The Coevolution of Language, Teaching, and Civil Discourse Among Humans: Our Family Business [1st ed.] 9783030485429, 9783030485436

Table of contents :
Front Matter ....Pages i-xxi
Prologue: The Strangest Story Ever (Donald M. Morrison)....Pages 1-5
Teaching in Humans and Other Animals (Donald M. Morrison)....Pages 7-22
Not a “Third Chimpanzee” (Donald M. Morrison)....Pages 23-56
An Evolutionary Explosion (Donald M. Morrison)....Pages 57-84
The Coevolution of Language, Brains, and Technology (Donald M. Morrison)....Pages 85-119
Pointing: The Royal Road to Language? (Donald M. Morrison)....Pages 121-163
Teaching from Childhood to Adulthood (Donald M. Morrison)....Pages 165-203
Teaching and Learning as Language in Action (Donald M. Morrison)....Pages 205-228
Civil Discourse: Thinking with Other Humans (Donald M. Morrison)....Pages 229-266
The Emergence of Civil Discourse (Donald M. Morrison)....Pages 267-291
Into the Uncertain Future (Donald M. Morrison)....Pages 293-329
Epilogue: An Invitation (Donald M. Morrison)....Pages 331-334
Back Matter ....Pages 335-360

Citation preview

The Coevolution of Language, Teaching, and Civil Discourse Among Humans Our Family Business d on a l d m . mor r i son

The Coevolution of Language, Teaching, and Civil Discourse Among Humans

Donald M. Morrison

The Coevolution of Language, Teaching, and Civil Discourse Among Humans Our Family Business

Donald M. Morrison Institute for Intelligent Systems University of Memphis Memphis, TN, USA

ISBN 978-3-030-48542-9 ISBN 978-3-030-48543-6 https://doi.org/10.1007/978-3-030-48543-6

(eBook)

© The Editor(s) (if applicable) and The Author(s) 2020 This work is subject to copyright. All rights are solely and exclusively licensed 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 Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Dedicated to my father Donald Harvard Morrison (1914–1959)

…and all the other teachers in our human family, past, present, and future.

Preface

Teaching is our family business. Early in the last century, my paternal grandfather, Louis French Morrison (1880–1961), was assistant superintendent of schools in Morgantown, West Virginia. His first son, my uncle Wilbur Yale Morrison, taught shop for many years in the Morgantown public schools. His second son, my father, Donald Harvard Morrison (note the choice of middle names) was a professor of Government at Dartmouth College, dean of the faculty, and the College’s first provost. On my mother’s side, my aunt, Jane Gibson Likert, taught high school English in Grand Rapids, Michigan. According to family lore, Gerald Ford, 38th president of the United States, was her student. Her husband, my uncle Rensis Likert, helped found the Institute for Social Research at the University of Michigan. Of my seven brothers and sisters, five (myself included) have been paid to teach. Two are college professors, one is recently retired from teaching English at a private school in Connecticut, and another served as chairman of the department of surgery at a medical school in the Pacific Northwest. Now, I’ll bet a cookie—a thousand!—that you have a teacher or two in your own family. The reason for my confidence: Anyone who helps

