary processes and biological factors of different animals.

1.8  Failure of Avoidance Learning in Rats Biological learning factors play an important role not only in classical conditioning but also in operant conditioning. Avoidance learning, which prevents impending threats and was believed to be a model for phobias, distances the organism from risks and fears; thus, it is a form of learning with adaptive value for survival. Therefore, most avoidance learning is easily accomplished. For instance, in 100

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trials of an avoidance learning task involving a reciprocal shuttle box, 80% of rats demonstrated the avoidance response of moving to the next compartment in response to a signal communicating danger, stopping the signal, and turning off the electric shock (Bolles 1969). In 40 trials of a task in which avoidance consisted of running on a running wheel, nearly 100% of rats demonstrated avoidance behavior. However, while rats can easily learn to press a lever to obtain food, they cannot learn to press a lever to avoid an electric shock. Numerous experiments over many years have failed to teach rats to press levers to carry out avoidance behavior. It is apparent that there are biological constraints even on learning of avoidance behavior with an impact upon survival. Bolles (1970) believed that the reason for this was that threatening stimuli and situations evoke strong innate unconditioned responses in animals. As seen in food acquisition and reproductive activity, each species has particular behaviors it uses to resolve biological issues. Likewise, he hypothesized that species-specific defense reactions (SSDR) evolved to handle threats. Nature provides few learning opportunities for handling threats such as predators, so each species must have specific, systematized defense reactions. This provides an explanation as to why defense reactions of rats (flight, freezing) are easily learned when the response involves avoidance, but learning is difficult when other responses are required. Rats do not press the lever to avoid an electric shock because they freeze. His hypothesis states that defensive behavior by animals lacks plasticity (Bolles 1979). This hypothesis is also in accordance with avoidance learning by invertebrate animals, whose behavioral repertoires lack plasticity.

1.9  Constraints on Avoidance Learning in Crayfish We trained crayfish, which are crustaceans, in one-way avoidance learning in a water tank partitioned into two compartments (Kawai et al. 2004a). The crayfish were placed in the departure area of the water tank, and a signal light was turned on; if the crayfish did not move to the next compartment within 10 s, they were given an electric shock. The interval between the signal and the start of the electric shock was 10 s; if the crayfish moved to the next compartment within this period, the signal stopped, and they were able to avoid the electric shock. A movement response within this 10-s period was defined as the avoidance response. If 10 s or more passed following the signal, a pulsing electric shock was delivered; if the crayfish moved to the next compartment during this period, they could stop the signal and the electric shock. This response was defined as the escape response. If the crayfish did not move to the next compartment within 35 s of the signal, the signal and the electric shock stopped automatically. Twenty such trials were conducted in a single session, and this was repeated for 32 sessions. The condition of the crayfish moving forward toward the next compartment (forward group) and the condition of moving backward (backward group) were compared. Because they have a tail-flip reflex (TF) in which they jump back quickly in dangerous situations, it was anticipated that the

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backward group would be advantageous. However, although escape reactions were initially seen in 100% of subjects in the backward group (Fig. 1.5), the avoidance response was not learned. In contrast, the forward group gradually learned the avoidance response. In the second experiment, after avoidance learning was established in the forward group, when the forward group and backward group conditions were changed, the backward group, which had previously exhibited only a flight response and not an avoidance response, exhibited an avoidance response by walking forward. However, the forward group, which had previously exhibited an avoidance response, did not exhibit any avoidance response whatsoever. Once again, the initial conditions were restored, and the same initial pattern was exhibited (Fig. 1.4). In other words, while the crayfish learned the relationship between the shining of the light and the electric shock through classical conditioning (signal→threat), their repertoire of responses for handling this was limited, and the TF occurred only after harm took place (Krasne and Woodsmall 1969), with the crayfish unable to act in advance of a perceived threat. In other words, the crayfish’s defensive reactions were also systematized and were not used to avoid threats in advance. This lack of plasticity may result from the fact that the nervous system of the crayfish processes stimuli in a decentralized fashion using numerous ganglia (Krasne 1969). The ganglion that controls TF (lateral giant fiber) is located in the tail region (Krasne 1973), so it may not receive orders from the forebrain, which controls learning.

Fig. 1.4  The mean avoidance performance of the two groups in the crayfish avoidance study (Kawai et al. 2004a)

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Fig. 1.5  The mean escape performance of the two groups in the crayfish avoidance study (Kawai et al. 2004a)

The crayfish, a crustacean, acquires signal-avoidance learning, but the achievement of avoidance differs depending upon the required reaction. This difference is caused not by constraints on learning but due to the constraint of lack of plasticity in execution. However, the defensive responses of rats have greater plasticity than those of invertebrates. Rats learned to run on a running wheel to avoid electric shocks but were unable to learn to stand on their hind legs (Bolles 1969). Thus, standing up may be further from the SSDRs than running. However, the standing response was observed in more than 50% of cases in the initial stages of training, but decreased with training. The SSDR theory, which does not acknowledge learning plasticity, cannot explain why rats cannot learn responses that occur in actual situations, such as the response of standing.

1.10  The Preparedness Theory of Phobias Studies of taste aversion learning and avoidance learning show that even basic learning of threat is constrained by biological factors of a species. We emphasize the idea that knowledge of the biological status and evolutionary history of a species is needed to understand this.

1.10  The Preparedness Theory of Phobias

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In response to extensive evidence of biological constraints on learning, Seligman (1970) advocated the preparedness theory of phobias. According to this theory, species are biologically prepared to learn to fear things and situations that threaten their livelihood based on the evolutionary history of the species. In other words, the theory holds that evolution shapes species in such a way that they can easily learn things that facilitate their survival. That is, depending on the species, there are genetic constraints on learnable responses. Due to natural selection, organisms are prepared to easily associate certain phenomena, while they are not prepared to associate other phenomena and may in fact be prepared in an opposite fashion. He referred to the CS-US associations that animals are innately prepared to form as “prepared associations” (for instance, the association between a taste and the feeling of being ill). Other potential associations include those whose formation is difficult even when paired stimuli are presented many times to the subject, referred to as “contraprepared associations” (for instance, tastes and shocks). Between these two are “unprepared associations” that are formed in the animal after stimuli are paired an adequate number of times, although the animal has no particular tendency toward that association. Regarding taste aversion learning, it is thought that because rats are evolutionarily prepared to associate taste with illness, with a single pairing of taste and illness, even young rats can be taught to acquire an aversion toward a taste (Klein et al. 1975). Seligman (1971) believed that it was appropriate to understand phobias as examples of biological preparedness for learning, because most phobias involve things and situations that have served as common threats to people throughout their evolutionary history. Seligman conducted an analysis of phobias and found that most of the millions of people who suffer from phobias are afraid of only a limited number of specific things and situations. Almost all phobias are related to animals such as snakes or to particular types of situations such as enclosed spaces, heights, etc. Hardly anyone suffers from phobias of common inanimate objects such as cups and desks. Thus, he was doubtful of Mowrer’s notion that phobias develop as a result of classical conditioning, a theory that was widely accepted. Another reason for this doubt is that people with phobias of snakes, heights, etc. have not necessarily experienced the corresponding US. In spite of the fact that they have never been bitten by a snake or fallen from a high place, they feel extreme fear toward these things. In other words, it seems that even people, who are capable of learning anything, have biological factors that make it easier to learn fear toward certain types of stimuli. The development of phobias is believed to be an example of the biological preparedness of people. People easily learn to fear things and situations that threatened our evolutionary ancestors. For instance, children easily develop fear of animals. Some human ancestors lived in arid regions. In such open spaces lacking trees or anything to obstruct their view, they could spot unfriendly people and threatening animals from far away. In these conditions, it was very dangerous for children to stray on their own away from adults, and they were highly vulnerable to attacks from animals, etc. Children, once attacked by frightening animals, likely did not forget the experience and must have avoided areas where such animals might appear, running to adults when they heard or caught sight of threatening animals. Such ­tendencies

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may have been passed down biologically and inherited by subsequent generations. Ultimately, people would easily learn to fear animals. The fact that most phobias of animals disappear after the age of 10 is in accordance with this theory. By this age, children learn where threats lie and how to protect themselves from attack. Seligman’s concept of preparedness is the polar opposite of the premise of equipotentiality. While the concept of preparedness resolves many contradictions, differences in preparedness explain only the speed and quantity of learning, not the establishment or non-establishment of associative learning. Rather than stating that it is impossible for rats to form associations between visual stimuli and feelings of illness, it predicts that more conditioning trials are needed than for the association between tastes and feelings of illness. The differences between preparedness, contrapreparedness, and unpreparedness for associations predict only quantitative differences, not qualitative differences. In fact, it has been shown that even rats can associate audiovisual stimuli with feelings of illness. According to Braveman (1977), many studies have shown that rats and other rodents can associate the appearance of food with feelings of illness, although numerous pairings are needed. It has also since been shown that rats can acquire an avoidance response by pressing a lever, a task which was at one time an extremely difficult challenge. When the experiment was configured so that pushing a lever opened a safe area, allowing the rats to escape or be guided to safety by the experimenters, the rats learned avoidance through their responses (Crawford and Masterson 1978). That is, when abstract reinforcement was the result of “fleeing,” the rats could perform avoidance behavior involving pressing a lever. The theory of preparedness states that the premise of equipotentiality is not valid and that animals including humans are innately predisposed to develop fear toward certain things. Seligman (1971) suggests that phobias (1) are acquired rapidly, (2) are unreasonable (cannot be suppressed despite mental comprehension), (3) are slow to extinguish, and (4) are more easily formed based on historical stimuli (snakes, etc.) than modern stimuli (electric faults). The range of things feared by people has been narrowed down; however, there has been no empirical examination of the evolutionary background against which these things came to be feared. Finally, it is worth noting that even Skinner acknowledged that phylogeny (genetics) can influence ontogenetic (learned) behavior (“Phylogeny [heredity] & ontogeny [learning] are friendly rivals and neither one always wins”, Skinner 1977, p. 1009). We come to any learning experience informed by our genetics, our phylogenetic environmental exposure, and our ontogenetic environmental exposure (but we may evaluate Skinner’s actual work than what he said; see also (Skinner 1966)). Fears are not simply learned, as Watson believed (Watson was not denying that a substantial part of behavior is inherited).

References

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References Bolles RC (1969) Avoidance and escape learning simultaneous acquisition of different responses. J Comp Physiol Psychol 68:355–358. https://doi.org/10.1037/h0027536 Bolles RC (1970) Species specific defense reactions and avoidance learning. Psychol Rev 77:32–48 Bolles RC (1979) Learning theory, 2nd edn. Holt, Rinehart & Winston, New York Braveman NS (1977) What studies on pre-exposure to pharmacological agents tell us about the nature of the aversion-inducing treatment. In: Barker LM, Best MR, Domjan MP (eds) Learning mechanisms in food selection. Baylor University Press, Waco, pp 511–530 Bregman EO (1934) An attempt to modify the emotional attitudes of infants by the conditioned response technique. J Genet Psychol 45:169–198. https://doi.org/10.1080/08856559.1934.10 534254 Bryner J (2011) Livescience.com, https://www.livescience.com/13434-phobias-fears-acrophobiaheights-agoraphobia-arachnophobia.html Crawford M, Masterson FA (1978) Components of the flight response can reinforce bar-­ press avoidance learning. J  Exp Psychol Anim Behav Process 4:144–151. https://doi. org/10.1037/0097-7403.4.2.144 DeCasper AJ, Fifer WP (1980) Of human bonding: newborns prefer their mother’s voices. Science 208:1174–1176. https://doi.org/10.1126/science.7375928 DeCasper AJ, Prescott PA (1984) Human newborns’ perception of male voices: preference, discrimination and reinforcing value. Dev Psychobiol 17:481–491. https://doi.org/10.1002/ dev.420170506 Emmelkamp PMG (1982) Phobic and obsessive-compulsive disorders. Springer, New  York. https://doi.org/10.1007/978-1-4684-7009-3 Etscorn F, Stephens R (1973) Establishment of conditioned taste aversions with a 24-hour CS-US interval. Physiol Psychol 1:251–253 Garcia J, Koelling RA (1966) Relation of cue to consequence in avoidance learning. Psychon Sci 4:123–124. https://doi.org/10.3758/BF03342209 Hodgson RJ, Rachman SJ (1972) The effects of contamination and washing in obsessional patients. Behav Res Ther 10:111–117. https://doi.org/10.1016/S0005-7967(72)80003-2 Jones MC (1924) The elimination of children’s fears. J Exp Psychol 7:382–390 Kawai N (2006) Cognitive abilities before birth: learning and long lasting memory in a chimpanzee fetus. In: Matsuzawa T, Tomonaga M, Tanaka M (eds) Cognitive development in chimpanzees. Springer, Tokyo, pp 48–63 Kawai N, Kono R, Sugimoto S (2004a) Avoidance learning in the crayfish (Procambarus clarkii) depends on the predatory imminence of the unconditioned stimulus: a behavior systems approach to learning in invertebrates. Behav Brain Res 150:229–237. https://doi.org/10.1016/ S0166-4328(03)00261-4 Kawai N, Morokuma S, Tomonaga M, Horimoto N, Tanaka M (2004b) Associative learning and memory in a chimpanzee fetus: learning and long lasting memory before birth. Dev Psychobiol 44:116–122. https://doi.org/10.1002/dev.10160 Klein SB, Domato GC, Hallstead C, Stephens I, Milulka PJ (1975) Acquisition of a conditioned aversion as a function of age and measurement technique. Physiol Psychol 3:379–384. https:// doi.org/10.3758/BF03326845 Krasne FB (1969) Excitation and habituation of the crayfish escape reflex: the depolarizing response in lateral giant fibers of the isolated abdomen. J Exp Biol 50:29–46 Krasne FB (1973) Learning in crustacea. In: Corming WC, Dyal JA, Willows AOD (eds) Invertebrate learning: vol. 2. Arthropods and gastropod mollusks. Plenum, New York, pp 49–130 Krasne FB, Woodsmall K (1969) Waning of the crayfish escape response as a result of repeated stimulation. Anim Behav 17:416–424. https://doi.org/10.1016/0003-3472(69)90141-9 Kuo ZY (1921) Giving up instincts in psychology. J  Philos 18:645–664. https://doi. org/10.2307/2939656

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Levine FM, Sandeen E (1985) Conceptualization in psychotherapy: the models approach. Routledge, London Lumsden CJ, Wilson EO (1982) Mind and linkage between genes and culture: a précis of genes, mind, and culture. Behav Brain Sci 5:1–7. https://doi.org/10.1017/S0140525X00010128 Morokuma S, Fukushima K, Kawai N, Tomonaga M, Satoh S, Nakano H (2004) Fetal habituation correlates with functional brain development. Behav Brain Res 153:459–463. https://doi. org/10.1016/j.bbr.2004.01.002 Mowrer OH (1947) On the dual nature of learning: a reinterpretation of “conditioning” and “problem-­solving”. Harv Educ Rev 17:102–148 Mowrer OH, Lamoreaux RR (1942) Avoidance conditioning and signal duration: a study of secondary motivation and reward. Psychol Monogr 54:269. https://doi.org/10.1037/h0093499 Pavlov IP (1928) Lectures on conditioned reflexes (trans: Gantt WH). Allen and Unwin, London Seligman MEP (1970) On the generality of the laws of learning. Psychol Rev 77:406–418. https:// doi.org/10.1037/h0029790 Seligman MEP (1971) Phobias and preparedness. Behav Ther 2:307–320. https://doi.org/10.1016/ S0005-7894(71)80064-3 Skinner BF (1966) The phylogeny and ontogeny of behavior. Science 153(3741):1205–1213. https://doi.org/10.1126/science.153.3741.1205 Skinner BF (1977) Herrnstein and the evolution of behaviorism. Am Psychol 32:1006–1012. https://doi.org/10.1037/0003-066X.32.12.1006 Thordike EL (1913) Educational psychology. Teachers College Press, New York Watson JB (1914) Behavior: an introduction to comparative psychology. Rinehart and Winston, New York Watson JB (1916) The place of the conditioned reflex in psychology. Psychol Rev 23:89–116 Watson JB (1925) Behaviorism. Norton, New York Watson JB (1930) Behaviorism, revised ed. W. W. Norton, New York Watson JB, Rayner R (1920) Conditioned emotional reactions. J Exp Psychol 3:1–14 Wilcoxon HC, Dragoin WB, Kral PA (1971) Illness-induced aversions in rat and quail: relative salience of visual and gustatory cues. Science 171:826–828. https://doi.org/10.1126/ science.171.3973.826 Wolpe J (1976) Theme and variation: a behavior therapy casebook (general psychology). Pergamon Press, New York

Chapter 2

Are Snakes Special in Human Fear Learning and Cognition? The Preparedness Theory of Phobia and the Fear Module Theory

Abstract  The preparedness theory of phobias hypothesizes that certain associations tend not to form because the particular combination of stimulus and response was not “prepared” in the process of evolution. In this chapter, I will discuss subsequent theoretical developments as well as related experimental results. Specifically, in this chapter I will give an overview of the fact that, with regard to biological fearrelated stimuli in classical conditioning of fear in people, (1) conditioning is quick, (2) there is high resistance to extinction, (3) associative learning is established even when the stimuli are subconscious, and (4) there are illusory correlations between stimuli and aversion. The preparedness theory of phobias explains why some objects are feared more than others. Although cars are more dangerous to pedestrians in modern society than dogs or snakes, people more often fear animals than cars because they are biologically “prepared” to fear animals. This raises the issue of innate factors in both recognition and emotional responses. Until recently, recognition process has been less studied. According to Seligman (1971), phobias have four distinct characteristics: rapid learning with regard to a particular subject, a fear reaction that the victim recognizes as unreasonable, belongingness (preparedness), and strong resistance to extinction. Rapid learning refers to the fact that only a small number of learning opportunities are needed for a particular stimulus to draw out the fear response. In some cases, one time may be sufficient. Unreasonable and unrecognized refers to the fact that the fear persists when its cause is present in spite of the patient’s understanding that there is no threat. Belongingness contradicts the equipotentiality premise, identifying that some conditioned and unconditioned stimulus combinations are easier to learn than others (McNally 1987). Strong resistance to extinction refers to the indefinite persistence of the phobia toward the CS despite the absence of the feared stimulus. This (strength of resistance to extinction) is important in the development of phobias and is in accordance with multiple clinical cases, demonstrating significant support for this theory, as will be described below. Studies have been performed to test the validity of the theory of preparedness with classical conditioning experiments using fear-related stimuli (pictures of snakes) and non-fear-related stimuli (pictures of flowers) as the CS (Öhman et al. © Springer Nature Singapore Pte Ltd. 2019 N. Kawai, The Fear of Snakes, The Science of the Mind, https://doi.org/10.1007/978-981-13-7530-9_2

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1985a). Autonomous responses conditioned by fear-related stimuli should resemble the characteristics of phobias (Schell et  al. 1991). The results have provided the most extensive support for the part of the theory concerning high resistance to the extinction of fear-related stimuli (Öhman 1979). The other characteristics posited by the theory have been supported by the results of some experiments, but not others. For instance, in a classical conditioning experiment with pictures of snakes/spiders or pictures of flowers/mushrooms as the CS, electric shocks as the US, and skin conductance response (SCR) as the CR, the first group of participants were given an electric shock after being shown a picture of a snake and were not given an electric shock after being shown a picture of a spider, thereby learning differential conditioning. In the second group, the roles of the snake and spider were reversed. The third group was given an electric shock after being shown a picture of a flower and was not given an electric shock after being shown a picture of a mushroom (in the fourth group, the roles of the flower and mushroom were reversed). As the results of such experiments generally indicate, a strong SCR was shown in response to the stimuli that signaled the electric shock, but not shown in response to the stimuli that did not signal an electric shock. The speed of differential conditioning was approximately the same in the case of the snake/spider and the flower/mushroom (Merckelbach et al. 1987, 1996; Öhman et al. 1976). These results do not support the preparedness hypothesis (McNally 1987). Öhman et al. assert that people more easily associate angry faces with aversive events (Dimberg and Öhman 1983; Öhman 1986). The reason is that, in the evolutionary history of humans, when an individual saw someone making an angry face at another person, it was likely that the angry person would then cause harm to someone, possibly oneself. As a result, people came to be prepared for a defensive reaction to angry faces. Whether or not people have a preparedness for angry faces was tested using the same procedure as the above-described differential experiment with the snake/spider. A picture of an angry face was used instead of the picture of the snake, and a picture of a happy face or a neutral face was used instead of the picture of the flower. In these studies, results have both supported and contradicted the preparedness theory (Dimberg and Öhman 1983, 1996; Öhman 1986). McNally (1987) states that although learning theory based on animal conditioning studies is useful in understanding phobias, the range of phenomena it can explain is highly limited. He points out that in primates, preparedness for fear changes throughout the course of development. For instance, because young monkeys are vulnerable to attacks by predators, they fear predatory animals; however, in adolescence disputes over group ranking begin, so “preparation” to associate fear with social phenomena such as angry faces takes place. Ultimately, the preparedness theory cannot determine whether it is preparedness or fear that encourages the development of particular phobias. Various criticisms have been leveled at the notion of equating human phobias with conditioning of animals in the laboratory (McNally 1987; Packer et al. 1991). Ultimately, evidence for the preparedness of people for fear-related stimuli such as snakes and spiders is inconsistent, and some believe that biological preparedness is not necessary (Davey 1992, 1995). Below, we will examine three theories proposed after the theory of preparedness that explain why people fear specific things.