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people learn is clearly a teacher, regardless of title. Take another of my sisters, a clinical social worker who spends her days helping her clients cope with cancer, divorce, and other such challenges. Or another sister, a master jigsaw-puzzle maker who, among her other jobs, helps her colleagues learn the secrets of trick puzzles, which, confusingly, have more than one solution. Or another brother, owner of a successful auto body shop in Vermont, who, since the 1970s, has taught dozens of employees how to straighten frames, remove dents, and mix paint colors to match. Almost certainly you have teachers like that in your own family. But wait, there’s more to it. Yes, teaching is my own family’s business, and quite likely a big part of yours. But teaching is also the business of our human family. Indeed, as you read this book, I mean to convince you that we and everyone we know and don’t know—all living humans— come from a long line of teachers, going back many thousands of generations; that our present circumstances and predicaments as a species are the result of untold numbers of teaching episodes, stretching as far back as some three million years; and, because teaching is so fundamental to our way of life on Earth, that our fate as a species will depend very much on our ability to continue helping members of upcoming generations acquire the life-sustaining, civilization-sustaining knowledge, skills, and habits of mind that have gotten us this far. Put simply, if we humans— as parents, teachers, colleagues, and friends—don’t dedicate ourselves to teaching, teaching well, and teaching the right things, we’ll soon be out of business. Not much, I hope you’ll agree, is more important than to understand how we’ve come to this. What is teaching? Why is it that humans alone use language to teach? What has teaching brought us, and where might it take us in the future? These are the questions I try to answer in this book. But before you begin reading, I need to post some warnings. For one thing, the account I’m about to give about how we came to be so dependent on teaching is not by any means straightforward and, I must admit, is likely to be partly wrong. If we really want to understand, at a deep level, what human teaching is and how it evolved in our species, we need to grapple with a large number of such arcane, controversial topics as the nature of monkey hunting among chimpanzees; the relationship between

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our diet, technology, and cranial capacity; how humans managed to colonize Australia some 65,000 years ago; and, most problematic of all, the mysterious forces, both biological and cultural, that led to the origin of human language and the evolution of our species. Although scientists in a broad range of relevant disciplines—including primatology, archaeology, anthropology, linguistics, evolutionary biology, and neuroscience—are beginning to converge on at least partial answers to some of the most vexing questions about human origins, the story of human evolution, and the evolution of teaching and learning through language, remains largely a matter of speculation. I hope you will be content with a plausible account, knowing that a definitive one is still out of reach. Finally, while I’ve tried to keep the technical jargon to a minimum, I ask you to bear with me as I explain, and then apply, technical terms such as coevolution, adaptive suite, symbolic reference, disambiguated pointing, niche construction, and—my favorite—Mitteilungsbedürfnis (German for “helpful chattiness”). I hope you’ll find, as I have, that these strange, initially off-putting words can become indispensable symbols for the important concepts they represent. Before you begin reading, you might like to have a look through the glossary, which begins on page 337. Memphis, USA

Donald M. Morrison

Acknowledgments

I could not possibly have written this book without the help and encouragement of numerous friends and family members, conversations with colleagues, and the published work of researchers in a broad range of disciplines. Of the latter, I have benefitted particularly from books and papers by Francisco Aboitiz, Leslie Aiello, Michael Arbib, Derek Bickerton, Barry Bogin, Adam Boyette, Alison Brooks, Jerome Bruner, Tim Caro, Dorothy Cheney, Frederick Coolidge, Gergely Csibra, Richard Dawkins, Terrence Deacon, Dan Dediu, Jared Diamond, Merlin Donald, Robin Dunbar, Tecumseh Fitch, Peter Gärdenfors, György Gergely, Marc Hauser, Suzana Herculano-Houzel, Barry Hewlett, Kim Hill, Anders Högberg, Magdalena Hurtado, François Jacob, Sverker Johansson, Hillard Kaplan, Chris Knight, Deanna Kuhn, Kevin Laland, Stephen Levinson, John Locke, Owen Lovejoy, Katharine MacDonald, Brian MacWhinney, Tetsuro Matsuzawa, Ashley Maynard, Sally McBrearty, Paul Mellars, John Mitani, Thomas Morgan, John Odling-Smee, Stephen Pinker, David Premack, Robert Quinlan, Robert Seyfarth, Chris Sinha,