2.1  The Neo-conditioning Model of Fear

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2.1  The Neo-conditioning Model of Fear The biggest weakness of the theory of preparedness is the assumption that for a person or animal to fear something, at least one experience of fear is required. However, people sometimes feel fear based on information, for instance, car accident statistics, without having a direct experience of the feared object themselves. Children begin to fear monsters and ghosts at a particular age even without having seen them; can the theory of preparedness explain this? Also, there are people who become afraid of traveling on airplanes after seeing airplane crashes on TV or who become afraid of snakes after seeing someone else showing fear toward them. These are clear examples of learning through language and observation. Also, not everyone who experiences fear develops a phobia. In one study (Di Nardo et al. 1988a, b), although two thirds of people with a phobia of dogs had had a traumatic experience such as being bitten by one, the same proportion of people did not become afraid of dogs after undergoing the same experience. Some phobias are acquired through direct conditioning; however, it is clear that they can also be acquired through other processes. Rachman (1977) believed that fear was acquired through three distinct processes: (1) direct learning through classical conditioning, (2) indirect learning through observation, and (3) verbally transmitted information. This theory received preliminary support from numerous studies (King et  al. 1998; Öst and Hugdahl 1983). For instance, there are reports of cases in which not only people but also animals have acquired fear through observational learning rather than direct CS-US conditioning. Rhesus monkeys raised in the laboratory often have no fear of snakes. By observing the fear of snakes among monkeys raised in the wild, these monkeys also learned to fear both toy and real snakes, a phenomenon that persisted for more than 3 months (Mineka et al. 1980). In addition, when some monkeys were shown that a model monkey was not afraid of snakes, this prevented the monkeys from acquiring fear even when later shown a video of the model monkey exhibiting fear of snakes (Mineka and Cook 1986; Mineka et al. 1984). When human infants observed their mothers looking at toy snakes, spiders, and flowers with negative facial expressions, the infants later came to fear these toys (Dubi et  al. 2008). Similarly, in an experiment in which children aged 7–9 were shown pictures of unfamiliar animals paired with pictures of smiling faces or fearful faces, they came to fear the animals paired with the fearful faces more than those paired with the smiling faces (Askew and Field 2007). There are few examples of adults acquiring fear through observational learning; however, some have been reported (Askew and Field 2008). It is possible that direct conditioning was established in these studies, but it is obvious that fear is learned through observation. In addition, fear can be acquired through hearsay, although this is limited to people. Children learn to avoid various stimuli by hearing negative verbal information (Field 2006; Muris and Field 2010). In one experiment, children were given positive, negative, or neutral information regarding three types of animals. The strength of the fear self-reported by the children and their latency in approaching

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these animals were greatest for animals for which they received negative information (Field et  al. 2001). The same effect was obtained in an experiment using a physiological index of increase in heart rate (Field and Schorah 2007). In spite of the presence of experimental results supporting this theory, its validity has been criticized on the basis of reports of contradictory phenomena. For instance, conditioning phenomena have sometimes occurred after the development of phobias. There was also a case in which fear did not develop despite numerous conditioning experiences (Di Nardo et al. 1988a, b). In addition, not everything can be feared based on observation. Of significance, it has been shown that acquisition of fear through observational learning is selective. Mineka et  al. showed a group of monkeys raised in the laboratory a video of wild monkeys showing fear toward flowers (non-fear-related stimuli), while a second group was shown a video of wild monkeys exhibiting fear toward snakes. The results showed that only the monkeys in the group that saw the snake video acquired fear (Cook and Mineka 1990). In addition, this theory does not explain why many people are afraid of particular situations (Hofmann et al. 1995) and things such as heights (Williams et al. 1985) and snakes. Menzies and colleague were the first to claim that there is another process for acquiring fears related to evolution (heights and water) in addition to associative learning (Menzies 1995; Menzies and Clarke 1993a, b). They added a fourth process related to evolutionary threats to Rachman’s three processes (Poulton and Menzies 2002). Based on this notion, experiences of particular negative phenomena are not necessary, while learning theory and preparedness theory hypothesize that a minimum degree of negative experience is necessary for fear to be felt (Poulton and Menzies 2002; Menzies and Clarke 1995). Menzies asked parents of children with water phobias when the children began to fear water, and they answered that the children had this fear for as long as they could remember (Menzies and Clarke 1993a). In addition, there are cases in which an individual feels fear despite never having experienced a frightening situation; however, in all cases these are fears related to evolution (water, heights, spiders) (Poulton and Menzies 2002). In contrast, the sources of fears toward dentists, etc. are clearly remembered.

2.2  S  electivity of Stimuli in Classical Conditioning of Fear in Humans In a series of classical conditioning experiments using the index of electrodermal response, Öhman and others tested whether fear was associated easily with stimuli with the potential to induce phobias (such as snakes and spiders) (Öhman et  al. 1976). In this way, phobias may be examples of learning in response to biologically prepared stimuli. In these studies, mild electric shocks were used as the US, and stimuli deemed phobia-related (pictures of snakes, spiders, etc.) as well as stimuli deemed non-phobia-related (pictures of flowers, mushrooms, etc.) were used as the CS in experiments involving participants who did not have phobias; the extent of

2.3  The Encapsulation Theory of Fear

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fear conditioning was evaluated on the basis of differences in electrodermal response. As a general result, conditioning was established more quickly for fear-­ related stimuli than non-fear-related stimuli, and resistance to extinction was greater. However, resistance to extinction was only greater when the US was an electric shock; when the US was a loud sound, the CS stimulus did not cause a difference in resistance to extinction (Öhman et al. 1976). In a similar experiment using an index of changes in heart rate, although an increase in heart rate (fear index) was seen in response to fear-related stimuli in the acquisition of conditioning, a decrease (caution toward the stimulus) was seen for flowers and mushrooms (Cook et  al. 1986). In this study, there was greater conditioning for snakes than for guns, which were paired with the US of a loud sound. Generally, in classical conditioning of fear, the extent of the conditioning is determined by the strength (Annau and Kamin 1961) of the electric shock and its duration (Kawai and Imada 1996). Selective association between snakes and aversive stimuli show that there is greater selectivity of fear in response to evolutionary threats than in conditioning toward modern-day threats.

2.3  The Encapsulation Theory of Fear Based on this series of studies, Öhman and Mineka (2001) proposed an evolutionary model of fear learning. The theory is based on the central concept of fear modules formed in the process of evolution and has four unique characteristics: (1) selectivity with regard to input (fear modules are sensitive to stimuli correlated to threats in the process of prior evolution), (2) automaticity (evolutionary threat-related stimuli activate the relevant module even when not consciously perceived), (3) the encapsulation of the fear module (greatly independent from conscious awareness), and (4) specialized neural circuits (controlled by specialized neural circuits formed through evolution). This theory explains that phobias cannot be consciously controlled even when the person has reasonable proof that they are safe due to the encapsulation of the fear module. This theory can be distinguished from previous theories in that it takes a neurobiological view of fear conditioning. This theory proposes two levels of fear learning: (1) basic associative learning observed as an automatic emotional reaction controlled by the amygdala and (2) incidental learning at the non-emotional cognitive level controlled by the hippocampus. Fear conditioning in humans using fearrelated stimuli activates both levels; however, fear conditioning using non-fear-­related stimuli only takes place at the non-emotional cognitive level. The type of fear that is ordinarily conditioned in the laboratory involves non-prepared (hippocampal) learning using stimuli unrelated to survival (buzzer sounds, etc.); therefore, it does not provide a good framework for understanding human phobias.

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2.4  U  nconscious Control of Responses to Fear-Related Stimuli Presented Through Backward Masking An experiment was conducted using the backward masking procedure to test whether fear responses involve conscious control (participation of the neocortex). With this method, a masking stimulus is presented immediately following a momentary visual stimulus, so the initial visual stimulus cannot be consciously perceived. The backward masking prevents visual processing in the primary visual cortex; thus, a response to a backward masked stimulus must rely on the activity of circuits not cortically mediated. This circuit that does not involve the cortex, which mediates visual awareness of stimuli, allows fear circuits to directly access visual stimuli. In one study, a photograph of snake, spider, flower, or mushroom was presented for a split second (30 ms), and all stimuli were followed by random dot noise masking stimuli (Öhman and Soares 1994). The participants were unable to recognize what the stimulus was; however, participants who were afraid of snakes developed SCR in response only to photographs of snakes, and participants who were afraid of spiders did the same in response only to photographs of spiders. This study suggests that threatening images are processed rapidly and subcortically. Similar results were observed in the conditioning study among participants who did not fear snakes (Öhman and Soares 1993). They were conditioned with electric shocks and a picture of snake that was not masked in the training phase. In the test without a US, they exhibited conditioned responses to a photograph of snake, which was presented through backward masking, while they did not recognize these images at a conscious level. In another group, conditioned with a neutral stimulus, which elicited conditioned responses in the training phase; however, the conditioning effect disappeared in a backward masking trial of the test phase. In a subsequent study, when a CS paired with an electric shock was backward masked, conditioning was established when the CS was a snake or a spider, but not when the CS was a flower or mushroom (Öhman and Soares 1998). That is, fear responses using SCR as an index are learned even with backward masking, which prevents access to visual cortical processing, when the CS is a fear-related stimulus, but not when the CS is a non-fear-related stimulus (Esteves et al. 1994). This suggests that the earliest mammals processed certain stimuli using primitive (non-cortically mediated) neural circuits predating the evolution of the neocortex. I will return this point in Chap. 8, discussing the possibilities of quick cortical processing. Strong conditioning between electric shocks and threat-related stimuli was also obtained in experiments using angry expressions (Öhman and Dimberg 1978). These stimuli were presented subconsciously, providing further proof that there is almost no involvement of conscious processing (i.e., Esteves et al. 1994; Esteves and Öhman 1993; Öhman and Soares 1993).

2.6  Resistance to Extinction

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2.5  I llusory Correlations Between Fear-Related Stimuli and Aversive Events In classical conditioning of humans and animals, the conditioned response increases in almost exact proportion to the rate of the presentation of the US at the time the CS is presented (Papini and Bitterman 1990; Rescorla 1968). However, when the CS is a biological fear-related stimulus, the rate of presentation of the US is greatly overestimated. This is so-called illusory correlations. For instance, when photographs of fear-related stimuli (snakes) are paired with electric shocks for participants who fear snakes, they perceive that these are paired more often than photographs of control stimuli (flowers, mushrooms, etc.). In an experiment with female participants, one third were presented with an electric shock following the CS (for the other two thirds, sound was presented, and nothing was presented as the US, respectively) (Tomarken et al. 1989). The experiment investigated whether participants could accurately predict the rate of presentation of the electric shock when the CS was a flower, a mushroom, and a snake. The results showed that the flower and mushroom were estimated with almost perfect accuracy; however, for the photograph of the snake, an illusory correlation was observed wherein the electric shock was thought to be presented more often than it actually was. Such an illusory correlation was not observed for guns and knives. An illusory correlation was also not observed when photographs of broken electronic goods were paired with electric shocks, again suggesting that stimuli relating to threats in the process of evolution (snakes) are unique (Tomarken et al. 1995). In another experiment (Kennedy et al. 1997), it was expected that when participants were asked to evaluate the extent to which electric shocks would accompany biological (snakes, spiders) or technical (guns, knives) fear-related stimuli prior to the experiment, they would indicate that the electric shock would be presented to the same extent for both as expected. However, after these stimuli were paired randomly with the electric shock in the actual experiment, an illusory correlation was seen only for biological fear-related stimuli (Amin and Lovibond 1997). There was a disparity between the extent of the threat perceived in advance and the fear that was actually induced. It appeared that biological fear-related stimuli (snakes and spiders) have a link to threats and aversion that cannot easily be changed through experience.

2.6  Resistance to Extinction As the extent of a conditioned response is determined by the strength of the US, studies have investigated resistance to extinction to determine how strongly conditioning has formed. Extinction refers to the phenomenon (or procedure) of the CS-US association being weakened by CS presentations without US and the CR ultimately ceasing to be observed. Resistance to extinction refers to the power to invoke the CR once the US stops being presented after the CS (extinction). After snakes and spiders or flowers and mushrooms were paired with an electric shock, and these stimuli

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started to invoke the CR, trials wherein only the CS was presented without the US were repeated. Although none of the stimuli signaled the arrival of the US, resistance to extinction was stronger for snakes/spiders than for flowers/mushrooms (Öhman et al. 1985a, b; Schell et al. 1991). However, it is not the case that all threat-related stimuli are similarly resistant to extinction. For instance, for broken electronic goods suggesting an electric short, for guns, etc. (Cook et al. 1986), the results did not indicate high resistance to extinction (Hugdahl and Kärker 1981). That is, here as well, it is apparent that there is high resistance to extinction only for stimuli that were related to threats in the process of evolution (Dawson et al. 1986). Fear responses invoked experimentally weaken when participants are informed that the US will no longer be presented following the CS (Grings 1973; Foa and Kozak 1986). However, although fear responses toward flowers and mushrooms extinguish more quickly when participants are verbally instructed, “you will not be shocked anymore,” verbal instructions do not encourage extinction when the CS is a snake or spider (Hugdahl and Öhman 1977).

2.7  The Development of the Fear Module Theory In Öhman and Mineka’s original version of the fear module theory, things that repeatedly threatened humans in the process of evolution were regarded as biological fear-related stimuli. Specifically, these were snakes, spiders, and the angry face of an individual of the same species. In another study several years later, however, Öhman et  al. distinguished between “interspecific fears” and “intraspecific fears” and distinguished between fear of snakes/spiders and fear of angry faces (Öhman et al. 2012). This is based on Öhman et al. (1985a, b)’s previous classification system of fears of other people and fears of animals. This classification is also in accordance with the two main categories of human phobias (American Psychiatric Association 2013). These are social phobias (fear of high-status people, meeting unknown people, being seen by other people, etc.) and phobias of animals (snakes, spiders, birds, dogs, cats, etc.). They further developed their theory, stating that these fears of two types of living things originated from two behavior systems that evolved separately: a social submission system and a predatory defense system. They hypothesized that the core stimuli of the former system are faces of the same species and that the core stimuli of the latter system are snakes. In fact, the faces of others and pictures of snakes are emotional stimuli, and people automatically take notice of them (Öhman et al. 2012) as monkeys do (Kawai et al. 2016; Kawai and Koda 2016). Two years after their initial paper, they published a paper that made small but important changes to their theory (Öhman and Mineka 2003). In addition, they changed their claim to state that snakes were more of a threat than spiders. In their new review article, they described the presence of snakes as the main predators throughout the evolution of primates, the uniqueness of fear learning toward snakes, the fact that conscious cognitive processes do not affect reactions toward snakes, and the fact that there is an illusory correlation between snakes and aversive stimuli. They also added the important point that people automatically take notice of

2.9  The Evolution of Fear Modules Related to Snakes

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photographs of snakes. This paper may appear essentially the same as the previous paper; however, it differs greatly from a theory based only on learning on two points: the limitation of biological threats to snakes and the mention of people automatically taking notice of them. This new, slightly modified theory has no direct relationship to the snake detection theory (SDT) discussed in detail in the following chapter; however, it can be seen as being a mediator of prior theories regarding biological constrictions on learning and SDT (Isbell 2006). The main changes in their theory are as follows.

2.8  Most Primates Fear Snakes Öhman et al. discuss data from an older study (Agras et al. 1969) involving an interview survey of people living in New England: snakes were the one thing feared most by 38% of women and 12% of men. Wild monkeys also fear snakes. According to a field survey (King 1997), 11 genera of primates exhibited fear-related reactions in all observed cases when facing large snakes. However, among monkeys raised in the laboratory, fear responses are easily habituated, perhaps because they are in fact not afraid of real or toy snakes (Nelson et al. 2003). However, even monkeys raised in the laboratory learn vicariously when they see monkeys from the wild showing fear of snakes (Mineka et al. 1980). Based on the above, they believe that there is adaptive value to primates fearing “large snakes” (Cook and Mineka 1991).

2.9  The Evolution of Fear Modules Related to Snakes They claim that it is particularly easy for people and primates to associate fear with snakes (or spiders) (Öhman and Mineka 2001). According to Burghardt et al. (2009), which reviewed some of the literature about snake fear from a herpetological perspective, this view (Öhman and Mineka 2001) seemed to discount instinctive recognition of snakes and emphasized conditioning, including social nature, while Wilson (1984) asserted that there is a genetic foundation for the attitudes that humans end up with and for the fact that humans can acquire these attitudes very quickly. To explain quick learning, Öhman and Mineka hypothesize that there is a fear module that is relatively independent from the behavioral, mental, and nervous systems. This module is believed to have evolved to assist mammals in defending against threats such as snakes. They believe that this module is selective toward threats to survival, activated automatically in response to them, resistant to the influence of higher cognition, and reliant upon specialized neural circuits. They hypothesize that rather than evolving among human ancestors in response to the survival threat of snakes, this specialized behavior module initially evolved in response to reptiles, which were constant threats to mammals. However, it is possible that among reptiles, snakes in particular represent typical fear module-­

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activating stimuli. In spite of this, their theory does not state that the human brain has specialized modules for automatically generating fear toward snakes. Rather, it holds that the basic design of fear modules was created among early mammals. As mammals began to evolve further into other mammals, this design was revised, refined, and specialized in accordance with the ecological factors of the individual species. In fact, there are even some mammals that hunt snakes. Although their theory was at least partly based on the Mineka’s lab experiments mentioned above, their experiments were criticized (Burghardt and Bowers 2017), because these studies were done in highly artificial settings with only a simple measure of behavior such as avoiding the snake or not taking food close to it (Burghardt et al. 2009). Their theory hypothesizes that biological fear-related stimuli are processed by the amygdala without cortical mediation. This is supported by evidence (mainly backward masking); however, the theory does not state in detail how specialization for snakes arose in the evolution of primates and humans. The theory explains/predicts that when classical fear conditioning uses this fear module, conditioning is rapid, strong, and formed unconsciously; however, the evolutionary background of the fear module and the specific neural circuits remain black boxes. The evolutionary background of the sensitive responses of humans and primates to snakes as well as the neural structures that support this system would be described in detail by SDT (Isbell 2006). The next chapter will give an overview of SDT, the neural structures it hypothesizes, and the results of behavioral experiments that support these.

References Agras S, Sylvester D, Oliveau D (1969) The epidemiology of common fears and phobia. Compr Psychiatry 10:151–156. https://doi.org/10.1016/0010-440X(69)90022-4 American Psychiatric Association (2013) Diagnostic and statistical manual disorders, 5th edn. American Psychiatric Association Publication, Washington, DC Amin JM, Lovibond PF (1997) Dissociations between covariation bias and expectancy bias for fear-relevant stimuli. Cognit Emot 11:273–289. https://doi.org/10.1080/026999397379926 Annau Z, Kamin LJ (1961) The conditioned emotional response as a function of intensity of the US. J Comp Physiol Psychol 54:428–432. https://doi.org/10.1037/h0042199 Askew C, Field AP (2007) Vicarious learning and the development of fears in childhood. Behav Res Ther 45:2616–2627. https://doi.org/10.1016/j.brat.2007.06.008 Askew C, Field AP (2008) The vicarious learning pathway to fear 40 years on. Clin Psychol Rev 28:1249–1265. https://doi.org/10.1016/j.cpr.2008.05.003 Burghardt GM, Bowers RI (2017) From instinct to behavior systems: an integrated approach to ethological psychology. In Call J, Burghardt GM, Pepperberg IM, Snowdon CT, Zentall T (eds) APA handbooks in psychology. APA handbook of comparative psychology: basic concepts, methods, neural substrate, and behavior. American Psychological Association, Washington, DC, pp 333–364. https://doi.org/10.1037/0000011-017 Burghardt GM, Murphy JB, Chiszar D, Hutchins M (2009) Combating ohpiophobia. In Mullin SJ, Seigel RA (eds) Snakes: ecology and conservation. Comstock Publishing Associates, Ithaca, pp 262–290

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Isbell LA (2006) Snakes as agents of evolutionary change in primate brains. J Hum Evol 51:1–35. https://doi.org/10.1016/j.jhevol.2005.12.012 Kawai N, Imada H (1996) Between- and within-subjects effects of US duration on conditioned suppression in rats: contrast makes otherwise unnoticed duration-dimension stand out. Learn Motiv 27:92–111. https://doi.org/10.1006/lmot.1996.0006 Kawai N, Koda H (2016) Japanese monkeys (Macaca fuscata) quickly detect snakes but not spiders: Evolutionary origins of fearrelevant animals. J Comp Psychol 130(3):299–303. doi: https://doi.org/10.1037/com0000032 Kawai N, Kubo K, Masataka N, Hayakawa S (2016) Conserved evolutionary history for quick detection of threatening faces. Anim Cogn 19:655–660. doi: https://doi.org/10.1007/ s10071-015-0949-y Kennedy SJ, Rapee RM, Mazurski EJ (1997) Covariation bias for phylogenetic versus ontogenetic fear-relevant stimuli. Behv Res There 35(5):415–422 King GE (1997) The attentional basis for primate responses to snakes. Paper presented at the annual meeting of the American Society of Primatologists, San Diego. (Revised for style and format, May, 2013) King NJ, Eleonora G, Ollendick TH (1998) Etiology of childhood phobias: current status of Rachman’s three pathways theory. Behav Res Ther 36:297–309. https://doi.org/10.1016/ S0005-7967(98)00015-1 McNally RJ (1987) Preparedness and phobias: a review. Psychol Bull 101:283–303. https://doi. org/10.1037/0033-2909.101.2.283 Menzies RG (1995) The etiology of phobias: a nonassociative account. Clin Psychol Rev 15:23– 48. https://doi.org/10.1016/0272-7358(94)00039-5 Menzies RG, Clarke JC (1993a) The etiology of childhood water phobia. Behav Res Ther 31:499– 501. https://doi.org/10.1016/0005-7967(93)90131-D Menzies RG, Clarke JC (1993b) The etiology of fear of heights and its relationship to severity and individual response patterns. Behav Res Ther 31:355–365 Menzies RG, Clarke JC (1995) The etiology of phobias: a nonassociative account. Clin Psychol Rev 15:23–48 Merckelbach H, Van den Hout MA, Van der Molen GM (1987) Fear of animals: correlations between fear ratings and perceived characteristics. Psychol Rep 60(3, Pt 2):1203–1209. https:// doi.org/10.2466/pr0.1987.60.3c.1203 Merckelbach H, Muris P, Schouten E (1996) Pathways to fear in spider phobic children. Behav Res Ther 34:935–938. https://doi.org/10.1016/S0005-7967(96)00052-6 Mineka S, Cook M (1986) Immunization against the observational conditioning of snake fear in rhesus monkeys. J Abnorm Psychol 95:307–318. https://doi.org/10.1037/0021-843X.95.4.307 Mineka S, Keir R, Price V (1980) Fear of snakes in wild- and laboratory-reared rhesus monkeys (Macaca mulatta). Anim Learn Behav 8:653–663. https://doi.org/10.3758/BF03197783 Mineka S, Davidson M, Cook M, Keir R (1984) Observational conditioning of snake fear in rhesus monkeys. J Abnorm Psychol 93:355–372. https://doi.org/10.1037/0021-843X.93.4.355 Muris P, Field AP (2010) The role of verbal threat information in the development of childhood fear. “Beware the Jabberwock!”. Clin Child Fam Psychol Rev 13:129–150. https://doi. org/10.1007/s10567-010-0064-1 Nelson EE, Shelton SE, Kalin NH (2003) Individual differences in the responses of naïve rhesus monkeys to snakes. Emotion 3:3–11. https://doi.org/10.1037/1528-3542.3.1.3 Öhman A (1979) Fear relevance, autonomic conditioning, and phobias: a laboratory model. In: Sjödén P-O, Bates S, Dockens WS III (eds) Trends in behavior therupy. Academic Press, New York, pp 107–134 Öhman A (1986) Face the beast and fear the face: animal and social fears as prototypes for evolutionary analyses of emotion. Psychophysiology 23:123–145. https://doi. org/10.1111/j.1469-8986.1986.tb00608.x

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Öhman A, Dimberg U (1978) Facial expressions as conditioned stimuli for electrodermal responses: a case of “preparedness”? J  Pers Soc Psychol 36:1251–1258. https://doi. org/10.1037/0022-3514.36.11.1251 Öhman A, Mineka S (2001) Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol Rev 108:483–522. https://doi.org/10.1037//0033-295X.108.3.483 Öhman A, Mineka S (2003) The malicious serpent: snakes as a prototypical stimulus for an evolved module of fear. Curr Dir Psychol Sci 12:5–9 Öhman A, Soares JJF (1993) On the automatic nature of phobic fear: conditioned electrodermal responses to masked fear-relevant stimuli. J  Abnorm Psychol 102:121–132. https://doi. org/10.1037/0021-843X.102.1.121 Öhman A, Soares JJF (1994) “Unconscious anxiety”: phobic responses to masked stimuli. J Abnorm Psychol 103:231–240. https://doi.org/10.1037//0021-843X.103.2.231 Öhman A, Soares JJF (1998) Emotional conditioning to masked stimuli: expectancies for aversive outcomes following nonrecognized fear-irrelevant stimuli. J Exp Psychol Gen 127:69–82 Öhman A, Fredrikson M, Hugdahl K, Rimmö PA (1976) The premise of equipotentiality in human classical conditioning: conditioned electrodermal responses to potentially phobic stimuli. J Exp Psychol Gen 105:313–337. https://doi.org/10.1037/0096-3445.105.4.313 Öhman A, Dimberg U, Öst L-G (1985a) Animal and social phobias: biological constraints on learned fear responses. In: Reiss S, Bootzin RR (eds) Theoretical issues in behavior therapy. Academic, New York, pp 123–178 Öhman A, Dimberg U, Ost L-G (1985b) Animal and social phobias: biological constraints on learned fear responses. In: Reiss S, Bootzin RR (eds) Theoretical issues in behavior therapy. Academic, New York, pp 123–178 Öhman A, Soares SC, Juth P, Lindström B, Esteves F (2012) Evolutionary derived modulations of attention to two common fear stimuli: serpents and hostile humans. J Cogn Psychol 24:17–32. https://doi.org/10.1080/20445911.2011.629603 Öst L-G, Hugdahl K (1983) Acquisition of agoraphobia, mode of onset and anxiety response patterns. Behav Res Ther 21:623–631. https://doi.org/10.1016/0005-7967(83)90080-3 Packer JS, Clark BM, Bond NW, Siddle DA (1991) Conditioning with facial expression of emotion: a comparison of aversive and non-aversive unconditioned stimuli. J Psychophysiol 5:79–88 Papini MR, Bitterman ME (1990) The role of contingency in classical conditioning. Psychol Rev 97:396–403 Poulton R, Menzies RG (2002) Non-associative fear acquisition: a review of the evidence from retrospective and longitudinal research. Behav Res Ther 40:127–149. https://doi.org/10.1016/ S0005-7967(01)00045-6 Rachman S (1977) The conditioning theory of fear acquisition: a critical examination. Behav Res Ther 15:375–387. https://doi.org/10.1016/0005-7967(77)90041-9 Rescorla RA (1968) Probability of shock in the presence and absence of CS in fear conditioning. J Comp Physiol Psychol 66:1–5. https://doi.org/10.1037/h0025984 Schell AM, Dawson ME, Marinkovic K (1991) Effects of potentially phobic conditioned stimuli on retention, reconditioning, and extinction of the conditioned skin conductance response. Psychophysiology 28:140–153. https://doi.org/10.1111/j.1469-8986.1991.tb00403.x Seligman MEP (1971) Phobias and preparedness. Behav Ther 2:307–320. https://doi.org/10.1016/ S0005-7894(71)80064-3 Tomarken AJ, Mineka S, Cook M (1989) Fear-relevant selective associations and covariation bias. J Abnorm Psychol 98:381–394. https://doi.org/10.1037/0021-843X.98.4.381 Tomarken AJ, Sutton SK, Mineka S (1995) Fear-relevant illusory correlations: what types of associations promote judgmental bias? J  Abnorm Psychol 104:312–326. https://doi. org/10.1037/0021-843X.104.2.312 Wilson EO (1984) Biophilia. Harvard University Press Williams SL, Turner SM, Peer DF (1985) Guided mastery and performance desensitization treatment for severe acrophobia. J  Consult Clin Psychol 53:237–247. https://doi. org/10.1037/0022-006X.53.2.237

Chapter 3

The Underlying Neuronal Circuits of Fear Learning and the Snake Detection Theory (SDT)

Abstract  In previous chapters, I described the behavioral aspects of fear learning in humans and animals. Öhman et  al. hypothesized (Öhman and Mineka 2001, 2003) that the neural mechanisms that process sources of fear (e.g., snakes and spiders), commonly shared among mammals, including humans, are independently maintained in the brain. The snake detection theory (SDT) proposes that the primate brain expanded to be able to detect snakes efficiently during the course of evolution (Isbell 2006, 2009); furthermore, it proposes the existence of an independent fear module in the brain and identifies its neural mechanism in more detail. This chapter will provide a review of the SDT. Before embarking on this review, we will first describe the neural mechanisms underlying general fear learning in relation to typical examples of Pavlovian conditioning through exposure to light and sound as conditioned stimuli (CS).

3.1  The Amygdala as the Brain’s Fear Center Comparative affective neuroscience studies suggested that primary-process emotional feelings are organized withing primitive subcortical regions of the mammalian brain that are anatomically, neurochemically, and functionally homologous in all mammals (Panksepp 1982). Panksepp posited for major emotional systems that were coded into the genome in rough form (as primary brain processes): seeking, rage, fear, lust, care, grief (panic), and play. Neuroscience research supports the existence of these primary-process (basic) emotional sysytems that inform animals how they are faring in the quest to survive. They are ancestral tools for survival, which are refined by basic learning mechanisms (secondary processes) (Panksepp 2010). A large number of fear conditioning studies have been conducted using rats to understand how fear is processed by the brain. LeDoux’s research conducted over more than 30 years greatly contributed to the understanding of the neural circuits related to fear conditioning (LeDoux 1996, 2002, 2015). Here, I introduce LeDoux’s findings to characterize the neural mechanisms responsible for the freezing response exhibited by rats as a result of exposure to CS (sound stimuli) predicting the delivery of US (electric shock). © Springer Nature Singapore Pte Ltd. 2019 N. Kawai, The Fear of Snakes, The Science of the Mind, https://doi.org/10.1007/978-981-13-7530-9_3

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Auditory stimuli are converted into nerve signals by the organ of Corti in the inner ear and are transmitted to the auditory nucleus (cochlear nucleus) of the brain stem through the auditory nerve. From here, the transmission pathway enters the contralateral side of the brain and is transmitted to the inferior colliculus of the midbrain. Further, the transmission pathway reaches the corpus geniculatum mediale (auditory thalamus), which serves as the nexus of auditory relays from the inferior colliculus to the thalamus and is then transmitted to the auditory cortex. Which areas in this series of pathways play decisive roles in fear conditioning? To investigate this, LeDoux began by damaging the auditory cortices of rats and confirmed whether fear conditioning was established (LeDoux et al. 1984). It was found that there was no influence on freezing response conditioning or changes in blood pressure even when obstructing the auditory cortex. In other words, it was found that the auditory cortex is not necessarily required for the formation of aural fear conditioning. Further, when the auditory thalamus and the auditory center were independently damaged, fear conditioning was not established in either case. Specifically, for fear conditioning to be established in response to an auditory stimulus, transmission of the auditory information along the auditory pathway from the inner ear to the thalamus is required; however, culmination at the auditory cortex is not necessary. Subsequent studies investigated the projection pathway by injecting a neural tracer into the auditory thalamus, whereupon auditory information was projected to four locations other than the auditory cortex. One of these locations was the amygdala. The amygdala is an almond-shaped nucleus located inside the temporal lobe interior and has long been known as the “fear center of the brain.” Klüver and Bucy (1937) reported that when the temporal lobes of both the brain hemispheres (including the amygdala) in monkeys were dissected out, the monkeys no longer exhibited uncomfortable emotions and no longer appeared to fear snakes. It has been reported that in rare cases, patients presenting with damage to the amygdala cannot recognize the sensation of fear or sources of fear, known as Klüver-Bucy syndrome (Bechara et al. 1995; Adolphs et al. 1995; Hamann and Adolphs 1999).

3.2  The Amygdaloid Complex The amygdala is a single large nucleus but consists of more than ten sub-nuclei (Sah et  al. 2003). These nuclei extend to various different brain regions. Which sub-­ nuclei play important roles in fear conditioning? When the central nucleus of the amygdala in rabbits was damaged, fear conditioning determined by changes in the heart rate was not observed (Kapp et al. 1984). The central nucleus communicates with regions of the brain stem that regulate changes in the heart rate and other autonomic nervous system reactions. Thus, since stimulation of the central nucleus of the amygdala leads to such cardiovascular and autonomic nervous system changes, it was hypothesized that the central nucleus is an important relay point for transferring fear stimuli to the autonomic nervous system. Subsequent studies found that the central nuclei are involved with a wide variety of different responses to

3.3 The “Low Road” and “High Road” to the Amygdala

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conditioned fear, such as the freezing response, autonomic nervous response, pain suppression, stress hormone release, and similar (Kapp et al. 1990). However, the differences between these reactions were mediated by different outputs from the central nucleus. For example, damaging the central gray matter extending to the central nucleus inhibited conditioning of the freezing response; however, it did not inhibit conditioning of the blood pressure response. Conversely, when another site (the hypothalamic cortex) was damaged, conditioning of the blood pressure response was inhibited; however, conditioning of the freezing response was not inhibited. Is the central nucleus the key to establishing conditioned fear responses? In a series of studies (LeDoux et al. 1990), LeDoux et al. found that even if a neuronal tracer is injected into the central nucleus of the amygdala, the tracer is not transported to the auditory thalamus; however, when the tracer is injected into the amygdaloid lateral nucleus, it is transported to the auditory thalamus. Therefore, when the lateral nucleus was damaged, fear conditioning was not established. These observations suggest that the CS, which is a fear signal, was received at the outer nucleus of the amygdala. Furthermore, it was hypothesized that the central nucleus is responsible for response adjustment. However, although they connect directly from the lateral nucleus to the central nucleus, neuronal projections are also present in the accessory basal nucleus and inferior basal nucleus of the amygdala. Furthermore, it remains unclear whether information regarding fear is transmitted directly to the central nucleus or through these sub-nuclei to the central nucleus.

3.3  The “Low Road” and “High Road” to the Amygdala Based on the series of studies showing that the “high-level pathway” via the cerebral cortex and the “low road” not passing through the cerebral cortex concurrently transmit information to the amygdala, LeDoux theorized involvement of the cerebral cortex was not required for the amygdala to induce fear responses. Low-level and high-­level pathways continued to remain in parallel during the course of evolution because of various benefits offered by the low road as well. The low road via the thalamus cannot distinguish minute differences in stimuli; however, information is transmitted quickly because of the smaller number of synapses between the sensory nerves and the amygdala. In rats, it takes only 12 ms for sound stimuli to reach the amygdala through the thalamus; however, it takes approximately twice this duration to reach the amygdala through the cortical pathway. However, the highlevel pathway accordingly transmits more detailed information. There are many advantages of the faster speed of information transmitted along the low road. As is often shown, such as when walking through the forest and a “snake” suddenly appears; however, upon closer examination, is revealed to be a rope, one tries to avoid danger quickly based on lower route activity. It is erroneous to confuse the rope with a snake; however, the cost of not reacting quickly in the event of encountering an actual snake is too high to react more slowly and carefully. Detecting threats quickly is extremely important for survival.

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Fig. 3.1  Schematic representation of the ventral visual pathway (the upper panel) and the subcortical visual pathway (the lower panel). Amy amygdala; LGN lateral geniculate nucleus; SC superior colliculus; TE inferior temporal area TE; TEO inferior temporal area TEO; Pulv pulvinar. This figure is drawn by Kawai based on Pessoa and Adolphs (2010)

In other words, there are two types of information systems: one sensory system parallel to the thalamus and another traversing the cortex to the amygdala. The subcortical route primarily conveys approximate information concerning the external environment, while more detailed and accurate depictions travel along the cortex. Both of these pathways terminate in the lateral nucleus of the amygdala, from which information is sent to the central nucleus through the internal pathway in the ­amygdala. Hence, it is believed that a defensive reaction is expressive of various kinds of fear output from these pathways (Fig. 3.1).

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3.4  The Amygdala May Not Be the Brain’s “Fear” Center LeDoux himself did not consider the amygdala as the brain’s “fear” center. Fear conditioning using aversive stimuli is also established in invertebrates such as crawfish (Kawai et al. 2004), sea hares, and various insects as described in Chap. 1. It is said that these animals do not become “fearful.” According to LeDoux, the amygdala and related neural circuits are mechanisms that can cause organisms to adopt defensive behaviors in response to threats. Damage to this region will cause the organism to no longer react defensively to threats (e.g., no longer exhibiting fear of snakes). However, LeDoux also believes that this type of reaction is different from the “feeling of fear” experienced by humans. As was mentioned previously in Chap. 2, fear conditioning is achieved in the unconscious state; this indicates that this behavior is established (fear conditioning as a defensive reaction) without consciously feeling fear. Indeed, fear learning is acquired through the lower-level pathway rather than via the cortex. However, many people still believe the amygdala to be the fear center because (1) people often feel fear when they perceive a threat and (2) the amygdala is responsible for dealing with threat response. Because of these presumptions, LeDoux insists that the amygdala is often thought to contribute to this “feeling of fear” (LeDoux 1996, 2002, 2015). LeDoux has consistently stated that the amygdala complex contribute to the unconscious side of fear. According to him, fear is generated by the cognitive system following the high-level pathway via the neocortex of the two parallel pathways leading to the amygdala. In other words, the feeling of “fear” is a conscious experience, and animals separate the various reactions to prepare for the experience of electric shocks. The terms “fear conditioning” or “fear system” make ambiguous the distinction between the process of ascribing conscious feelings of fear and the unconscious process of controlling a defensive reaction induced by a threat. Although such a subtle distinction may be confusing, the distinction is important for scientific research (LeDoux 2004). This is also the position that many researchers have taken. Pavlov (1928) was cautious when describing this behavior psychologically, and rather than “speculating that animals are similar to humans and attempting to subjectively postulate the emotional states of animals,” Pavlov tried to explain the prediction of animal saliva secretion physiologically. Tinbergen (1951) also holds a similar position, “ Hunger, like, anger, fear, and so forth, is a phenomenon that can be known only by introspection. When applied to another species, it is merely a guess about the possible nature of the animal’s subjective state.” The position distinguishing between this unconscious subcortical pathway from the “experience of fear” is also consistent with the SDT described in the next section.

3.5  New Theory of Innate Threats Not Related to Learning The neural fear circuit, described by LeDoux, concerns learning of a fear (defensive) response to pure sound stimuli not originally associated with any threat. To investigate the neural mechanisms of fear learning, researchers conducted a study using neutral stimuli without bias (i.e., believed to be novel to the subject.)

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The concern here is whether the subject feels threatened by a specific object (snake) and how the nervous system reacts to it. In Chaps. 1 and 2, I described how fear is learned quickly; however, among others, the conditioning of stimuli related to evolutionary threats is said to be unconscious, rapid, and strongly formed. The nervous system appears to be designed to feel fear against evolutionary threats; however, what is the nature of this design in terms of the neural mechanism of fear learning? Öhman et al. (Öhman and Mineka 2003) speculated that the amygdala and hippocampus are involved in the processing of objects that evoke threat responses in the evolutionary ancestors of many mammals; however, they did not clarify the details. The theories that assume that animals are somehow primed to evolutionary threat-related stimuli as described in Chaps. 1 and 2 are all “learned” behaviors (including autonomic nervous system reactions). However, another theory advocates the presence of evolutionary neural mechanisms in humans and other primates based on a completely different context from fear learning that is capable of recognizing threat-related objects (snakes). This is the SDT.  This theory assumes that humans and primates with large brains are the result of an evolutionary imperative to be able to visually detect snakes efficiently. This theory is not concerned with efficiency of learning or difficulty of erasure and explains fundamentally why primates have relatively large brains compared with other mammals. It is not on evidence from learning theory or experimental results of psychology that this theory is based on mostly evidence drawn from comparative anatomical studies of the primate brain and from anthropology as well as primatology. From this point forward, this chapter will describe the structure and function of the primate brain in the context of SDT.

3.6  The Snake Detection Theory (SDT) In the primate brain, information is analyzed independently along the dorsal and ventral pathways after visual information originating from the retina is received by the primary visual cortex (V1) in the brain. The dorsal pathway is responsible for the processing of spatial information such as positioning (“where”), while the ventral pathway is responsible for the analysis of the nature of the target object (“what”). This information is later integrated; however, the process by which this occurs is still unknown. The parvocellular (P) layer of the lateral geniculate nucleus (LGN), located in the center of the visual pathway, is associated with the ventral pathway of the brain and is primarily responsible for perceptual processing such as determination of morphology. However, the magnocellular (M) layer of the LGN is associated with the dorsal processing pathway in the brain and is partly responsible for exercising, reaching, and gripping (Goodale and Milner 1992; Goodale and Westwood 2004). Examination of many primates revealed that the extent of overlapping of the binocular visual fields was determined by positive correlations with many variables other than the number of neurons in the M layer of the LGN (size of the primary visual field, number of neurons of the P layer, neocortex, and a large brain) (Barton

3.7 Emergence of Primate Ancestor Species and Their Predators

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2004). This is one reason why primates with large brains have wide, overlapping, binocular visual fields; in addition to the characteristic of reaching and grasping onto nearby food, it is also necessary to view objects through close-range stereopsis. The overlapping of binocular visual fields is particularly advantageous for manipulating objects and grasping branches when moving around narrow branches (Barton 2004). However, regarding objects hidden on the ground (such as supplementary food sources such as insects), SDT postulates that this mode of vision was likely suitable for seeing through camouflage (Compton 1995). Based on these observations, Isbell, an anthropologist, proposed that overlapping binocular vision of the early primates, visual specialization, and expansion of the brain were evolutionary adaptations brought about by the threat of snakes, which posed the greatest danger to these primates at this stage of evolution. Hence, the visual system and brain structure of anthropoids are believed to have further developed in reaction to the appearance of snakes (Isbell 2006). Why did the visual capacities of early primates evolve? Animals will not survive long unless they are able to efficiently detect threats from predators and take appropriate action. Isbell reconstructed the evolutionary history of these animals by arranging the order and timing of the era when early mammals, primates, and their predators first appeared. Although snakes, birds of prey, and other carnivorous species are mainly predisposed to hunting mammals (Cheney and Wrangham 1987; Isbell 1994; Kingdon 1997), today, the time of their appearance had a profound impact on the evolution of primates’ brains. The theory to be described in the following sections sheds light on the evolutionary circumstances under which the SDT presumes that snakes catalyzed primate brain expansion.