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Acknowledgments

Dan Sperber, Kim Sterelny, Sid Strauss, Michael Tomasello, Lev Vygotsky, David Watts, Thomas Wynn, and Guy Woodruff. Bill Griffin, Barry Hewlett, Molly Hunter, Brig Klyce, Brian MacWhinney, Chris Sinha, Gail Sansbury, Sid Straus, and Joe Walters gave helpful comments and encouragement on earlier versions of the manuscript. I am particularly indebted to my closest readers: Alan Collins, Trevor Peard, and, especially, Stan Franklin. In addition to reading the entire book and correcting numerous errors, Stan continues to send me nearly daily links to relevant research papers in a broad range of disciplines. Finally, I am grateful to the dozens of teachers who helped shape the contents of my own brain from an early age, including, as a representative sample: my mother, who taught me to tie my shoes, twice; my father, to whom the book is dedicated, who died when I was eleven, but not before teaching me chess, to be kind to strangers, and how to make a kind of firecracker with an old-fashioned barrel key, a nail, a length of string, and some wooden match tips; Benjamin Tsou, who first kindled my interest in language and culture; and Catherine Snow, who fanned the flames.

Contents

1

Prologue: The Strangest Story Ever

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Teaching in Humans and Other Animals What Is Teaching? Do Birds and Bees Do It? The Special Case of Teaching in Humans Food for Thought Further Reading References

7 10 15 19 20 21

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Not a “Third Chimpanzee” The Hominid Family Tree Evolution of Adaptive Suites Evolved Differences Between Chimpanzees and Humans Anatomical Differences Diet and Food Extraction Technologies Social and Sexual Arrangements Group Size and Social Dynamics Chimpanzee Sex

23 25 27 30 31 33 33 35 36

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What’s Love Got to Do with It? Differences in Life History Cognition, Communication, and Collaboration The Case of “Cooperative” Monkey Hunting Chimpanzee and Human Communication Compared A Note on the Issue of “Human Uniqueness” Food for Thought Suggested Reading References

38 41 42 44 49 50 51 53 53

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An Evolutionary Explosion The Participants The Anatomy of an Explosion The Hominin Adaptive Suite Problems and Solutions Bipedalism as a Catalyst? Again, Why Only Us? Foraging, Territory and Diet Habitat, Bipedalism, and Pair Bonding Selection Pressure for Enhanced Cognition Fueling a Supersized Brain: A Missing Piece Teaching as a Biocultural Activity Food for Thought Suggested Reading References

57 58 60 62 65 66 70 71 72 74 77 79 80 81 82

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The Coevolution of Language, Brains, and Technology What Do We Mean by “Language?” The Relationship Between Language and Technology Evolution of Stone Tool Manufacture When Did Language Emerge? The Timing of Language Origins: A Synthesis So That’s When, But How?

85 88 90 92 96 107 112

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Food for Thought Suggested Reading References

113 116 117

6

Pointing: The Royal Road to Language? Tipping Points in Dynamic Systems Disambiguated Pointing as a Tipping Point Language Precursors in Primates Language-Ready Brains Building Selection Pressure The Tipping Point A Spark Falls on a Patch of Dry Grass Runaway Change? A Return to Equilibrium Chapter Summary Food for Thought Suggested Reading References

121 124 128 132 143 145 147 150 152 153 155 157 158 159

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Teaching from Childhood to Adulthood Teaching: Biology or Culture? The Supersized Brain Problem Life in Small-Scale Hunter-Gatherer Societies Age Structure of Hunter-Gather Residential Groups Hunter-Gatherer Childhoods The Ontogeny of Human Teaching and Learning Learning to Hunt in Hunter-Gatherer Cultures From “Natural” to “Culturally-Biased” Pedagogy Does the Ontogeny of Teaching “Recapitulate” Phylogeny? Individual Differences in Natural Pedagogy

165 170 173 175 177 179 181 190 191 194 195

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Food for Thought Suggested Reading References

196 199 199

Teaching and Learning as Language in Action The Anatomy of a Teaching Episode Sample Hypotheses Generated by the Framework Language as Action in a Complex World Evolution of Teaching Tactics, Strategies, and Metastrategies Looking Ahead Food for Thought Suggested Reading References

205 207 210 211

Civil Discourse: Thinking with Other Humans Civil Discourse Defined Civil Discourse and Teaching Civil Discourse and Epistemology Some Important Epistemic Forms How Did Animal Thinking Become Human Thinking? Food for Thought Suggested Reading References