3.7  E  mergence of Primate Ancestor Species and Their Predators Although the oldest mammalian fossils are from approximately 65 million years ago (Archibald 2003), it is believed that placental species appeared about 100–105 million years ago according to paleontological methods such as molecular analysis (Murphy et  al. 2001a, b; Springer et  al. 2003, 2004; Waddell and Shelley 2003; Reyes et al. 2004). The snake species that preyed upon mammals had evolved to this size as early as 100 million years ago (Greene and Burkghardt 1978; Greene 1983). Some reptiles evolved basic venom systems, and snakes subsequently evolved even more powerful venom-producing systems as early as 60 million years ago (Cadle 1988). This dating suggests that birds and rodents had already appeared and coexisted during the era when primates evolved from insectivores capable of fast movement. It was approximately 11–23 million years ago when snakes from the Old World first appeared in South America from Asia via North America (Zamudio and Greene 1997). In contrast, platyrrhines are believed to have reached South America from Africa approximately 35 million years ago (Arnason et al. 1998). Adaptive divergence

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of platyrrhines started around 26 million years ago, which means that these species’ dispersed throughout the land without exposure to venomous snakes (Schneider et al. 1993, 2001; Chaves et al. 1999; Cropp and Boinski 2000). If this is true, the susceptibility to snakes may be different between platyrrhines and catarrhines. An interesting point in SDT is that unlike the continents of Africa, Asia, South, and Central America, Madagascar has no indigenous snakes. There are venomous snakes in Madagascar, but no true venomous colubrids. The Madagascar monkey has never been exposed to snakes with lethal venom (Glaw and Vences 1994; Kardong 2002; Vidal 2002). Monkey species in Madagascar may be more susceptible to snakes than the South American platyrrhines species. According to data from molecular analysis, the Accipitridae family (large birds of prey such as eagles and hawks) and the Falconidae family (falcons) diverged in South America approximately 68–80 million years ago (Sibley and Ahlquist 1990). However, according to fossil data, the appearance of birds of prey occurred approximately 55 million years ago (Feduccia 1995). It is at least clear that birds of prey emerged after snakes and placental species appeared. The appearance of the first carnivorous species is also believed to have occurred approximately 55 million years ago (Martin 1989; Wayne et al. 1989; Madsen et al. 2001; Murphy et al. 2001a, b). SDT postulates that the earliest predator for the early mammals were snakes that exist today in the same morphology as they did 100 million years ago. The emergence of birds of prey occurred at least 20 million years after the appearance of the first snake species, which fed on mammals, and terrestrial carnivores (bears, large cats, wolves) appeared afterward. Since snakes fed on early mammals, including early primates, mammals had to evolve to escape from snake predators. Some species evolved to move quickly to escape, while others became able to detect snakes via smell. Some animals acquired resistance to snake venom. Monkeys and apes in Africa, which had been exposed to the threat of snakes for approximately 100 million years, developed superior eyesight. African chimpanzees and the researchers following them still encounter snakes frequently (McGrew 2015). Dawkins (1982) wrote that predators and non-predators are always competing in evolution. When an adaptation that allows a non-predator to defend better against the predators occurs, a characteristic is selected that will enable the predator to continue the pursuit. For example, if the body coloration of a non-predator changes to be well camouflaged with the environment, the predator will evolve a more sensitive sensory system to search for camouflaged non-predators. Isbell hypothesized that in the same way, snakes that were preys to mammalian predators stimulated each other’s evolutionary courses. In comparison to other animals, primates are superior in terms of depth perception, which is convenient for crossing branches and grasping objects beyond trees (impossible for other animals), and also have excellent visual acuity and color vision. To realize the visual characteristics of snakes, it was necessary to enlarge the region related to vision in the brain (many areas of the primate brain are involved with vision). Isbell speculates that this evolutionary adaptation likely did not occur all at once. The overlapping of binocular visual fields and visual acuity are clearly different between anthropoids and prosimians (Ross 2000). Furthermore, the differences

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between catarrhines and platyrrhines are not large but still discernible. In response to the evolution of vision in primates and the evolutionary courses of other animals, snakes that had previously attacked and killed preys via body constriction evolved a less energy-intensive method of killing prey approximately 60 million years ago (Greene 1997). This adaptation was venom. In response to this, primates further evolved their vision. One of the many examples illustrating this point is that Madagascar monkeys that were not exposed to venomous snakes have inferior vision than other primates. Specifically, these monkeys have no fovea to see greater details. However, these monkeys still have better vision than other mammals.

3.8  Uniqueness of SDT SDT has some aspects in common with other theories described in the previous chapters. For example, according to the fear module theory, a component of the mammalian brain is automatically activated (a fear module) that informs the subject of the threat independently of cortical control, which is believed to help the animal escape from threat. In addition, a very large amount of experimental and clinical findings as well as archaeological and biological evidence such as fossils and molecular data presented by SDT have led to the common assumption between the two theories that humans and other primates evolved in response to the threat of predatory snakes. However, there is a major difference between SDT and the fear module theory. The fear module is a behavioral and nervous system; however, Öhman et al. focused on the behavioral aspects (especially on learning) of the fear module (Öhman and Mineka 2001, 2003), while SDT focuses on the neurological basis of the mammalian fear module and how it became more specialized in primates compared with other mammals. SDT is a theory aiming to understand how the primate brain has undergone specific evolution, and the fact that it is capable of more specialized vision than other mammals supports the hypothesis directly related to this theory. Unlike the fear module hypothesis, SDT does not address the strength and efficiency of the learning process in which a certain stimulus becomes a signal of danger such as electric shock, because this theory does not mention learning at all. Furthermore, while the fear module theory assumed that snakes and spiders were evolutionary threats (the fear module theory was later revised to include only snakes as threats), SDT considers only snakes. According to Isbell, this theory is not a theory of emotion (fear) but a theory of evolution of the brain and the visual system. Similarly to LeDoux and others, Isbell approaches SDT as a theory of how our brain processes visual cues suggestive of snakes, rather than as a “fear” theory. LeDoux clarified the details of the pathway that activates the amygdala without involving the cortex and named it the “low road.” SDT also supports this idea in that it emphasizes the pathway of activating the amygdala without going through the cortex. However, LeDoux focused on the process of activating the amygdala which was originally neutral (auditory) stimulation, finally focusing on the process of

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establishing learning (defensive reactions). SDT does not provide any explanation regarding auditory information and defensive reactions; however, it differs in how it focuses on the processing of visual information in the brain. SDT aims to explain the evolutionary circumstances under which the primates’ brain expanded and how the threat of snakes is perceived visually. LeDoux distinguished between the ­subcortical and cortical pathways with the terms “low road” and “high road” that are now in common use. However, in SDT, these pathways are referred to as the superior colliculus (SC)-pulvinar visual system and the LGN visual system. As will be addressed in the next section, SDT considers the components of the SC-pulvinar visual system to be a more complex circuit that utilizes the cortex, rather than the simple route hypothesized by LeDoux. The goals of SDT are as follows (Isbell 2006): (1) to demonstrate based on the relative order of appearances of snakes, birds of prey, and carnivorous species that the first of the placental species’ modern predators were snakes; (2) to identify the structures of the brain in the fear module that have the function of warning the animal of danger and prompting an appropriate reaction; (3) to verify the pathways by which the primate brain transmits large amounts of visual information to the fear module; (4) to verify that snakes acted as a selection pressure that promoted binocular vision, the specialization of the visual capacity, and the enlargement of the brain as adaptations in early primates and also to show that venomous snakes served as selective pressure promoting the further specialization seen in anthropoids; and (5) to investigate the diversity in the vision systems of broad-nosed and narrow-nosed monkeys during the periods they coexisted with venomous snakes. Unlike the theory proposed by LeDoux, which assumes a “fear module” including the nerve nuclei and brain regions other than the amygdaloid complex, SDT differs greatly in that it more specifically identifies the fear module. The specific and detailed neural basis of the fear module considered by SDT will be addressed in the following sections.

3.9  The Connections of the Fear Module LeDoux postulated that defensive reactions against threats were triggered quickly and unconsciously through a low-level pathway rather than via the cortex. However, evidence suggests that the neural circuit that conveys the threat information is not as simple and direct as originally thought and is more complicated than transmitting information while incorporating various areas of the cortex (see Fig. 8.3). In SDT, this pathway involves not only the amygdala but also the koniocellular (K) pathway (known as the W pathway in non-primates) of the visual system that transfers information from the retinas to the amygdala via the SC, the pulvinar, the locus coeruleus (LC), and portions of the cortex (Hendry and Yoshioka 1994). The pulvinar is very small particularly in small mammals; however, it is much more prominent in primates and is further developed in anthropoids. As such, SDT hypothesizes that the pulvinar played an important role in primate visual evolution.

3.11 The Superior Colliculus (SC)

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SDT proposes the brain regions described in the following sections as comprising the neural basis of the fear module and describes how these regions interact with other regions of the brain to detect threats.

3.10  The K Pathway The primate visual system has often divided into two pathways: the parvocellular (P) and magnocellular (M) pathways. There are comparatively few studies compared to the functions of the P and M pathways (but see Warner et al. 2012, 2015); however, the existence of the W pathway (K pathway) was identified (Hendry and Yoshioka 1994; Hendry and Reid 2000). Similar in physiology and connectivity to W cells in feline lateral geniculate nucleus, K cells form three pairs of layers in macaques. The W pathway is known as the K pathway in primates, as it was until recently thought to be absent in primates. The W pathway projects primarily from the retina to the SC and has limited contact with the LGN (Henry and Vidyasagar 1991). The K pathway extends not only from the retina to the SC but also to various layers of the LGN (K layer and S layer) (Hendry and Reid 2000). The K pathway links the SC and the LGN, and there is no other connection between these regions (Casagrande 1994). The K pathway connecting the SC with the pulvinar from the K cells of the K layer occupies a greater proportion than the other pathways do (M, P pathways) in the SC-pulvinar (a part of the fear module) visual system. Thus, the SDT assumes the K pathway to play various major roles of the fear module.

3.11  The Superior Colliculus (SC) The SC is a structure located proximal to the pineal gland in the midbrain (tectum), inferior to the thalamus, which connects to both the right and left hemispheres (May 2006). The SC is located on the rostral side of the mesencephalon and the central gray matter of the dorsal midbrain, superior to the inferior colliculus. The SC is remarkably similar across mammalian species and has a seven-layer structure. These layers are divided into superficial (stratum zonale, SZ I layer; stratum griseum superficiale, SGS II layer; stratum opticum, SO III layer) and deep (IV–VII layers) layers (Kanaseki and Spragne 1974). The most input of the SC from the retina is received by the superficial layer and is projected to the K layer of the LGN and the lateral posterior nucleus-pulvinar complex (Stepniewska et al. 1999, 2000) but also receives input from various other regions (the V1, V2, middle temporal gyrus [MT], and posterior parietal cortex [PPC]) of the neocortex, enabling the cortex to transmit information to the SC (Kaas and Huerta 1988). The projection path from the SC to the pulvinar is relatively short and can be difficult to detect in primates; however, in vertebrates other than primates, this serves as the primary visual system (Henry and Vidyasagar 1991). There

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are three large branches of the SC-pulvinar visual system (Berson 1988; Sherman and Spear 1982). One is the pathway from the retina to the SC, which extends to the LGN. A second branch is the pathway from the retina to the pulvinar via the SC. The third branch extends from the retina directly to the pulvinar. All three pathways extend to the cortical regions in the MT field responsive to the extrastriate cortical regions such as the V2 and movement-related visual cues.

3.12  The Lateral Posterior Nucleus (LP)-Pulvinar Complex The LP of the thalamus is a nuclear group between the medial and lateral medullary lamina, which constitutes an intermediate center of somatosensory sensation and belongs to the extrapyramidal system. The posterior nuclear group is divided into the dorsolateral nucleus and ventral nucleus. The LP-pulvinar complex consists of the dorsolateral nucleus, the posterolateral nucleus, and the pulvinar nucleus. The LP-inferior pulvinar interacts with the retina, the LC, and the superficial layers of the SC (Stepniewska 2004). The optic nerve primarily transmits visual information to the LGN and the SC of the midbrain; however, only few optic nerve fibers connect directly to the posterolateral-pulvinar. The posterolateral nucleus has mutual nerve fiber communication with the visual cortices including the V1 and V2. The posterolateral nucleus receives nerve fiber communication from other nuclei of the thalamus and connects to the parietal association field (superior apical lobule). Hence, sensory information is analyzed and integrated in the parietal association field. The pulvinar nucleus of rodents and other small mammals are extremely small and cannot be identified in some species; however, it is distinctly present in primate species, particularly anthropoids (Chalupa 1991; Jones 1985; Stepniewska 2004). The pulvinar nucleus may be classified into four different divisions, with which three of them involved in visual processes. These three major divisions are differentially associated with the cortical structures of early visual processing (primary visual cortex), low- to medium-level extrastriate visual cortex (occipital areas), to higher cortices (parietal, frontal, cingulate cortex). The inferior pulvinar (PI) and the ventral part of lateral pulvinar (PL) receive major cortical inputs from primary visual centers (striate and the adjoining extrastriate cortices), whereas the dorsal part of the lateral and medial pulvinar (PM) receive input from higher-level processing cortices (the posterior parietal and parts of frontal cortex) (Stepniewska 2004). These three major subdivisions receive also subcortical input from the SC. The PL are topographically organized which reflect the retinotopic representations of the SC and the extrastriate cortex. Although the inferior lateral pulvinar seems to exhibit minimal variation between primate species and is believed to have been less influenced by evolutionary changes, expansion can be identified on its dorsal side particularly in anthropoids.

3.15 The Connections of the LP-Pulvinar Visual System

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3.13  The Amygdaloid Complex The amygdala is an almond-shaped nucleus existing behind the temporal lobes of vertebrates, including humans, and consists of multiple neural nuclei with different functional characteristics. Typical nuclei include the basolateral nuclear complex (comprised of the lateral nucleus, basal nucleus, and the accessory basal nucleus), the anterior nucleus, the central nucleus, and the cortical nucleus. The outer nucleus that responds to auditory information and visual cues such as the direction of the line of sight of others receives information from the LC, the dorsal pulvinar nucleus, the IT, the superior temporal sulcus (STS), and the basolateral and central nuclei of the amygdala (Jones and Burton 1976; Amaral et al. 1992; Aggleton and Saunders 2000; LeDoux 2000). The central nucleus mediates the kinetic expression of fear (e.g., the freezing response) (Kalin et  al. 2004) and is linked to the basolateral nucleus, the LC, the periaqueductal gray (PAG), and the substantia nigra. In terms of the ratio of the size of the amygdala relative to the size of the body, SDT presumes that there were virtually no changes due to evolution, since the central nucleus is not particularly large in primates. The basolateral nucleus receives input from the LC, PPC, and DLPFC; it projects to the V1, MT, and the central nucleus in the amygdala, to activate fear responses (Selemon and Goldman-Rakic 1988; Aggleton and Saunders 2000). The basolateral nucleus is enlarged in haplorhines (catarrhines and platyrrhines) and correlates with the size of the primate neocortex; however, there is no correlation in insectivorous species (Barton and Aggleton 2000: Barton et al. 2003).

3.14  The Locus Coeruleus (LC) The LC is a small nucleus located on the dorsal side of the central nervous system bridge in the vertebrates. The LC has extensive connections and is connected with the SC, LP-pulvinar, amygdala, LGN, and V2.

3.15  The Connections of the LP-Pulvinar Visual System The pulvinar is the main thalamic centers of the primate visual system, as well as the LGN (Warner et al. 2012, 2015). The pulvinar, however, receives relatively few primary ascending afferents from the retina and other visual centers in the brain stem (the SC) compared to the LGN, while it has strong associations with neocortex. The pulvinar nucleus is located at the rear of the thalamus and receives inputs from the V1, V2, SC, the anterior tectum (pretectum) region, and other regions and projects onto the visual cortex, temporal lobe, and parietal lobe. The inferior lateral pulvinar is connected to the deep layer of the SC and does not extend to the V1; however, it extends to many cortical areas such as the V2, V4, the inferior temporal

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Fig. 3.2  Schematic representation of three divisions of the pulvinar and some of their connections. Some of the connections (such as connection to the cingulate cortex) are omitted for simplicity. PM medial pulvinar, PL lateral pulvinar, PI inferior pulvinar, SC superior colliculus, Amy amygdala, the ventral visual pathway (the upper panel), and the subcortical visual pathway (the lower panel). Illustration by Nobuyuki Kawai

lobe [IT], PPC, and the dorsolateral prefrontal cortex (DLPFC) (Trojanowski and Jacobson 1974; Glendenning et al. 1975; Baleydier and Mauguiere 1987; Selemon and Goldman-Rakic 1988; Robinson and Petersen 1992; Gutierrez et  al. 2000; Stepniewska 2004). The optic nerve primarily transmits visual information to the LGN and the SC of the midbrain; however, a few optic nerve fibers also connect directly to the LP-pulvinar. The LP-inferior pulvinar interacts with the retina, the LC, and the superficial layers of the SC. The connection of the LP-pulvinar visual system is described using the diagram of a macaque monkey’s brain (Fig. 3.2). Although considerably more sophisticated than the circuit presumed by the fear module theory, it should be noted that SDT does not assume that these circuits comprise the entire fear modules.

3.16  What Are the Functions of the Fear Module? How do these fear modules contribute to the detection of threats (especially snakes)? Animal brains need to detect and avoid dangerous animals for survival. One way to avoid predators is to find predators early. Particularly in primates, SDT assumes that the visual system evolved to locate predators more reliably and quickly over the course of evolution. How each element of the fear module functions is described below in the context of SDT. The SC-pulvinar visual system not involving the cortical system is important for rapid threat detection. The SC-pulvinar visual system (or “extrageniculate system”

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for more general appellation) is a component of the “fear module” that processes visual information that is activated automatically (although not homologous, it corresponds with the lower-level route proposed by LeDoux). In other words, SDT assumes that animals need a part of the neocortex to detect threats and respond appropriately, while SDT suggests that other cortical areas are also involved in threat detection. Since vertebrates other than mammals do not possess a neocortex, this assumption seems to be valid. The SC, pulvinar, LGN, V1, and V2 present in the SC-pulvinar system exist in all mammals. If snakes had exerted selective pressure on the mammalian visual system, the cells of the mammalian visual system would respond to the visual features of the snake. One such stimulus is a periodic pattern that is unusual in nature but is common in every snake. The periodic patterns of snakes are mainly due to their scales (see Chap. 8). The basic patterns made by scales generally contain line segments with different slopes, edges, corners, and contours (Coss 2003). Cells related to early visual processing in mammals respond strongly to any of these patterns, suggesting the possibility that many mammals were exposed to the threat of snakes. The K pathway that conveys the majority of information of the SC-pulvinar visual system is responsible for the preconscious detection of noticeable stimuli. It is believed that the main function of the SC in mammals is to detect and avoid predators (Sewards and Sewards 2002). The SC also plays a role in directing the head and eyeballs toward the subject of attention for viewing and listening (Klier et  al. 2003). Although the neurons of the superficial layers of the SC respond to changes in moving objects and light, the superficial layers of the SC receive only visual information (Kadoya et al. 1971). In contrast, some neurons of the deeper layer of the SC respond (1) vigorously to quickly moving stimuli of line-shape and (2) respond also to both the somatic and auditory stimuli. Interestingly, the directions of the visual receptive field of neurons which respond to both visual and auditory stimuli coincide with those of the auditory receptive field. The deeper layer of the SC seems to play roles to direct orientation in space to important information by capturing and integrating the visual and auditory inputs or the visual and somatosensory inputs (Stein 1978). Electrical stimulation to the deep layer of the SC in the monkeys elicited the saccadic eye movements. The amplitude and direction of this saccadic movement were irrelevant to the eye positions but were determined by locations of the neurons to be stimulated. The amplitudes were increased as the locations the stimulation traveled from rostral (which represent the foveal vision) to caudal (which represent peripheral vision) portions. The stimulations of medial portions made the saccadic eye movements upward, and the stimulation of lateral portions made them downward (Robinson 1972). Thus, apparent visual input in the peripheral visual field will result in elicitation of greater downward saccadic movements. This function will help mammals to find snakes more quickly. The deep layer of the SC also relates to the covert shift in attention before moving the gaze to a clear stimulus in the environment (Ignashchenkova et al. 2004). The deep layer of the SC seems to be involved in the processing of predator-related information (Isbell 2009). When electrical stimulation was applied to the deep layer of the SC in rats, defensive reaction such as the freezing response and rushed movement occurred immediately. When this area was damaged, such behaviors were not observed (Ellard and Goodale 1988; Northmore et al. 1988; Sewards and Sewards 2002). There is nerve

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fiber communication from the PAG and the cuneiform nucleus to the deep layer of the SC. Cells in the SC of felines respond strongly to synchronous movements of short lines; moreover, this reaction becomes stronger as the line segment becomes longer. With regard to snakes, a short line was used to identify the snake’s scales, and the latter was useful for identifying the body of a snake (Isbell 2009). Superior colliculus neurons of monkeys encode a visual saliency map during viewing natural scenes (White et al. 2017). Although it has been proposed that the function of the SC in primates differs from other mammals, primates have come to rely on visual input for the detection of threats over the course of evolution. Thus, the SDT assumes that the function of the SC is the same for primates as well (Isbell 2006, 2009). The function of the LP-pulvinar complex is the integration of binocular vision and sensory information, localization of visual attention to related stimuli, and ocular movements (Robinson and Petersen 1992; Robinson 1972). As described above, the pulvinar receives major afferent inputs from the visual cortex and the SC. Visual cortex analyzes visual information precisely, whereas the SC is involved in the use of visual stimuli as targets for orienting movements. The SC mediates neural activities of the pulvinar by eye movements. Saccade-related activity has been also found in neurons in the inferior pulvinar (PI) (Petersen et al. 1985; Robinson et al. 1986). Lesions of pulvinar resulted in an abnormal pattern of eye movements (Stepniewska 2004). Neural activities of the pulvinar to moving stimuli during fixation are suppressed during eye movements. These studies suggest a role for the integration of visual and oculomotor information by the pulvinar (Stepniewska 2004). Targets of attention include threatening predators, tree-branches, and trees to guide locomotion. The function of the inferior and ventral lateral pulvinar is to facilitate selective visual processing and to move attention to related objects. These are essentially equivalent to the function of the SC. In addition, in anthropoids, the SC promotes V2 neural activity (Soares et al. 2001). The pulvinar is also involved in visual attention (Robinson and Petersen 1992). Impaired attentional selection has been documented in humans and monkeys following pulvinar damage (Petersen et  al. 1987; Rafal and Posner 1987). The pulvinar suppress processing of irrelevant visual information and facilitate processing of information that has behavioral importance (Petersen et  al. 1985; Robinson and Petersen 1992). Therefore, the pulvinar contributes visual attention including attentional filtering in the presence of visual distractors (LaBerge and Buchsbaum 1990) and selective attention to stimulus shape, speed, and color (Corbetta et  al. 1991). Among subdivisions of the pulvinar, neurons in the PI and the ventral portion of the PL may be related to focusing attention selectively on the form or color of a visual stimulus. Unit recording studies showed that PI and PL neurons have well-defined receptive fields with sizes positively correlated with their eccentricity, and most neurons are sensitive to moving stimuli (Petersen et  al. 1985; Benevento and Miller 1981). These neurons also may be characterized by sensitivity to stimulus orientation, directional selectivity for motion, or both orientation and direction selectivity; however, the majority of cells are either broadly tuned or nonselective for these attributes (Petersen et al. 1985). Thus, the portions of ventral pulvinar contribute to processes such as visual fixation, visual pattern discrimination, and attending to salient stimuli (Robinson and Petersen 1992; Acuna et al. 1983). In contrast to PI and PL,

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the dorsal portions of the lateral pulvinar and medial pulvinar (PM) do not have clear retinotopic organization. These portions seem to contribute to visuospatial (directed) attention and spatially directed reaching (Petersen et al. 1985; Acuna et al. 1983). These portions have connections with posterior parietal and frontal cortices, the SC, and amygdala. Thus, the PM may be related to memory and emotion formation. The cells of the pulvinar in humans (Kastner et al. 2000; Kastner et al. 2004: Villeneuve et  al. 2005) and feline animals (Casanova 2004) respond strongly to moving or blinking grid patterns. SDT specifies that such patterns are very similar to the mosaic patterns created by the scales when snakes move. Among these, the cells that are most sensitive to such lattice patterns are those of the medial pulvinar, which connects to the amygdala (Casanova 2004). The LC has the largest number of NA-containing neurons assembled in the central nervous system. NA is a neurotransmitter involved in the promotion and learning of memory of aversive events in particular. The LC is activated in response to threat stimuli and vigilance need-related stimulation (Berridge and Waterhouse 2003). Damage to the LC in rats has been shown to decrease the freezing response in reaction to threats (Neophytou et al. 2001). In other words, the LC plays a vigilance function in threat situations. Based on these observations, it is assumed that in the fear module of the brain, the LC is responsible for general alert sensations, while the SC and the pulvinar detect the actual threat. In addition to the SC, pulvinar, and LC, the fear module also contains the amygdala (see Fig. 3.3). The amygdaloid complex plays a major role in the processing and memory of emotional reactions. It is strongly involved in the

Fig. 3.3  Representative primate brain showing the relative locations of the various structures of the fear module, which are connected by the solid lines and its connection to the K pathway (visual pathway). Some cortex areas, which connect these structures, are also shown. Amy amygdala, LC locus coeruleus, V1,V2, V4 visual areas 1, 2, and 4, SC superior colliculus, Pulv pulvinar, MT middle temporal cortex, DLPFC dorsolateral prefrontal cortex. (This figure is drawn by Nobuyuki Kawai based on Isbell (2009))

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processing of negative emotions, especially fear and anger. The basal lateral nucleus of the amygdala is involved in learning for threat stimulation, learning of fear reactions, changing stored memories, etc. Based on this, it is hypothesized that the amygdala must play an important role in learning what threats are and how to respond (Jones and Burton 1976; LeDoux 2000). The amygdala is also involved in the assessment of threats. Furthermore, it is sensitive to clues given by others, and attention is shifted automatically in the direction of the line of sight where others are directed in healthy subjects (Okamoto and Kawai 2006; Kawai 2008, 2011). However, subjects with damage to the amygdala can detect happy faces but not angry faces (Amaral 2003). This seems to be because such subjects no longer have the capacity to automatically focus their eyes (Adolphs et al. 2005). If a subject cannot judge the situation by looking at the peer’s angry face, the possibility of survival of that subject will be low. Humans and monkeys can readily spot an angry face of conspecifics among neutral faces (Kawai et al. 2016). Nerve fiber communication from the deep layer of the SC to the pulvinar involved in defensive reactions only exists in the medial pulvinar where the most intense neural activity in response to lattice patterns is observed. This medial region is in contact with the amygdala, which controls fear reactions. If the medial pulvinar is damaged, the lateral nucleus of the amygdala to which it projects will not be activated even if when the subject looks at fearful faces, and accordingly such subjects are unable to recognize the expression of fear (see Fig. 3.2). The lateral nucleus of the amygdala plays a major role in recognizing (social) fear; however, medial part of the pulvinar and the SC that convey information to the amygdala also seem to play major roles as well. In the “detection” of snakes, individual regions included in the SC-pulvinar system will play roles in recognizing the visual information characteristic of the snake and transmit this information to the amygdala to activate the defensive reaction. In addition to these roles, SDT also postulates that the neocortex is included as a fear module to detect threats. The important areas are the neocortical V2 and V4 areas. The cells of the V2 are sensitive to short line segments, corners, contours, obstacles (these four are important for depth perception), and long object movements (Peterhans and von der Heydt 1993). Both of these help to find the contours and corners of snakes’ scales and the movement of their long bodies along with snakes hidden in plants and rocks. The neurons of the V2 react more strongly than do those of the V1 in binocular vision, which help distinguish snakes from their habitat. The cells of the V4 and IT cortex react selectively to the snake’s scale geometry. The V4 cells respond strongly to checkerboard patterns presented in peripheral vision (Kastner et al. 2000). The cells of the IT cortex respond more strongly to rhomboid patterns than to random dots, triangles, or circles. The shape of snakes’ heads is generally rhomboid. Obviously, other stimuli in the natural habitat comprise corners, contours, and inclined line segments; however, rhombic patterns of a uniform size and in conjunction with long body shapes are most often indicative of a snake. The SC, pulvinar, and V2 (the V2 receives compensatory input from the SC-pulvinar and the LGN visual systems) can integrate spatial and morphological visual cues (Kastner et al. 2004). The elements of snake visual features detected by multiple brain regions are morphologically and spatially integrated so that the amyg-

3.17 Snake-Detecting Neurons?

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dala body can activate the defense reaction (move away from the snake). Of course, the LGN visual system also handles visual information related to fear sources; however, according to the SDT, this system is not a part of the fear module because the system creates a conscious sensation (Isbell 2009, p. 81), which causes strong fear sensations and is not a primarily factor to drive primate visual systems. In other words, animals do not heavily rely on a neocortex to visually detect and respond to threats except only limited areas, such as V1, V2 and V4. This makes sense because nonmammalian vertebrates do not possess a neocortex, whereas they are able to respond appropriately to threats. For instance, forebrain (telencephalon)-ablated goldfish learned to escape from the electric shock as quickly as non-operated control fish, while they were not able to acquire and remember avoidance learning (Hainsworth et al. 1967; Overmier and Papini 1986).

3.17  Snake-Detecting Neurons? SDT assumes the abovementioned role in these brain regions constituting the fear module; however, do these neurons actually contribute to snake detection? Although Isbell and other neuroscientists have shown that cells of the pulvinar, one of the fear modules, react quickly and vigorously to images of snakes, only a few studies have been conducted to date. These neurons suggest that monkeys may excel at automatically and rapidly detecting snakes. As mentioned above, it has generally been thought that the pulvinar contributes to directing the eyes to objects in the external environment. Then, SDT assumes that this ability has the purpose of helping to quickly detect potential threats in open areas. To test this prediction employed, two monkeys in neuroscience studies were born in captivity and never witnessed a snake. Isbell et al. embedded electrodes in the heads of two Japanese macaques (Macaca fuscata), which were then showed four kinds of pictures (Van Le et al. 2013). During this, activity from the cells inside and outside the primate medial and dorsolateral pulvinar (both characteristically large in primates) in the brain was recorded. The images included a snake (coiled and uncoiled), a face of a macaque of the same species (angry face/neutral face), hands of another macaque, and geometric figures (circle, square, cross). Of the 91 neurons measured, 40.6% were “snake-best” neurons that responded most to snake photographs compared with other photographs. The other 28.6% were “face-best” neurons, 18.7% were “hand-best,” and 12.1% were “shape-best.” It is not surprising that many neurons responded to an image of a macaque’s face. Because the face is a very important source of information for primates, such as knowing that other members of the group are angry. What was important was that the pictures of snakes triggered the greatest and fastest response from neurons. For example, the neurons responding to the image of the snake began responding faster than the neurons responding to the geometric figures and the angry face by 25  ms and 15  ms, ­respectively. Since conscious recognition of the subjects that we and other primates are seeing is time-consuming, it is necessary for the pulvinar nucleus to respond

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quickly even before more detailed information becomes available, suggesting a possibility that this quick detection is supported by these neurons. In addition, the macaques were presented with photographs of macaque faces that were scrambled, shredded into many pieces, or blurred, and neuron responses were markedly weakened for the scrambled facial photographs. In addition, reaction intensity was also weakened for images in which low-frequency information was uncoupled from the spatial frequency component and only the high-frequency component remained. However, neurons responded strongly to photographs lacking high-frequency components and with only low-frequency components similar to the original photographs. These findings suggest that low-frequency components (e.g., scales) are important for snake recognition even in snake photographs. However, it should be noted that color information is reduced in the images that retained high-­ frequency components, whereas color information remained in images with low-­ frequency components. Following this study, Isbell investigated whether activity of the pulvinar neurons differed on the basis of the posture of the snake in a subsequent study utilizing delayed nonmatching to sample (DNMS) tasks. Of the 821 pulvinar neurons recorded when Japanese macaques were shown photographs of snakes with threatening posture and snakes in neutral postures, 78 neurons responded to all snake photos regardless of posture. The pulvinar neurons on the medial and dorsal sides responded more strongly to the images of snakes in threatening poses than to the photographs of snakes in neutral poses; however, there was no significant difference at the time of response. Analysis of these 78 neurons through a multidimensional scaling analysis revealed that after a 50-ms stimulus presentation, these cells could be classified as a cluster of neurons reacting to offensive and non-offensive snake postures. These findings indicate that the primate pulvinar neurons distinguish offensive postures from non-offensive postures by snakes very quickly. It is surprising that pulvinar neurons exhibit different responses to variations in snake characteristics, and this is meaningful for both humans and monkeys. However, it is not yet clear how these pictures differ except for the posture of the subject. For example, by lifting a creature with a sickle-shaped neck such as a snake, it may be easier to see owing to the difference in the snake’s scale coloring because many snakes have white-colored underbellies (the dorsal side of the snake is often colored to be camouflaged with soil and leaves, and the scales can be difficult to distinguish). The visual characteristics of snakes will be reviewed in a later chapter. Apart from the details of the stimuli presented, these studies indicate that the neurons in the fear module of the primate brain respond strongly and quickly to snakes, which provides neuroscientific evidence supporting SDT.

3.18  Predictions Using SDT An appropriate theory makes predictions that lead to the discovery of phenomena that would not otherwise have been discovered or encourages further study. As explained previously, a portion of pulvinar cells respond strongly to snakes. SDT

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has made more than 30 unique predictions, and some were consistent with available evidence by the time SDT was proposed; however, many had not yet been tested. Although SDT offers many predictions as shown in Table 3 of Isbell (2006), of the nine core predictions of the theory, the following four anticipated new phenomena. 1. Primates detect immobile snakes faster, or more reliably, or from a greater distance than other mammals do. 4. Venomous snakes arrived in South America after platyrrhines. 5. Platyrrhines radiated in the absence of venomous snakes. 9. Catarrhines detect snakes faster, or more reliably, or from a greater distance than platyrrhines and prosimians do. Of these four, the first and the last can be verified through psychological studies. The other five predictions are also important: 2 . Venomous snakes have never existed on Madagascar. 3. Prosiminans have less specialized visual systems than anthropoids. 6. Platyrrhines have more variable visual systems than catarrhines (not an artifact of sampling bias). 7. Venomous snakes evolved in Africa or Asia before catarrhines radiated. 8. Catarrhines have the most specialized visual systems of the primates. Among them, the hypothesis that primates, including humans, recognize snakes quickly and accurately is accumulating large amounts of supporting evidence. In the following chapter, I will outline the experiments that verified this hypothesis.

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Sherman SM, Spear PD (1982) Organization of visual pathways in normal and visually deprived cats. Physiol Rev 62:738–855 Sibley CG, Ahlquist JE (1990) Phylogeny and classification of birds: a study in molecular evolution. Yale University Press, New Haven Soares JGM, Diogo ACM, Fiorani M, Souza APB, Gattass R (2001) Changes in orientation and direction selectivity of cells in secondary visual area (V2) after GABA inactivation of the pulvinar in Cebus monkeys. Soc Neurosci Abstr 27:1633 Springer MS, Murphy WJ, Eizirik E, O’Brien SJ (2003) Placental mammal diversification and the cretaceous-tertiary boundary. Proc Natl Acad Sci USA 100:1056–1061. https://doi. org/10.1073/pnas.0334222100 Springer MS, Stanhope MJ, Madsen O, de Jong WW (2004) Molecules consolidate the placental mammal tree. Trends Ecol Evol 19:430–438. https://doi.org/10.1016/j.tree.2004.05.006 Stein BE (1978) Nonequivalent visual, auditory, and somatic corticotectal influences in cat. J Neurophysiol 41:55–64 Stepniewska I (2004) The pulvinar complex. In: Kaas JH, Collins CE (eds) The primate visual system. CRC Press, Boca Raton, pp 53–80. https://doi.org/10.1201/9780203507599.ch3 Stepniewska I, Qi H-X, Kaas JH (1999) Do superior colliculus projection zones in the inferior pulvinar project to MT in primates? Eur J  Neurosci 11:469–480. https://doi. org/10.1046/j.1460-9568.1999.00461.x Stepniewska I, Qi H-X, Kaas JH (2000) Projections of the superior colliculus to subdivisions of the inferior pulvinar in new world and old world monkeys. Vis Neurosci 17:529–549. https:// doi.org/10.1017/S0952523800174048 Tinbergen N (1951) The study of instinct. Oxford University Press, New York Trojanowski JQ, Jacobson S (1974) Medial pulvinar afferents to frontal eye fields in rhesus monkey demonstrated by horseradish peroxidase. Brain Res 80:395–411. https://doi. org/10.1016/0006-8993(74)91025-7 Van Le Q, Isbell LA, Matsumoto J, Nguyen M, Hori E, Maior RS, Tomaz C, Tran AH, Ono T, Nishijo H (2013) Pulvinar neurons reveal neurobiological evidence of past selection for rapid detection of snakes. Proc Natl Acad Sci USA 110:19000–19005. https://doi.org/10.1073/ pnas.1312648110 Vidal N (2002) Colubroid systematics: evidence for an early appearance of the venom apparatus followed by extensive evolutionary tinkering. J Toxicol: Toxin Rev 21:21–41. https://doi. org/10.1081/TXR-120004740 Villeneuve MY, Kupers R, Gjedde A, Ptito M, Casanova C (2005) Pattern-motion selectivity in the human pulvinar. NeuroImage 28:474–480. https://doi.org/10.1016/j.neuroimage.2005.06.015 Waddell PJ, Shelley S (2003) Evaluating placental inter-ordinal phylogenies with novel sequences including RAG1, gamma-fibrinogen, ND6, and mt-tRNA, plus MCMC-driven nucleotide, amino acid, and codon models. Mol Phylogenet Evol 28:197–224 Warner CE, Kwan WC, Bourne JA (2012) The early maturation of visual cortical area MT is dependent on input from the retinorecipient medial portion of the inferior pulvinar. J Neurosci 32:17073–17085. https://doi.org/10.1523/JNEUROSCI.3269-12.2012 Warner CE, Kwan WC, Wright D, Johnston LA, Egan GF, Bourne JA (2015) Preservation of vision by the pulvinar following early-life primary visual cortex lesions. Curr Biol 25:424–434. https://doi.org/10.1016/j.cub.2014.12.028 Wayne RK, Benveniste RE, Janczewski DN, O’Brien SJ (1989) Molecular and biochemical evolution of the Carnivora. In: Gittleman JL (ed) Carnivore behavior, ecology, and evolution. Cornell University Press, Ithaca, pp 465–494 White BJ, Berg DJ, Kan JY, Marino RA, Itti L, Munoz DP (2017) Superior colliculus neurons encode a visual saliency map during free viewing of natural dynamic video. Nat Commun 8:14263. https://doi.org/10.1038/ncomms14263 Zamudio KR, Greene HW (1997) Phylogeography of the bushmaster (Lachesis muta: Viperidae): implications for neotropical biogeography, systematics, and conservation. Biol J  Linn Soc 62:421–442. https://doi.org/10.1111/j.1095-8312.1997.tb01634.x

Chapter 4

Ontogeny and Phylogeny of Snake Fear

Abstract  In previous chapters, I have argued that recent theories have presupposed the notion that people feel innately threatened by particular stimuli (e.g., snakes and spiders). However, people learn either directly or indirectly that particular stimuli are threatening, so it is necessary to investigate whether responses (fear responses) to particular threatening stimuli are “innate” using babies and animals with no experience of these stimuli. This chapter provides an overview of studies using babies (infants) and monkeys as subjects. Neither babies nor monkeys show explicit fear responses toward snakes (or spiders, etc.). However, they closely watch videos of snakes when sounds expressing threats are heard. Thus, it appears that there do exist latent associations. In addition, human infants and monkeys quickly find the pictures of snake among those of flowers than vice versa. Such findings support the claim of snake detection theory (SDT) that primates evolved a visual system for efficiently detecting snakes. I described in Chaps. 1 and 2 that people innately feel fear toward particular stimuli because fear learning is established more easily toward these stimuli. Öhman and Mineka (2001) believe that in the course of evolution, people came to fear reptiles, which were threats to our human ancestors. In Chap. 3, I discussed SDT (Isbell 2006), which states that primates and humans acquired visual systems for detecting snakes, their main predators in the course of evolution. If humans “innately” fear particular stimuli (snakes), babies and monkeys, who have never seen snakes, should also show particular responses (fear) toward snakes. In this chapter, I give an overview of studies that have investigated the behavior of babies (infants) and laboratory-raised monkeys toward snakes.