229 232 233 234 238 260 260 262 263

10 The Emergence of Civil Discourse Epistemic Understanding from Infancy Through Adulthood Epistemic Understanding in Evolutionary Time Epistemic Fluency and Cultural Transmission Civil Discourse and the Colonization of Australia The Beginnings of Epistemic Diversity Food for Thought Suggested Reading References

218 223 226 227 227

267 268 274 278 280 286 289 289 290

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Into the Uncertain Future Science, Education, and Politics An Evolutionary “Design Flaw?” Is Formal Education the Answer? Teaching and Learning in the Pleistocene and Now Civil Discourse and Modern Schooling Attempts to Remodel Classroom Talk Technology to the Rescue? The Future of Teaching and Learning with Computers Can Machines Make Us More Intelligent? Discussion Questions Suggested Reading References

293 295 297 300 302 306 308 310 315 319 324 327 327

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Epilogue: An Invitation References

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Glossary

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Index

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List of Figures

Fig. 3.1 Fig. 4.1

Fig. 4.2

Highlights of hominid evolution Hominin adaptive suite. A highly schematic depiction of 26 evolved traits and behaviors that distinguish humans from other species of great ape. The thicker, up-and-down arrows indicate whether the trait has increased or decreased in prominence over time. The thinner, double-ended arrows indicate possible causal relationships; each of the traits, including teaching, is understood to be related, directly or indirectly, to every other (Adapted from Lovejoy 2009) Ancestral habitats, cranial capacity, and degrees of hominin terrestrial bipedalism. The horizontal axis represents (a) the expanding hominin habitat over time, from the fruit-bearing trees of the Miocene African rainforest (the domain of our last common ancestor with chimpanzees), into the woodlands, onto the savannah, then out into the rest of the world, and (b) the estimated timing of the transitions from occasional to habitual to obligatory bipedalism.

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Fig. 5.1 Fig. 5.2

Fig. 7.1 Fig. 8.1 Fig. 10.1

List of Figures

The vertical axis (the shaded graph) represents the growth in hominin brain size, from under well under 500 c.c. in early ancestors—including the last common ancestor of humans and chimps (the “LCA,” hiding in the forest)—to around 1300 c.c. or larger in Denisovans, Neanderthals, and modern humans (Neanderthal brain capacity was slightly larger than the average for modern humans; although no Denisovans skulls have been found to date, their close genetic relationship to Neanderthals suggests cranial capacity was in the same range). Note the step increases in cranial capacity associated with the transition from occasional to habitual bipedalism in the Australopithecus species, and from habitual to obligatory bipedalism in H. habilis Five estimates for the first emergence of human language Evolution of hominin brains, tools, and language. The horizontal axis represents (a) the emergence of increasingly sophisticated (but long-standing) hominin tool industries; and (b) hypothesized step increases in the complexity of language, from primate precursors, through increasingly complex protolanguages, to modern human language. The shaded graph on the vertical axis represents step increases in cranial capacity for representative hominin species A biocultural model of teaching (Tree artwork created by www.freepik.com) Hypothetical evolution of six core teaching strategies over three different time periods Stages in the development of epistemic understanding. Note Estimates for the onset of the multiplist, evaluativist, and inclusivist stages are strictly hypothetical

67 86

107 173 221

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List of Tables

Table 3.1 Table 7.1 Table 7.2 Table 7.3 Table 8.1 Table 11.1

Chimpanzees and humans compared Age structure of a typical hunter-gatherer residential group Age of first participation in adult hunting expeditions for 21 different groups Hunter-gatherer childhood in six traditional cultures An early teaching tool kit? Percentage of subjects generating “genuine evidence” and alternative theories concerning causes of school failure

31 177 178 180 214

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1 Prologue: The Strangest Story Ever