4.1  Is Fear of Snakes Learned? When a frightening stimulus is sensed, the amygdala is activated. When people find snakes and spiders, the amygdala is activated, and fear is experienced. The similar processes also occur in response to guns and sword. Guns and swords arose relatively © Springer Nature Singapore Pte Ltd. 2019 N. Kawai, The Fear of Snakes, The Science of the Mind, https://doi.org/10.1007/978-981-13-7530-9_4

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recently in human history, so it is impossible that humans are built to fear them innately. Do adults, then, fear guns and swords because of threatening experiences? I have never been cut with a sword, yet when I polish my genuine Japanese katana (sword produced more than 150 years ago), I feel a chill run down my spine as I appreciate its beauty. In this case, I feel fear because I have learned indirectly by seeing or reading about people being killed with swords and guns in movies and books (cf. Rachman 1977). Adults and preschool-age children learn various fears from stories and from the people around them. Even babies, when they begin to walk, learn from their parents that cars are dangerous. Babies learn of numerous dangers indirectly by various means, without experiencing the dangers for themselves. My daughter never once watched television until she turned one. My daughter did not go to preschool, and great care was taken to only read her stories in which snakes did not appear. She was never shown a snake at the zoo. However, at the age of 2, we played a game with her where she tried to name animals using cards showing cute illustrations of the back view of the animal on the back side and the front view of the animal on the front side. When we flipped to the card of a cute illustration of a snake, my daughter said, “That’s scary,” and began to cry. I considered this an example of innate fear of snakes. The video of my daughter’s reaction remains one of the highlights of my talks on fear of snakes, but even so, it cannot be said definitively that my daughter never had an opportunity to learn to fear snakes (Fig. 4.1). Myths around the world depict snakes as sinister creatures. In Greek mythology, while the infant Hercules slept in his cradle, his stepmother Hera (wife of Zeus) placed two snakes inside. Medusa’s hair is made of snakes that turn anyone who looks at them into stone. In the Old Testament of the Bible, Adam and Eve are driven from Eden after being tempted by a serpent to eat the fruit of the tree of knowledge. God said to the serpent, “Thou art cursed… upon thy belly shalt thou go, and dust shalt thou eat all the days of thy life.” The Kojiki, a record of Japanese mythology, features a giant constrictor with eight heads and eight tails, a serpent of such enormous size that cedar and cypress trees grow from its body. Susanoo-no-­Mikoto makes the serpent drink sake from all eight of its heads, and when he slays it in its Fig. 4.1  My daughter who was playing a naming task cried out by watching a “cute” snake illustration (Photo by Nobuyuki Kawai)

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drunken stupor, the sword emerges from its tail. This sword is Kusanagi-no-Tsurugi, one of the Three Sacred Treasures handed down to a new Emperor in the enthronement ceremony as a testimony of Imperial Rank. As seen here, snakes appear as sinister creatures in myths from around the world, and frightening, evil, snakelike creatures often appear even in picture books and cartoons for children. Thus, it appears possible that children learn by the age of 2 or 3 that various stimuli, including snakes, present threats. Some studies have focused on even younger children.

4.2  Are Infants Afraid of Snakes? DeLoache and LoBue (2009) investigated whether 9-month-old infants fear snakes. Nine-month-old infants and their mothers came to the laboratory, and the infants sat in their mothers’ laps while videos of animals with distinct shapes (elephants, giraffes, and snakes) moving around were shown. The researchers investigated two behaviors. The first was the time the infants spent watching the videos. People turn their eyes away from things that frighten them. Likewise, it was predicted that if the infants had already developed a fear of snakes, they would spend less time looking at the snakes, but on the other hand, it was also thought that they might pay more attention to the snakes and spend a longer time looking at them as compared to the other animals. The other behavior consisted of the infants reaching toward the screen and attempting to grab it. A previous study showed that children at this age reach out and try to touch a video image on a TV screen (DeLoache et al. 1998), as if it were an actual three-dimensional object. It was predicted that if the infants were afraid of snakes, they would try to grab them less frequently than the other animals. The results of the experiment were inconclusive. There was no statistical difference in the time spent observing snakes and the other animals or in the number of times the infants tried to touch them. In fact, even when the 9-month-old infants tried to touch the snakes, their behavior did not demonstrate fear. Thus, the authors concluded that fear toward snakes is not demonstrably developed in 9-month-old infants (DeLoache and LoBue 2009). Subsequently, the authors performed an experiment using the same procedure in which somewhat older children (18–36  months) were shown actual snakes and spiders (LoBue et  al. 2013). With this method, the children did not show fearful avoidance behavior toward the snakes and spiders, and they showed the same degree of interest toward spiders and snakes as they did toward non-threatening animals such as hamsters and fish. That is, children 3  years of age and younger did not display fear responses toward snakes and spiders (unlike my daughter). These experimental results support LeDoux’s idea that “fear is an experience created by the cerebral cortex.” If fear is a subjective experience created by the cerebral cortex through the activation of the amygdala (memory as experience), in babies with undeveloped cerebral cortexes and (nearly) no experience of defensive

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reactions toward snakes and spiders, perhaps fearful behavior (or subjectively experienced fear) is not expressed toward these stimuli. Thus, another experiment of the same study (DeLoache and LoBue 2009) used a new method to investigate whether fear toward snakes forms easily due to latent associations. Here, a method called the audio-visual matching paradigm was used. This experimental method made use of the phenomenon that when a particular sound is played, infants aged 4–7 months observe a corresponding video for longer. For instance, when the sound of a drum is played in the background, children watch a video of a drum being played longer than a video of a woman playing peekaboo (Spelke 1976). Likewise, while listening to the sound of laughter, children watch a video of a smiling face longer than a video of a sad face (Walker 1982). Applying this knowledge, babies aged 7–16 months were shown videos of snakes and animals other than snakes (e.g., giraffes, elephants) lined up from left to right on a big screen. At this point, the children heard either fearful or happy human voices. The question was which video the babies would watch longer with each type of voice. While the fearful voice played, the babies watched the video of snakes for longer. However, when watching animals other than snakes, the different voices led to no difference in observation time. This indicates that children tend to associate videos of snakes with fearful voices. That is, these results indicate that there is “preparedness” between snakes and fear.

4.3  L  aboratory-Raised Monkeys Do Not Show Fear Responses Toward Snakes The lack of fear responses toward snakes in children younger than 3 years old seems to correspond to the fact that human-raised monkeys either display nearly no fear response toward snakes or their fear responses are quickly extinguished. In outdoor field research conducted on 11 genera of primates, the primates showed fear responses, such as sounding alarm vocalizations, when they encountered snakes (Öhman and Mineka 2003). Many studies have reported that fear reactions to snakes are observed with a wide variety of primate species reared in the wild, including white-faced capuchins (Cebus capucinus; Boinski 1988), saddle-back tamarins (Saguinus fuscicollis nigrifrons; Bartecki and Heymann 1987), vervet monkeys (Cercopithecus aethiops; Seyfarth et  al. 1980a, b; Struhsaker 1967), common marmosets (Callithrix jacchus; Barros et  al. 2002), squirrel monkeys (Saimiri sciureus; Murray and King 1973; Levine et al. 1993), and rhesus monkeys (Macaca mulatta; Joslin et al. 1964; Mineka et al. 1980). However, a study in which a small number of laboratory-raised monkeys were presented with snakes showed that the monkeys displayed defensive reactions toward toy and real snakes, but were quickly acclimatized (Mineka 1987; Nelson et al. 2003). Actual experiences with live snakes may alter the monkeys’ fear of snakes. For example, laboratory-born (snake-naïve) squirrel monkeys had elevated plasma cortisol elevations (index of stress) following

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exposure to a live snake, but not following exposure to a moving fish (Levine et al. 1993). However, some studies show that snake-naïve monkeys exhibit fear when presented with snake toys. Captive-born marmosets (Callithrix penicillata) produced alarm calls toward a stuffed rattlesnake (Crotalus durissus) (Barros et al. 2002). Crab-eating macaques (Macaca fascicularis) and tufted capuchins (Cebus apella) reacted fearfully to rubber snakes, although the latter habituated and started manipulating the models in repeated exposures (Vitale et  al. 1991). Snake-naïve mouse lemur (Microcebus murinus) and putatively naïve pig-tailed macaques (Macaca nemestrina) exhibited fear response toward a snake toy over a lizard toy, while the apparent reactions were mild (Weiss et al. 2015). There seems to be an innate preparedness for displaying fear (defensive) reactions toward snakes. Shibasaki, former student of mine, and his colleagues (2014) employed the audio-visual matching paradigm used with babies (DeLoache and LoBue 2009) to investigate whether there is a special association with snakes in monkeys. Sixteen Japanese monkeys born in captivity were placed one by one into an experimental device, and two visual stimuli were presented side by side on a screen placed directly in front of the monkeys (photographs of snakes and flowers). A visual measurement device was used to determine which stimulus the monkeys observed more intently. At this point, either an alarm vocalization from a different individual of the same species or contact calls that were not alarm signals were played from a speaker placed behind the monitor as the photograph was shown. The monkeys’ line of sight during the presentation of the audio stimuli was recorded, and the observation time was calculated. The results from the 16 monkeys were averaged, and there was no difference in observation time between the snake and the flower for the contact call, but when a snake was shown on the left and a flower was shown on the right, the monkeys observed the snake for significantly longer with the alarm vocalization. The opposite arrangement of pictures led to no difference in observation time when presented with the alarm vocalization. In general, the right amygdala responds more strongly to threats, so this was interpreted to mean that when a threatening snake was shown in the left visual field (processed by the right amygdala), the monkeys looked at the snake for longer. These results suggest that when an alarm vocalization is heard, there is a tendency toward more intent visual observation in response to threatening stimuli presented simultaneously or subsequently.

4.4  Attention Bias Toward Snakes in Infants and Adults Not only is there a latent association between frightening stimuli and snakes in infants; they also tend to quickly take notice of snakes. In an experiment with infants aged 9–12 months, LoBue and DeLoache (2009) showed a photo of a snake and a photo of a flower side by side on a large screen and investigated which the infants would look at first. The infants looked at the photo of the snake more quickly than the photo of the flower. They also looked more quickly at pictures of angry faces as

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compared to pictures of smiling faces, demonstrating that infants quickly turn their attention to threatening stimuli and that they already regard snakes as threats. These findings demonstrate that there is a tendency for innately feared stimuli to be noticed more quickly. Another experiment that more precisely measured attention of children aged 3–5 showed that caution toward snakes is already exhibited by preschoolers. Before discussing this experiment, I would like to discuss a study using adult subjects. Numerous previous psychological experiments have shown that snakes and spiders draw attention (Öhman and Mineka 2001). Öhman and colleagues conducted a visual search experiment using color photographs of snakes and spiders as fear-­ relevant stimuli and color photographs of flowers and mushrooms as fear-irrelevant stimuli (Öhman et al. 2001). The photographs were shown in a matrix of four (2 × 2) or nine photos (3 × 3). The matrices included either photographs of a single category (all flowers) or had one photograph (the target) of a different category mixed in (a snake among flowers). The matrices that included the target featured either a fear-irrelevant stimulus among fear-relevant stimuli or vice versa. The participants determined whether the matrix shown consisted entirely of photographs of the same category or whether a photograph from another category was mixed in. Judgments were quicker when a fear-relevant stimulus was included among fear-­ irrelevant stimuli (a snake among flowers) as compared to the opposite case (a flower among snakes). That is, detection occurred more quickly when the target was a snake or a spider than when it was a flower or a mushroom. Moreover, the fact that the time to detect the fear-irrelevant stimuli increased as the size of the matrix grew bigger (from 4 to 9), while the time to detect fear-relevant stimuli did not, suggests that fear-relevant stimuli pop out. Based on these results, the authors concluded that stimuli that were threats in the course of human evolution, such as snakes and spiders, are processed preferentially (i.e., they command attention). LoBue and DeLoache (2008) used this visual search task to investigate whether children aged 3–5 years who had nearly never encountered actual snakes would tend to take notice of snakes. Because it is difficult for children of this age to make judgments using keyboards, the experiment was conducted using touchscreen monitors. For each trial, the children placed their hands at a particular position in front of the screen and as quickly as possible touched a photograph that did not belong to the same category from the 3 × 3 matrix of photographs. When snakes and flowers were each displayed as targets in Experiment 1, snake targets were found more quickly among photographs of flowers than the opposite case. In Experiment 2, pictures of frogs were used instead of flowers, and the time to detect snake and frog targets was compared. These results again showed that snakes were detected more quickly. Experiment 3 compared snakes and caterpillars, both of which are characterized physically by long bodies without legs, but again snakes were detected more quickly. We also conducted a similar experiment targeting children aged 4–6 (Hayakawa et al. 2011). It is known that in adults, detection of snakes is faster for black and white photos of snakes and flowers than for color photos of the same, indicating that color is not the key factor (Flykt 2005). However, it remained unclear what factors are relied upon by children. Therefore, 111 children aged 4–6 were asked to detect

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Fig. 4.2  Mean reaction time of the participants to detect snake or flower targets. Vertical bars represent standard deviations (Hayakawa et al. 2011)

the target, either a flower or a snake, among both black and white and color photos. Snakes were detected more quickly than flowers in both chromatic categories, but with the same target, response time was faster for color than it was for black and white photos (Fig. 4.2). This differs from the result for adults, suggesting that children may rely on different factors to detect snakes (Fig. 4.3). To determine which factors we rely upon to detect snakes, another experiment used black and white photographs of snakes in different positions to investigate the possibility that differences in position are a factor. Specifically, pictures of snakes in attack positions, either coiled or with their heads raised to strike, as well as snakes slithering along in non-attack positions, were presented against the same background flower photographs, and detection times were measured. In an experiment on 74 people, including children aged 3–4 as well as adults, both adults and children aged 3–4 detected photos of snakes in attack positions more quickly than they did photos in non-attack positions (Masataka et al. 2010). This indicates that there is already a tendency to notice snakes’ attack positions at the age of 3. In other words, this suggests the possibility that attention is drawn innately to snakes in attack positions. However, the cause could also be that the snake pictures we placed in the “attack” category displayed other characteristics of the snakes (such as their scales) more strongly. A later chapter will describe the characteristics of snakes that lead humans and primates to detect them (Chap. 7).

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Fig. 4.3  A preschool child is identifying the single snake target among eight flower distractors by touching the snake image on a touch-sensitive monitor (Hayakawa et al. 2011)

4.5  A  ttention Toward Threatening Stimuli Is Regulated by Experience Interestingly, snake and spider specialists do not have negative evaluations of these creatures, but like nonspecialists, they find them more quickly in a visual search task (Purkis and Lipp 2007). However, a negative priming experiment showed that although specialists do not have negative evaluations (i.e., fear) toward these creatures, nonspecialists do. In addition, though specialists have different evaluations of dangerous snakes and spiders and of non-dangerous snakes and spiders with similar appearances (they fear poisonous animals), there is no difference in the evaluations of non-experts who do not know which animals are poisonous. That is, the experiment demonstrated that detection of snakes and spiders occurs automatically, but negative evaluations (fear) are not necessarily automatic. This also suggests that detection speed is independent from evaluations of and feelings toward snakes and spiders (e.g., DeLoache and LoBue 2009; LoBue et al. 2013). However, negative experiences cause threatening stimuli to be detected more efficiently. When Purkis and Lipp (2009) carried out a visual search task after conditioning photos of animals that are not normally threatening (dogs, birds, and fish) with adverse electrical shock stimuli, photos of these animals were detected as quickly as those of snakes and spiders. This suggests that attention toward threatening stimuli grows stronger with experience (LoBue and Rakison 2013). Blanchette (2006) investigated the detection time of modern threatening stimuli (guns and knives) and evolu-

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tionarily threatening stimuli (snakes, spiders) as well as the increase in response time due to the increase in the number of interfering stimuli. Both types of threatening stimuli were detected faster than were non-threatening stimuli, and there was no difference in the increase in response time. Others have obtained the same result, with snakes and spiders detected faster than flowers and mushrooms and guns and syringes detected faster than cups and cellular phones (Brosch and Sharma 2005). This equivalence between phylogenetic and ontogenetic threats could derive from learning of modern threats. However, in this case, nonhuman primates and young children would show differential threat-superiority effects for phylogenetic and ontogenetic threats, because they have little experience of modern threats. LoBue (2010) realized that children do not experience modern threats in the same way. Children in the United States and Japan receive vaccinations prior to entering school, so they think of syringes as painful (frightening). However, they have nearly no opportunities to touch knives and scissors, so they have not directly experienced these as threats. After confirming with parents that their 3-year-old children had had adverse experiences with syringes but not with knives, LoBue had the children complete visual search tasks. In one task, photographs of knives and spoons served as the target and interfering stimuli, respectively, while in the other task photos of syringes and pens played these roles. The results for the two tasks were structurally similar. Interestingly, while syringes were detected faster than pens were, the detection time for knives did not differ from that for spoons. These results indicate that by the age of 3, children learn stimuli with which they have had adverse or threatening experiences and can detect them rapidly.

4.6  Monkeys Quickly Detect Snakes Results showing that syringes are detected quickly suggest the possibility that 3-year-old children detect snakes quicker than flowers due to some form of learning. In other words, it is possible that just as children in their infancy do not show “fear responses” toward snakes, but detect syringes quickly by the age of 3 due to adverse experiences with them, children have already learned fear of snakes by the age of 3 (indirectly, through means such as language) and respond to them sensitively. Babies are exposed to various types of information, and it is not possible to isolate them completely from adverse information about snakes. Thus, it is necessary to confirm whether sensitive responses toward snakes are innate, as predicted by SDT, using animals that have never in their lives come into contact with snakes. My former student Shibasaki and I carried out such an experiment (Shibasaki and Kawai 2009). The monkeys were raised in a laboratory and were investigated whether they would quickly detect snakes despite never having once seen a real one. The experiment used a visual search task in which three Japanese monkeys selected a single target photo of a different type among nine photos arranged in a grid on a computer screen (Figs. 4.4 and 4.5). When the target photo was a snake and the other photos were flowers, detection time was faster (in

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Fig. 4.4  Monkey (Zaq) performing visual search for flower image among snake distractors

Fig. 4.5  Monkey (Zaq) performing visual search for snake image among flower distractors

two out of three monkeys) than when a photo of a flower was the target and all the other photos were snakes. In the next experiment, in order to rule out the possibility that color was a key factor, gray-scale pictures were used, but the same general results were obtained. Response time was in fact faster with gray-scale pictures, and all three monkeys quickly detected the snake (Fig. 4.6). These results suggest that the sensitive response by which snakes are quickly detected is innate and was passed down by a common ancestor, at least in humans and Old World monkeys. This is in complete accordance with the predictions of SDT.

References

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Fig. 4.6  Mean reaction time for locating discrepant snake or flower target among flower or snake distractors, respectively. Each bar in the panels indicates median reaction time of each monkey. Vertical lines depict the 95% confidence interval (CI) (Shibasaki and Kawai 2009)

Further, the monkeys did not show a fear response when they saw the photos of snakes. This also demonstrates a disparity between fear as a subjective experience and rapid (automatic) processing of threatening stimuli. This suggests that, as described in Chap. 3, detection of snakes is processed by a subcortical system. These experimental results demonstrate that the attention bias toward snakes is already present at the age of 3. However, none of the monkeys or children showed any resistance to touching the photos of snakes. In other words, they did not demonstrate fear reactions to photographs of snakes, but they did detect snakes quickly. These results generally support SDT, suggesting that although laboratory-­ raised monkeys and young children do not show fear toward snake pictures (probably because fear of snakes is a type of “psychological experience” processed in the cortex), even monkeys that have never seen a snake and children who have rarely in their lives experienced them are equipped with a visual system that detects snakes, suggesting the existence of an innate snake detection mechanism (see Chap. 8 for the discussion of the monkeys’ fear responses toward actual snakes).

References Barros M, Boere V, Mello EL Jr, Tomaz C (2002) Reactions to potential predators in captive-born marmosets (Callithrix penicillata). Int J Primatol 23:443–454 Bartecki U, Heymann EW (1987) Field observation of snake-mobbing in a group of saddle-­ back tamarins, Saguinus fuscicollis nigrifrons. Folia Primatol 48:199–202. https://doi. org/10.1159/000156296

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Blanchette I (2006) Snakes, spiders, guns, and syringes: how specific are evolutionary constraints on the detection of threatening stimuli? Q J Exp Psychol 59:1484–1504. https://doi. org/10.1080/02724980543000204 Boinski S (1988) Use of a club by a wild white-faced capuchin (Cebus capucinus) to attack a venomous snake (Bothrops asper). Am J  Primatol 14:177–179. https://doi.org/10.1002/ ajp.1350140208 Brosch T, Sharma D (2005) The role of fear-relevant stimuli in visual search: a comparison of phylogenetic and ontogenetic stimuli. Emotion 5:360–364. https://doi.org/10.1037/1528-3542.5.3.360 DeLoache J, LoBue V (2009) The narrow fellow in the grass: human infants associate snakes and fear. Dev Sci 12:201–207. https://doi.org/10.1111/j.1467-7687.2008.00753.x DeLoache JS, Pierroutsakos SL, Uttal DH, Rosengren KS, Gottlieb A (1998) Grasping the nature of pictures. Psychol Sci 9:205–210. https://doi.org/10.1111/1467-9280.00039 Flykt A (2005) Visual search with biological threat stimuli: accuracy, reaction times, and heart rate changes. Emotion 5:349–353. https://doi.org/10.1037/1528-3542.5.3.349 Hayakawa S, Kawai N, Masataka N (2011) The influence of color on snake detection in visual search in human children. Sci Rep 1:80. https://doi.org/10.1038/srep00080 Isbell LA (2006) Snakes as agents of evolutionary change in primate brains. J Hum Evol 51(1):1–35 Joslin J, Fletcher H, Emlen J (1964) A comparison of the responses to snakes of lab- and wild-reared rhesus monkeys. Anim Behav 12:348–352. https://doi.org/10.1016/0003-3472(64)90023-5 Levine S, Atha K, Wiener SG (1993) Early experience effects on the development of fear in the squirrel monkey. Behav Neural Biol 60:225–233. https://doi.org/10.1016/01631047(93)90428-K LoBue V (2010) What’s so scary about needles and knives? Examining the role of experience in threat detection. Cognit Emot 24:180–187. https://doi.org/10.1080/02699930802542308 LoBue V, DeLoache JS (2008) Detecting the snake in the grass: attention to fear-­ relevant stimuli by adults and young children. Psychol Sci 19:284–289. https://doi. org/10.1111/j.1467-9280.2008.02081.x LoBue V, DeLoache JS (2009) Superior detection of threat-relevant stimuli in infancy. Dev Sci 13:221–228. https://doi.org/10.1111/j.1467-7687.2009.00872.x LoBue V, Rakison DH (2013) What we fear most: a developmental advantage for threat-relevant stimuli. Dev Rev 33:285–303. https://doi.org/10.1016/j.dr.2013.07.005 LoBue V, Bloom Pickard M, Sherman K, Axford C, DeLoache JS (2013) Young children’s interest in live animals. Br J Dev Psychol 31:57–69. https://doi.org/10.1111/j.2044-835X.2012.02078.x Masataka N, Hayakawa S, Kawai N (2010) Human young children as well as adults demonstrate ‘superior’ rapid snake detection when typical striking posture is displayed by the snake. PLoS One 5:e15122. https://doi.org/10.1371/journal.pone.0015122 Mineka S (1987) A primate model of phobic fears. In: Eysenck H, Martin I (eds) Theoretical foundations of behavior therapy. Plenum Press, New York, pp 81–111 Mineka S, Keir R, Price V (1980) Fear of snakes in wild- and lab-reared rhesus monkeys (Macaca Mulatta). Anim Learn Behav 8:653–663. https://doi.org/10.3758/BF03197783 Murray SG, King JE (1973) Snake avoidance in feral and laboratory reared squirrel monkeys. Behaviour 47:281–289 Nelson EE, Shelton SE, Kalin NH (2003) Individual differences in the responses of naive rhesus monkeys to snakes. Emotion 3:3–11. https://doi.org/10.1037/1528-3542.3.1.3 Öhman A, Mineka S (2001) Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol Rev 108:483–522. https://doi.org/10.1037//0033-295X.108.3.483 Öhman A, Mineka S (2003) The malicious serpent: snakes as a prototypical stimulus for an evolved module of fear. Curr Dir Psychol Sci 12:5–9 Öhman A, Flykt A, Esteves F (2001) Emotion drives attention: detecting the snake in the grass. J Exp Psychol Gen 130:466–478. https://doi.org/10.1037//0096-3445.130.3.466 Purkis HM, Lipp OV (2007) Automatic attention does not equal automatic fear: preferential attention without implicit valence. Emotion 7:314–323. https://doi.org/10.1037/1528-3542.7.2.314

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Chapter 5

Do Snakes Draw Attention More Strongly than Spiders or Other Animals?

Abstract  Adults detect pictures of snakes and spiders more quickly than pictures of mushrooms or flowers. The same holds true for pictures of fear-irrelevant animals which are detected more quickly than pictures of non-animal objects, however pictures of fear-relevant animals (i.e., snakes and spiders) are detected more quickly than are pictures of fear-irrelevant animals (such as birds and fish). In addition, fear-relevant animals (i.e., snakes and spiders) draw attention more strongly than do fearirrelevant but repulsive animals (such as lizards and cockroaches). These results substantiate the fear module theory (Öhman and Mineka, Psychol Rev 108:483–522, 2001). However, recent studies have shown that spiders do not draw attention as strongly as do snakes. We conducted visual search tasks in which fear-­relevant (snakes or spiders) and fear-irrelevant stimuli (Experiment 1, flowers or mushrooms; Experiment 2, koalas or birds) were presented as both target stimuli and distractors (Shibasaki and Kawai 2011). Snakes captured attention more than spiders did when snakes were target stimuli, while snakes held attention more strongly than spiders did when snakes were distractors. A series of studies investigating the attention-drawing power of snakes and spiders under conditions of greater perceptual load indicated that while snakes automatically drew attention even under high perceptual load, the same was not true for spiders. In a visual search study we conducted using monkeys, we found that while snakes were detected among fear-­irrelevant animals (koalas) more quickly than vice versa, the time required to detect spiders among koalas was about the same (i.e., was not faster) than the time required for the reverse. These results deviate from the original fear module theory and instead are consistent with the snake detection theory (SDT), which holds that snakes are the only animals that have always been feared by humans and primates.

© Springer Nature Singapore Pte Ltd. 2019 N. Kawai, The Fear of Snakes, The Science of the Mind, https://doi.org/10.1007/978-981-13-7530-9_5

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5.1  V  isual Search Tasks with Adults: Animals Draw Attention More Strongly than Non-animal Stimuli Many anthropologists, neuroscientists, and psychologists have long considered both snakes and spiders to be innate fear-relevant stimuli for humans (Öhman and Mineka 2001, 2003). Humans form associations between pictures of snakes or spiders and electric shocks more strongly than between pictures of guns or knives and shocks, despite the fact that, in modern environments, guns and knives are more dangerous than snakes and spiders as described in the previous chapters. Öhman and Mineka (2001) postulated that humans are evolutionarily predisposed to process ancestrally fear-relevant stimuli. This fear module hypothesis is also consistent with evidence that humans find pictures of evolutionarily fear-relevant stimuli more quickly than those of neutral stimuli in visual search tasks. Öhman et al. (2001) demonstrated that adult humans more quickly detect a deviant snake or spider picture in a complex array of neutral distractor stimuli (e.g., pictures of flowers or mushrooms) than vice versa. In line with the evolutionary view (Öhman and Mineka 2001, 2003), young children with relatively little prior exposure to snakes or their representations also reacted faster when identifying snakes than flowers (Hayakawa et  al. 2011; LoBue and DeLoache 2008; Masataka et al. 2010), which suggests that prior experience with snakes may not play a major role in enhanced human sensitivity (LoBue and Rakison 2013). Although some studies have suggested that individuals may quickly learn to fear these animals through observations, stories, and/or myths in the early stages of life (LoBue et al. 2010), our recent empirical studies suggest that evolution equipped our ancestors with a readiness to easily associate fear with recurrent threats and with a visual system predisposed to quickly detect dangerous animals (Öhman and Mineka 2001, 2003; Shibasaki and Kawai 2009).

5.2  S  nakes Are Detected More Quickly than Other Threatening Animals However, some studies contest this interpretation. For example, fear-relevant stimuli are detected quickly. In comparisons of speeds of detecting innate (snakes) and learned fear-relevant stimuli (guns), Fox et al. (2007) found that, while neither was detected more quickly than the other, both were detected more efficiently than neutral stimuli (flowers and mushrooms). Studies in which infants (Hayakawa et  al. 2011; LoBue and DeLoache 2008; Masataka et  al. 2010) and monkeys (Shibasaki and Kawai 2009) found snakes quickly in a visual search task have been criticized for not using animals other than snakes, particularly spiders (Soares et al. 2014). In a visual search task similar to the above task, which used pictures of flowers and mushrooms along with pictures of fear-irrelevant animals (horses and cats), Lipp et al. (2004) reported that the pictures of horses and cats were detected more quickly than the pictures of mushrooms and flowers. In another series of visual search experiments, pictures of fear-irrelevant

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animals (horses, cats, dogs, dolphins, etc.) were detected more quickly than pictures of plants (Tipples et  al. 2002). These results suggest that photos of animals are detected more quickly than photos of plants (and mushrooms) regardless of whether the animals are fear-relevant. However, when Lipp et al. (2004) examined the length of time it took 5-year-old children to detect neutral stimuli (rabbits) among silhouettes of fear-relevant (snakes and spiders) and fear-irrelevant stimuli (fish, birds, etc.) as distractors, they found that the fear-relevant stimuli delayed detection longer (in other words, the distraction effect was greater, i.e., they drew greater attention) than did the fear-irrelevant stimuli. Fear-relevant distractor stimuli also had a greater distraction effect when the images were degraded. These findings suggest that children grasp the general characteristics of threatening stimuli and have their attention sufficiently drawn by those stimuli by the age of 5. Threatening stimuli (snakes and guns) are detected more quickly than neutral stimuli, while pictures of animals are detected more quickly than pictures of anything else. A visual search experiment was conducted using pictures of snakes, spiders, birds, and fish (Lipp 2006). They are all animal pictures. In this experiment, pictures of snakes and spiders were detected significantly more quickly than were pictures of birds and fish (fear-­irrelevant animals). In other words, it was confirmed that, in visual processing of pictures of animals, threatening animals (snakes and spiders) are prioritized. Lipp and Waters (2007) also investigated the degree of attention drawn by fear-­ relevant animals (snakes or spiders) and animals that are merely repulsive (lizards or cockroaches). The fear-relevant stimuli and the repulsive stimuli were not directly paired with each other but were instead paired with pictures of neutral animals (birds or fish) that are neither fear-relevant nor repulsive to determine the level of distraction in detection of the neutral target stimuli. For example, a target bird was paired with a snake in one trial and with a lizard in another trial; the two trials were then compared to determine how long detection of the bird was delayed (i.e., whether the snake or the lizard drew attention). When a spider or a cockroach was used as a distractor in detection of a neutral target, detection was slowed more by the spider; similarly, when a snake or a lizard was used as a distractor, detection of the neutral target was slowed more by the snake. In other words, snakes and spiders demonstrate greater power to orient attention. It is indicated that threatening stimuli (snakes and spiders) orient attention more strongly than do similarly unpleasant animals for which biological preparedness is inconceivable (cockroaches and lizards). Developmental studies have also demonstrated that threatening animals capture attention more than do harmless animals. Although 1- to 4-year-old children have greater attentional bias toward both non-dangerous (hamsters, fish) and dangerous animals (snakes, spiders) than to inanimate toys (LoBue et al. 2013), both children (Penkunas and Coss 2013a, b) and adults (Yorzinski et al. 2014), however, detect dangerous animals (snakes and lions) faster than non-dangerous animals (lizards and antelopes). Penkunas and Coss (2013b) conducted a series of visual search task, in which dangerous animals were compared with innocuous animals (snake vs. lizard in Experiment 1; lion vs. antelope in Experiment 2). They found that both pre-

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school children and adults located snakes and lions more quickly than their nonthreatening counterparts. Following this study, Penkunas and Coss (2013a) investigated whether children’s greater attention to dangerous animals (snakes, spiders, and lions) over innocuous animals was enhanced by actual experience of ­dangerous animals in nature. They tested 3- to 8-year-old children from two distinct locations (rural vs. urban) of southern India. In the visual search tasks containing one target image embedded in matrices of eight distractor images, children from both locations detected snake and lion images more quickly than nonthreatening lizard and antelope images, respectively. Neither urban nor rural children displayed a bias for detecting horses versus cows, the latter constituting a familiar animal with strong religious significance. The reaction times of urban and rural children were quite similar, indicating that periodic encounter with dangerous animals early in life did not facilitate better snake and lion detection.

5.3  Are Spiders Dangerous to Humans and Monkeys? It should be noted that snakes belong to the most feared animals both among adults (Arrindell 2000; Davey 1994) and children (DeLoache and LoBue 2009; Morris 1967; Prokop et al. 2009; Tomažič 2011; but see Ballouard et al. 2015) and also present one of the most common causes of human phobias (Davey 1995; Öhman and Mineka 2003). Psychological studies on fear-relevant animals have conventionally used pictures of snakes and spiders as animals that elicit fear (Öhman and Mineka 2001). Snakes and spiders have been used in this manner because they are the most common animal objects of phobias among the general population, and they have been presumed to pose dangers throughout the history of human evolution (Öhman et  al. 1976; Öhman and Mineka 2001, 2003). As snakes, spiders are common objects of phobias (American Psychiatric Association 2013) and are rated as highly frightening in the general population. Agras reported that the prevalence of fear of snakes was 390/1000 population while fear of dentists was only 198/1000 (Agras et al. 1969). Fear of snakes has been studied extensively in many nonhuman primates (Bartecki and Heymann 1987; Boinski 1988; Mineka et  al. 1980; Seyfarth et  al. 1980). In a field study of 11 primate genera, the primates demonstrated alarm calls and other fear-related responses when they encountered snakes (Öhman and Mineka 2003). Snake-naïve laboratory-raised monkeys also detected snakes more quickly than they did flowers in a visual search task (Shibasaki and Kawai 2009). Thus, it is asserted that the phylogenic origin of fear of snakes dates back to early primate evolution (Isbell 2006; Shibasaki and Kawai 2009). However, there are no studies on fear of spiders in primates other than humans; its origin is thus unknown. Of the approximately 38,000 species of spiders, only 0.1–0.3% are venomous species dangerous to humans (Steen et al. 2004), and many venomous species live hidden and scarcely come in contact with humans (Cartwright 2001; Schmidt 1985). Thus, Cartwright (2001) has stated that it is unlikely that these small creatures ever posed any threat to humans during our evolution; although there are a number of more or less serious symptoms attributable to spider bites,

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reported mortality is limited (Isbister and White 2004). In fact, even for the Australian funnel web spider—considered the most venomous of all spiders— “bites are uncommon and severe envenoming even less common” (Isbister and White 2004, p. 485). In addition, because spiders primarily prey on insects rather than mammals (Nyffler 1999), the case for an evolutionary origin of spider fear is clearly weaker than that for snakes (Kawai and Koda 2016). However, fear of spiders is reported to be one of the most prevalent animal fear, and there is no doubt that spiders are abhorrent to humans (Jacobi et  al. 2004). When Gerdes et al. showed pictures of spiders, bees, beetles, and butterflies to college students and had the students assess the pictures in terms of fear, disgust, and perceived danger, spiders were rated the highest in every category (Gerdes et  al. 2009). Bees are encountered more often by humans than spiders, and bees often appear in groups; therefore, objectively speaking, bees are more dangerous than spiders. Nevertheless, spiders were assessed as being more dangerous than bees, which may indicate that spiders have some special significance for humans (Gerdes et al. 2009; Vetter and Visscher 1998). As stated earlier, cockroaches and lizards do not draw as much attention as do snakes and spiders. Although human adults have been shown to quickly detect deviant spider pictures among an array of mushroom pictures, this attention bias disappeared when the deviant spider pictures were embedded among animal pictures (LoBue 2010; see also Öhman et al. 2012; Shibasaki and Kawai 2011). Therefore, it is possible that spiders do not draw attention as strongly as snakes.

5.4  Attentional Capture and Hold in Visual Search Tasks Attention comprises several sub-processes. Visual search tasks involve at least two of these processes: capturing and holding attention (Gerdes et al. 2008). Stimuli that strongly capture attention can orient attention quickly; with stimuli that strongly hold attention, it is difficult to disengage attention (Salemink et al. 2007). Although many studies have demonstrated that snakes and spiders are detected more quickly than flowers and mushrooms, these studies have only dealt with the process of attentional capture; they have not considered results from the perspective of attentional hold. However, in visual search task trials in which a target is presented, is it that snakes as targets strongly capture attention or that snakes as background stimuli with flowers as targets hold attention strongly, thereby delaying detection of the flower? The answer is unclear. It is conceivable that, as distractors, snakes and spiders delayed detection of flowers and mushrooms. Furthermore, in experiments using visual search tasks, snakes and spiders have been lumped together as animals that elicit fear; however, there may have been a difference in attentional bias between snakes and spiders. In a visual search experiment in which pictures of fruit were consistently used as distractors, speeds of detection were compared for three different target stimuli (snakes, spiders, and mushrooms); snakes and spiders were found to be detected significantly faster than mushrooms (Soares et al. 2009b). Additionally, as the number of stimuli (including distractors) was increased from 6 to 12 to 18, detection of each target took more

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time. However, the slope for detection time was shallower for snakes than for spiders and mushrooms; this result indicates that snakes capture attention more strongly than do spiders and mushrooms, even when perceptual load is high. However, this study did not include an experiment in which snakes and spiders were distractors; therefore, it is unknown whether they would demonstrate any difference in the degree to which they hold attention. In addition, distractors consisted only of fruit; the study did not determine whether snakes are detected more efficiently than spiders when pictures of animals are used as distractors. Therefore, we conducted two visual search experiments (Shibasaki and Kawai 2011): one using pictures of snakes, spiders, flowers, and mushrooms (Experiment 1) and the other using pictures of snakes, spiders, birds, and koalas (Experiment 2). We analyzed the results for snakes and spiders separately to determine whether they differed in their capacity to capture and hold attention.

5.5  How Much Do Snakes and Spiders Hold Attention? Twenty participants (11 men) were presented with four or nine color photographs and asked to determine whether there was one odd item (the target) among them that was of a different category from the others (e.g., one picture of a snake among many pictures of flowers) or whether there was no odd item (e.g., all the pictures are of flowers); the participants responded by pressing a keyboard as fast as they could. Stimuli were presented as a set of fear-relevant stimuli (snakes or spiders) with fear-­ irrelevant stimuli (flowers or mushrooms; snakes and spiders were not paired with each other nor were flowers and mushrooms paired with each other) (Fig. 5.1). When a target was present, the odd item was detected more quickly when it was a fear-relevant stimulus than when it was fear-irrelevant. This is consistent with past results (Öhman et  al. 2001; Shibasaki and Kawai 2009). The target, no matter whether it was a snake or a spider, popped out in nearly the same amount of time

Fig. 5.1  Mean reaction time to detect a discrepant fear-relevant (snake or spider) or fear-irrelevant (flower or mushroom) target among fear-irrelevant or fear-relevant distractors, respectively, in small (2 × 2) and large (3 × 3) stimulus matrices. Vertical bars indicate standard errors of the mean. (a) Fearrelevant target among fear-irrelevant distractors and vice versa. (b) Snake or spider among fear-irrelevant distractors. (c) Fear-irrelevant target among snakes or spiders (Shibasaki and Kawai 2011)

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among four pictures and nine pictures (see Fig. 5.1a). When we separately analyzed trials using a snake and those using a spider as the target, there was no difference between them (see Fig. 5.1b). This may have been due to a floor effect that prevented detection of a difference that should have been observed. For trials using a flower or a mushroom as the target, when we separately analyzed the use of snakes and the use of spiders as a background, we found that detection of the flower or mushroom was slower when snakes were used as the background stimuli. This result indicates that detection of a flower or a mushroom was delayed due to attention being held by a picture of a snake (Fig. 5.1c). So, what about this power to hold attention? When there is no odd item, the participant must confirm the absence of an odd item by searching the pictures one by one. Therefore, the time it takes for the participant to confirm that all of the pictures are of the same category (i.e., there is no target) should be longer when the pictures hold attention more strongly. Obviously, this confirmation takes longer with nine pictures of the same category than with four pictures. This assessment took longer when there was no target (odd item) than when there was a target. Furthermore, the assessment took longer for an array composed of only fear-relevant stimuli with no target than for an array of only fear-irrelevant stimuli (flowers or mushrooms). This result shows that fear-relevant stimuli hold attention more strongly (Fig. 5.2a). Importantly, when we compared assessments with pictures of spiders only versus pictures of snakes only, we found that assessment was slower with pictures of snakes only than with pictures of spiders only with both four-­picture (mean 1159 ms vs. 1050 ms) and nine-picture (mean 1369 ms vs. 1124 ms) arrays (Fig. 5.2b). This result is consistent with the explanation that when an array comprises only fear-relevant stimuli with no target, it takes longer to determine that there is no target because the fear-relevant stimuli presented in the visual search task not only capture attention but also hold it (Brosch and Sharma 2005).

Fig. 5.2  Mean reaction time to judge that a target was not present in each matrix. (a) Matrix of all fear-irrelevant pictures or all fear-relevant pictures. (b) Matrix of all snake pictures or all spider pictures (Shibasaki and Kawai 2011)

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These results also suggest that snakes not only capture attention more strongly than do spiders but also hold attention more strongly (Soares et al. 2009b). As I stated earlier, not only fear-relevant animals but also pictures of other animals are detected more quickly than pictures of plants and mushrooms. Furthermore, among pictures of animals, pictures of snakes and spiders are detected particularly quickly (Tipples et al. 2002; Lipp et al. 2004; Lipp 2006). However, while snakes and spiders demonstrate a difference in their capacity to capture and hold attention, it is unknown whether this difference would also be observed in a visual search experiment using only pictures of animals. It has been reported that, when pictures of animals are used as distractors instead of plants, pictures of snakes and spiders are detected quickly but do not pop out (Lipp 2006). It may be that the use of pictures of animals alone attenuates attentional capture and hold by pictures of snakes and spiders, resulting in a lack of difference between snakes and spiders in comparisons with other animals. Additionally, in Experiment 1, while there was no difference between snakes and spiders in attentional capture (i.e., in detection, Fig. 5.1a), animals were extremely easy to detect among flowers and mushrooms; therefore, the difference may have been concealed by a floor effect. In other words, it is conceivable that, when background stimuli consist of animals rather than flowers or mushrooms, snakes and spiders are difficult to detect, thereby making the concealed difference apparent. Therefore, in Experiment 2, we conducted a visual search task using color photos of snakes and spiders as fear-relevant stimuli and color photos of koalas and birds and fear-irrelevant stimuli. First, we examined whether the fear-relevant stimuli would be detected more quickly than the fear-irrelevant stimuli as well as whether there would be a pop-out effect. We also examined whether snakes would continue to hold attention more strongly than spiders when birds and koalas were used as background stimuli. Seventeen individuals (6 men) participated in an experiment with the same procedure as previously described but with birds and koalas in place of flowers and mushrooms. Although the fear-relevant stimuli were detected more quickly than the fear-irrelevant stimuli, response time increased with the number of stimuli, even for fear-relevant stimuli; further, no pop-out effect was observed (Fig. 5.3a). However, when a snake was the target, detection was quicker than when the target was a spider, thus confirming our prediction (Fig. 5.3b). As with Experiment 1, when a bird or koala was the target, detection was slower when the background was snakes than when the background was spiders (Fig. 5.3c). Furthermore, in trials with no target, assessments took longer for arrays comprising only fear-relevant stimuli than for arrays composed of only fear-irrelevant stimuli (Fig. 5.4a). In addition, assessment was slower for arrays with only snakes than for arrays with only spiders, while assessment became slower as the number of stimuli increased, confirming the results of Experiment  1 (Fig.  5.4b). The interaction between the type and number of stimuli was also significant. As the number of stimuli increased (i.e., as the perceptual load increased), the attentional capture effect of snakes also increased. In other words, it was indicated that snakes hold attention more strongly than do spiders (Fig. 5.4b).