Let’s begin with a story, recognizing it’s just a story… Somewhere in Africa, some 6–10 million years ago, during the late Miocene—a period of gradual cooling and drying marked by shrinking forests and expanding grasslands—two small-brained, somewhat bipedal apes, daughters of the same mother, parted ways. One sister wandered off with a band that ventured deeper into the receding jungle. Partly because her group specialized in gathering the fruits and tender young leaves so freely available in the treetops, their legs grew shorter over time and more powerful, the better for shimmying up tree trunks. Their toes and fingers grew longer, the better for grasping branches, but not so good for walking upright or the fine manipulation of tools. For safety, they slept in trees at night. When they descended to the ground during the day, they retained an ability to walk on two legs for short distances, but, like their cousins the gorillas, they came to rely on an awkward four-legged gait, supporting themselves in front with their knuckles. In sexual matters, females on this sister’s side of the family pursued a clever “have your cake and eat it too” strategy: they mated with multiple partners during periods of relatively low fertility and then grew more © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_1

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selective during their much shorter periods of maximum fertility. In this way, females built useful affiliations with multiple males and at the same time exerted reasonable control over the fate of their own genes. For their part, males competed aggressively with each other for sexual access to willing females, taking on as many as would have them. However, given the paternity confusion resulting from their promiscuity, they had no reason to protect and provision particular females or even their own offspring. As a result, the males remained self-serving bachelors, leaving the females to forage alone for themselves and their young ones. Like many other primates, males and females continued to manipulate and maintain their social relationships with other group members using a combination of snarling, aggressive displays—featuring their enlarged, sharpened canines—and, in their more peaceful hours, strengthening their affiliations by carefully stroking and plucking each other’s scaly, parasite-ridden pelts. They learned (by observing others) how to use rocks and sticks as foraging tools, but they never learned how to sharpen a stone and attach it to the end of a stick. Like all primates, these animals sought to shape each other’s behavior using a rich repertoire of facial expressions, physical gestures, and vocalizations, sometimes for the benefit of others, as in the case of alarm calls, but more often for their own selfish purposes—to obtain food or sex or to frighten away competitors. And for some reason, perhaps for the same reason, it seems these creatures never developed the capacity to peer inside each other’s minds, to imagine the inner life, beliefs, and mental perspectives of another thinking being like themselves, or to consider the difference between their own beliefs and those of others. As a result, like all animals, they continued to be good learners, but, unlike some, they never engaged in anything much resembling intentional instruction. Today, we call this sister’s descendants chimpanzees. The other sister’s band pursued a radically different path—an extraordinary, high-risk evolutionary journey that would eventually take twelve of her descendants to the cratered surface of the moon and back. As the journey started, these creatures began to forsake the relative safety of fruit-bearing trees in the shrinking rainforests for a riskier but potentially more prosperous life as itinerant foragers in the expanding woodlands,

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grassy savannas, river valleys, and other waterside habitats that had begun to appear in the wake of the receding jungles. Along the way, something especially strange happened in the brains and bodies of these increasingly bipedal apes. Instead of mating freely with multiple partners, individual males and females began finding themselves attracted to special someones, often the brainier ones. Forsaking their earlier promiscuity, they began forming increasingly strong, relatively permanent bonds with their lovers, formed extended family groups, and began raising children together. No longer competing directly with other males for access to sex, and thereby freed from the need to keep a watchful eye on all the females in their groups, males came to engage in cooperative, increasingly long-distance foraging expeditions, returning to a base camp at the end of the day with food to share with their mates, children, and extended family members. Partly because of the new social arrangements and division of labor, these creatures became better fed, lived longer, and began to expand their numbers. Most consequentially, nourished by their new diet, and in response to the new cognitive demands of family living, their brains began to grow larger in proportion to the rest of their bodies, eventually, over millions of years, tripling in size. As a result, and under building pressure to feed their burgeoning, energy-hungry brains, these animals, male and female alike, became increasingly skillful, opportunistic omnivores, learning to extract and process the wide variety of highly nutritious but difficult-to-acquire foods they discovered in their expanding territories: underground tubers, shellfish buried in mud, marrow hidden in the bones of scavenged carcasses left unattended by the original killers. And as their brains grew bigger, they began to apply their intelligence in new ways. They learned to exploit the fracture properties of basalt and other volcanic rocks, knocking one rock against another to split off sharp flakes—good for sharpening sticks and stripping flesh from carcasses. Their hands, already equipped with shorter fingers and opposable thumbs, became increasingly adapted for gripping and precisely manipulating these new tools. Eventually, they learned to collectively track down and kill large animals, to control fire, to haft sharpened stones to sticks, to make boats and nets, to manufacture assault rifles.