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Fig. 5.3  Mean reaction time to detect a discrepant fear-relevant (snake or spider) or fear-irrelevant (bird or koala) target among fear-irrelevant or fear-relevant distractors, respectively, in small (2 × 2) and large (3 × 3) stimulus matrices. (a) Fear-relevant target among fear-irrelevant distractors and vice versa. (b) Snake or spider among fear-irrelevant distractors. (c) Fear-irrelevant target among snakes or spiders (Shibasaki and Kawai 2011)

Fig. 5.4  Mean reaction time to judge that a target was not present in each matrix. (a) Matrix of all fear-irrelevant pictures or all fear-relevant pictures. (b) Matrix of all snake pictures or all spider pictures (Shibasaki and Kawai 2011)

In summary of our study (Shibawaki and Kawai 2011), as with Experiment 1, detection of fear-relevant stimuli among fear-irrelevant stimuli was quicker than when reversed, thus confirming a search asymmetry between fear-relevant and fear-­ irrelevant stimuli. However, when the number of distractors was increased, the detection slope steepened for not only fear-irrelevant but also fear-relevant stimuli. Thus, it was indicated that, when the distractors were other animals, snakes and spiders ceased to pop out. Additionally, in trials with no target, the time required to determine that there was no target increased significantly as the number of stimuli increased, regardless of the type of stimuli; however, this assessment was significantly slower when the array was composed of fear-relevant stimuli. Between snakes and spiders, it was confirmed that snakes not only capture attention more strongly but also hold it more strongly.

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There was no significant difference between detection of snakes and spiders in Experiment 1; however, in Experiment 2, in which snakes and spiders were compared with other animals, snakes were detected significantly more quickly than were spiders. These results indicate that Experiment 1 may have featured a floor effect. As stated earlier, in a study in which flowers and mushrooms were consistently used as distractors and which compared snakes/spiders with horses/cats as targets, there was no significant difference in detection (i.e., attentional capture) speed (Lipp et al. 2004). However, in this study as well, there may have been a latent difference concealed by a floor effect. Thus far, studies (Flykt 2005; Lipp 2006; Lipp et al. 2004; Öhman et al. 2001) have shown that, when an array consists only of fear-relevant stimuli, determining that there is no target takes less time while other studies (Brosch and Sharma 2005; Tipples et al. 2002) have shown that, under these same conditions, this assessment takes longer. These two types of studies offer different explanations for their respective results. For studies finding that an array composed entirely of fear-relevant stimuli yields quicker assessments, authors explained that fear-relevant stimuli, such as snakes and spiders, activate the amygdala (Öhman et  al. 2007), thereby facilitating perceptual processing (Phelps et al. 2006). On the other hand, for studies finding that assessment is delayed, authors explained that fear-relevant stimuli hold attention more strongly than do fear-irrelevant stimuli (Forbes et al. 2011; Lipp and Waters 2007), thereby inhibiting visual search. The latter explanation holds that, when the target is a fear-irrelevant stimulus, the snakes and spiders used as distractors inhibit visual search, thereby delaying target detection. In a visual search experiment (Rinck et al. 2005) that consistently used a dragonfly as a neutral target and spiders, beetles, and butterflies as distractors, when spiders were used as distractors, detection of the dragonfly was significantly slower among the spider-fearful group than among the non-anxious group. In another experiment in which participants assessed a schematic face presented in the center while ignoring a flanking face presented on either side with a different emotional expression, the distraction effect was greater when the flanking distractor faces showed negative expressions (Fenske and Eastwood 2003). In yet another visual search task, detection of a happy face was delayed when angry faces were used as distractors (Horstmann et al. 2006). These results are consistent with the explanation that fear-relevant stimuli as distractors hold attention, thereby delaying detection of the target. However, there are not many results that support the former explanation. Studies (Pflugshaupt et al. 2005; Rinck and Becker 2006) analyzing eye movements with regard to pictures of spiders have shown that after humans’ attention is captured by a spider, they then quickly avert their gaze. Although it is unknown whether humans would also quickly avert their gaze from pictures of snakes, the present study suggests that it would be even more difficult to avert one’s gaze from a snake than from a spider. Although one study (LoBue et al. 2014) examined eye movements in a visual search task involving snakes/spiders and flowers/mushrooms, this study did not analyze snakes and spiders independent of one another. Animal phobias are generally the product of predation defense systems originating from early mammals’ fear of predators (Öhman and Mineka 2001, 2003); fear of snakes

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is considered to be in the “fear of predators” category (Soares et al. 2009a, b). When an animal encounters a predator, freezing to avoid discovery is an effective strategy (Fanselow 1994); the difficulty in averting one’s gaze quickly from a snake may be a form of freezing.

5.6  S  nakes Draw Attention Automatically Even When Perceptual Load Is High, But Spiders Do Not Soares et  al. (2009a, b) compared spider- and snake-fearful human participants using a visual search task. Although spider-fearful participants more quickly detected their feared stimuli (spiders) against a background of fruit pictures than fear-relevant but non-feared stimuli (snakes), there was no significant difference between the detection latencies of the feared stimuli (snakes) and the fear-relevant but non-feared animal stimuli (spiders) for participants fearful of snakes. The authors’ interpretation of these results is that fear was highly specific, whereas snake fear was associated with a more generalized enhanced evaluation of all negative stimuli (thus, spider detection by the snake-fear participants were facilitated). Therefore, to determine whether snakes strongly draw attention via bottom-up processes, Soares and colleagues (2014) conducted experiments comparing snakes and spiders under conditions of high perceptual load. The study comprised four experiments. Experiments 1 and 2 featured a visual search task in which participants were instructed to determine whether a single target (a snake, a spider, or a mushroom) was present among pictures of distractors (fruits). In Experiment 1, which included 57 participants, a short stimulus duration was established as the high perceptual load condition. Of the 288 trials, half did not contain a target while the other half did; in these latter 144 trials, a snake, a spider, and a mushroom were used as the target in one third of the trials each. In each condition, the stimulus duration was 300, 600, or 1200 ms, while the stimulus set size was either four or eight pictures. It was found that detection was quicker as stimulus duration was shorter. With a duration of 600 ms, both snakes and spiders were detected more quickly than mushrooms, whereas with a duration of 300 ms, snakes were detected significantly faster than spiders or mushrooms. This result is consistent with our own study (Shibasaki and Kawai 2011), in which snakes were detected more quickly than spiders. Detection accuracy was not associated with stimulus duration or set size, while snakes were detected significantly more accurately than mushrooms (snakes were detected significantly more accurately than spiders with a duration of 300 ms and a set size of four pictures). In their Experiment 2, the spatial position of the target was manipulated. The 42 participants focused on a fixation point in the center of the screen prior to stimulus presentation; stimuli were then presented in various positions in an imaginary 6 × 6 grid (36 rectangles). The target was presented in a foveal (the central 2 × 2 grid: less than 1.2 visual angle), parafoveal (the paracentral 12 cells of 4 × 4 grid: less than 3.4

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visual angle), or peripheral (the outer 20 cells: less than 5.7 visual angle) location. Each set consisted of 3, 6, 12, or 18 pictures. The increases in reaction time per additional stimulus (i.e., slopes) were calculated and analyzed. Targets presented in the foveal and parafoveal locations were detected at a faster speed (ms/item) than targets presented in peripheral locations, while the detection speed (ms/item) for snakes was significantly faster than for spiders or mushrooms. There was no difference in the detection speed (ms/item) for snakes between foveal and peripheral locations. Experiment 3 examined whether snakes would draw attention even when they were irrelevant to the target. In this experiment, the target was a bird, while the background stimuli were fruits. In some trials, all pictures of fruits were the same (homogenous condition), while in other trials, all pictures of fruits were different (heterogeneous condition). In some trials, a picture of a snake, a spider, or a mushroom was inserted as distractors among the background stimuli. For the homogenous and heterogeneous conditions, comparisons were made between two groups (n = 57). It was hypothesized that even among cluttered scenes, snakes would draw more attention (i.e., delay detection of the target for longer) than spiders or mushrooms. With stimulus sets of six pictures, in both the homogenous and heterogeneous conditions, detection of the target (a bird) was slower in trials in which a snake was presented than in trials in which a spider, mushroom, or no distractor was presented. However, with stimulus sets of four pictures, in both the homogenous and heterogeneous conditions, detection of the target was significantly slower with a spider as a distractor than with no distractor (there were no differences between spiders and other stimuli). Experiment 4 examined whether snakes would automatically draw attention under an even higher perceptual load. The task in this experiment was to determine which of two letters (X or N) was displayed. The objective was to examine the distraction effects of snakes and other stimuli under different perceptual load conditions. In the low perceptual load condition, the letter X or N was presented alone. In the high perceptual load condition, the target letter was surrounded by five other letters in a 2 × 3 array; the position of the target among the six letters was randomized for each trial. In 20% of the trials for both conditions, a snake, spider, or mushroom was presented in the peripheral area, 9.5° vertically or horizontally from the center (the fixation point), to examine their distraction effects. In overall analysis, some main effects were observed, while there was no main effect of distractor and no interaction effect of perceptual load and distractor. However, when contrast comparisons were used, assessment of the target letter was found to be significantly slower in trials in which a snake was presented than in trials with no distractor. No such effects were observed for any other distractors. Thus, even when utilizing top-­ down processing (in this case, to assess certain letters), a picture of a snake presented in a peripheral location interfered with that processing. In other words, snakes were suggested to draw attention in a bottom-up fashion. To summarize these results, snakes were shown to automatically draw attention even under high perceptual load conditions (short stimulus duration, large numbers of stimuli, presentation of the target in the peripheral area, cluttered scenes, interference in top-down processing), whereas the same was shown not to be true for spi-

5.7 Cultural Differences in the Fear of Spiders

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ders. In these experiments, the pictures of snakes and spiders were controlled for mean brightness and contrast, and there was no difference in power at any range of spatial frequency. These results are in accord with our own study (Shibasaki and Kawai 2011), suggesting that only snakes draw special attention.

5.7  Cultural Differences in the Fear of Spiders Davey (2002) proposed that evolutionary threats like snakes, spiders, heights, water, and enclosed spaces might cause specific bodily sensations, and it is the misinterpretation of these sensations that leads to the heightened prevalence of such fears. In other words, the mechanism that ensures that these stimuli are most likely to become fear-relevant is bodily or perceptual. In our research, target snakes have been detected more quickly than spiders. Target detection speed is considered to reflect the degree of fear of the target (Öhman et al. 2001; Rinck et al. 2005; Soares et  al. 2009a); therefore, it is possible that the participants in these experiments merely had greater fear of snakes than of spiders. However, in an experiment by Soares et al. (2014) that found no difference in the degree of fear of snakes and fear of spiders, they still found that snakes were detected more quickly than spiders. These results demonstrate that the difference in the degrees of fear of snakes and spiders does not necessarily determine their respective power to draw attention. However, spider-fearful people detect spiders more quickly than they do snakes, while snake-fearful people detect snakes more quickly than they do spiders (Flykt and Caldara 2006; Öhman et al. 2001; Rinck et al. 2005 but see Soares et al. 2009b). Therefore, there is still some possibility that the individual’s level of fear of the target animal affects their visual searches (regardless of the fact snakes draw attention strongly). In Japan, there are several species of lethally venomous snakes but no venomous spiders. It may be that many Japanese people are aware of these facts and therefore do not fear spiders as strongly as they do snakes, resulting in attenuation of attention to spiders. Many Japanese, at least my family, do not kill a spider but release it, when we find it in our house. Because we have learned Budda’s tale on spider ‘Spider’s thead’ by Ryunosuke Akutagawa: “… Budda noticed a man named Kandata is wriglling with other sinners in the bottom of Hell. This man, Kandata was a sinner who comitted murder, set fire to a house, and did various evel deeds. But he did a good deed once in his life. The reason is like this. One day, when Kandata was passing through a deep woods, he saw a small spider was crawling on the ground. Kandata lifted up his foot immediately and tryed to stamp a spider, but he suddenly reconsidered. “No, no. It is so small but a living thing. It is so cruel to take away spider’s life thoughtlessly. “ And he didn’t kill a small spider and saved it. Budda remembered that Kandata saved a small spider while he was looking down to Hell. And he thought he is going to try helping Kandata out of Hell for his good deed. Fortunately, a heaven’s spider was weaving its web with a beautiful silver thread on a jade colored lotus leaf by his side. Budda took a spider’s thread softly, and put it down straight to the bottom of Hell through white lotuses. People in India are said to have

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less fear of spiders than do people from other countries (the UK, Netherlands, the US, Korea, China, and Japan) (Davey et al. 1998). As these examples show, there are cultural differences in the level of fear of spiders. Therefore, it is asserted that fear of spiders is not a biologically prepared characteristic but is, instead, propagated culturally (Cartwright 2001; Steen et al. 2004). For example, Davey (Davey 1994; Davey et al. 1998) surmised that spiders have been considered to be deeply associated with disease epidemics since the tenth century in Europe and that cultural propagation of this association through the present day has created a susceptibility to fear of spiders (Gerdes et al. 2009). It is also held that fear of snakes and fear of spiders differ in quality (Soares et al. 2009b); it is said that, whereas fear of snakes is a fear of being bitten, fear of spiders is rooted in disgust related to inflection of disease (Matchett and Davey 1991). Going by this theory, it is conceivable that the origin of the fear of spiders is far shallower than the origin of the fear of snakes.

5.8  Is Fear of Spiders Learned? The presence of cultural differences in fear of spiders suggests that the fear is learned. One study suggests that fear of spiders is a threat acquired in the process of evolution and that attention is directed toward a template for spiders beginning in early development (Rakison and Derringer 2008). In this study, which used preferential looking and habituation paradigms, when 5-month-old children were presented with a schematic spider (a silhouette of a spider), a schematic spider with reconfigured features, and a completely scrambled image of a spider, the children looked at the schematic spider for longer than the other two images (Fig.  5.5). However, when these images were composed of simple rectangles, the schematic Fig. 5.5 Schematic images of spiders comparable to those used in Rakison and Derringer (2008). The left images are schematic spiders, and the right images are reconfigured features. The similar figures on the upper panel were used in their Experiment 1, and those on the lower panel were used in their Experiment 2. Illustration by Nobuyuki Kawai

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spider, which had eight legs and two parts comprising the body, drew the same level of attention as the spider with reconfigured features and the completely scrambled spider. With flowers as well, a schematic flower did not draw significantly more attention than did a flower with reconfigured features or a completely scrambled flower. Therefore, the authors asserted that infants are equipped with a mechanism to detect (the “perceptual templates” of) spiders and other animals that posed potential threats over the course of evolution. However, the study only compared spiders to other forms of spiders and flowers to other flowers; there is no proof that infants at that age direct their attention more toward templates of spiders than to templates of flowers or other animals. LoBue (2010) conducted visual search experiments with 3-year-old children and with adults to investigate whether spiders are detected more quickly than ­mushrooms (Experiment 1) and cockroaches (Experiment 2). Both preschoolers and adults detected spiders among mushrooms and cockroaches more quickly than vice versa. For adults, there was no difference between detection of spiders and of cockroaches. Although children detected spiders more quickly than they did cockroaches, this result only reflects that the children detected the threatening animal more quickly; it does not prove that spiders draw attention as strongly as snakes. On the other hand, there are several pieces of evidence that snakes begin drawing attention in infancy. Thrasher and LoBue (2016) replicated a previous study by one of the authors in which they determined whether snakes strongly drew attention from 6- to 9-month-olds while measuring their physiological responses. A video clip of a snake or an elephant (each 6 seconds in duration) was presented on a screen along with either a fearful voice or a happy voice; the authors then examined which image the infants looked at for longer. During video presentation, a stimulus that would provoke a startle response (presentation of a bright flash of light or a burst of loud noise for 50 ms) was presented twice; the authors examined the infants’ resulting startle response (via electromyography) and changes in heart rate. As in the previous study (DeLoache and LoBue 2009), the infants looked at (i.e., paid attention to) snakes with a fearful voice for a long time. The infants demonstrated a particularly quick startle response and a low heart rate (indicators of directing attention) at a startling stimulus during presentation of an image of a snake paired with a fearful voice. However, there was no difference in startle magnitude (which is considered to indicate fear) between presentation of a snake with a happy voice and presentation of an elephant with either a happy or fearful voice. Infants at that age are likely incapable of labelling snakes as scary. This assumption is supported by the fact that infants do not show any evidence of actual fear when exposed to videos of moving snakes (DeLoache and LoBue 2009; Thrasher and LoBue 2016). While snakes capture infants’ attention, snakes are not necessarily an aversive or negatively valenced stimulus for infants or young children. Thus, infants merely direct their attention toward snakes due to low-level visual features characterizing snakes (Bertels et al. 2017; LoBue et al. 2010; Rakison and Derringer 2008), such as their curvy shapes (e.g., LoBue 2014; LoBue and DeLoache 2011; LoBue et al. 2017) or scales (Van Strien and Isbell 2017; Kawai and He 2016) without actually feeling any fear.

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5.9  M  onkeys Quickly Find Pictures of Snakes, But Not Pictures of Spiders, Among Pictures of Non-threatening Animals Although studies on human infants have shown that infants direct attention toward snakes by 1 year old, there is no evidence that infants direct attention toward spiders or other threatening animals (though they do direct more attention to spiders than toward flowers, mushrooms, or cockroaches). While these results suggest the existence of an innate threat detection mechanism with regard to snakes, it cannot be ruled out that fear of snakes may be acquired completely through experience. Studies on primates have shown that wild monkeys fear snakes and that monkeys raised in captivity, which are snake-naive, come to fear them through vicarious learning (Mineka et al. 1980) and can find a picture of a snake among pictures of flowers more quickly than the reverse (Shibasaki and Kawai 2009). However, no studies have observed monkeys to fear spiders or direct their attention toward spiders. Using monkeys, we previously conducted a visual search study that strongly indicated that primates possess an innate snake detection mechanism (Shibasaki and Kawai 2009); however, it is also possible that animals were simply detected more quickly than flowers (Soares et al. 2014). Among others, spiders may be quickly detected by monkeys. If monkeys detect spider pictures efficiently among mushroom pictures, however, it does not mean that spiders are evolutionarily fear-­relevant animals, because pictures of fear-irrelevant animals were also quickly found among flower or mushroom pictures by humans (Lipp et al. 2004). These results do not support the notion that spiders are processed pre-attentively in visual systems. We therefore conducted an experiment using three different monkeys from those mentioned above to determine whether snakes are detected more quickly than other animals as well as whether spiders are similarly detected more quickly than other animals (Kawai and Koda 2016). Three Japanese monkeys learned to perform a visual search task that involved choosing a single odd stimulus from among nine gray-scale pictures displayed on a touchscreen (Fig.  5.6). These monkeys were raised in captivity and were snakenaïve. After the monkeys learned to search for an odd animal among pictures of other animals, we conducted an experiment using snakes and non-threatening animals (koalas). Once the monkeys identified the odd item correctly at least 95% of the time for at least three consecutive days, we analyzed data for the following 6 days (total 432 trials). In Experiment 1, the monkeys were shown snakes and koalas; in Experiment 2, the monkeys were shown spiders and koalas. All three monkeys detected a single snake among koalas more quickly than vice versa. This result not only replicates the previous result with another monkeys but also rules out the argument that animals are merely detected quickly. However, in Experiment 2, none of the three monkeys demonstrated a difference in their speeds at detecting spiders and koalas. These results demonstrate not only that spiders are not necessarily detected more quickly than other animals but also that threatening animals are not necessarily detected more quickly than non-threatening animals (Fig. 5.7). This pat-

Fig. 5.6  Stimuli used in Kawai and Koda (2016). The koala target is presented among snake distractors (a) and among spider distractors (c). The snake target is presented among koala distractors (b)

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Fig. 5.7  Median reaction times for locating discrepant target picture among distractor pictures. The asterisks indicate p-values (∗ p