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In the process, they damaged untold numbers of habitats, and directly or indirectly caused the extinction of untold numbers of other species. Most remarkably, and long before assault rifles, these creatures had somehow grown what amounted to a kind of “third eye”—a highly evolved system of neural circuitry (grown from wiring inherited from their own primate ancestors) which allowed them to peer, with increasing acuity, into the minds of other members of their groups and imagine, however imperfectly, the perspectives and beliefs of a fellow thinker. At the same time, they became increasingly altruistic, willing to share information for the common good. They began directing each other’s attention, by pointing with their fingers, to objects and events of mutual concern in the immediate environment: a leopard in a tree, the tracks of other animals, distant food patches, the best place to hit a rock to strike off a sharp flake. Under pressure to communicate their beliefs and intentions more accurately and efficiently, they began to develop an increasingly sophisticated set of signals, in which certain conventionalized combinations of gestures, facial expressions, and vocalizations could be used to signal meanings quickly, with increasing precision. Eventually, these signals came to have internal, brain-based representations with both internal (mental) and external (real-world) referents—an expanding set of linguistic symbols. All manner of meanings could now be shared. Gestures and vocalizations could be used, not just to frighten and summon, but to convey helpful information: “Antelope carcass over there.” And to direct the behavior of others: “You go that way. I stay here.” Because individuals who were even slightly better at using the new signaling system became better fed, better at attracting mates, and better at raising children to maturity, the genetic programs that made them better communicators and better mind readers spread throughout their populations. The new signaling system—language—had become part of their environment and had begun to shape the brains of its users for its own purposes. Over millions of years, the nearly infinite complexity of the natural world—and the advantages that accrued to those who could better organize this complexity in their own minds and the minds of others— created pressure for ever more sophisticated and efficient versions of

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language. Along the way, the creature’s vocal and auditory systems became specialized for high-speed speech, capable of producing and interpreting strings of phonemes (speech sounds), representing an infinite number of possible meanings, at the rate of 10–15 per second. The coevolution of brains, technology, and language had produced an animal with an entirely new way of thinking and communicating about the world with others, of engaging in joint activities for the common good, and of efficiently transmitting hard-won, life-sustaining knowledge and skill from experts to novices, down through the generations. The second sister’s descendants had become a new species of talking, teaching ape—the most ingenious species on Earth, and the most dangerous. They had become us.

2 Teaching in Humans and Other Animals

One morning a few years ago, during a visit to my in-laws in Hong Kong (just as I was beginning to write this book), I stepped into the elevator outside their flat. Wanting to descend to the ground floor, I found myself hesitating between pressing the button marked G, or just below, B. I’d used the same elevator many times before, and knew very well, or should have, that B would take me to the ground floor, the last stop. For some reason, probably because of the association between “G” and “ground,” I pressed G. When the door opened and I stepped out, I realized I’d landed on a floor one stop above my destination. So I walked down a flight. As I passed through the lobby, the Cantonese-speaking doorman greeted me, then said, in English, “For ground floor, press B.” One night not long afterward, back in the United States, I was standing onstage in a music club in Memphis, Tennessee, “sitting in” on baritone saxophone next to my new friend Tommy Lee Williams, a professional tenor player. Between songs, Tommy Lee looked down at my setup (the crucial arrangement of mouthpiece, ligature, and reed) and said simply, “That’s right.” Weeks later, in the same setting, a trombonist took me aside between sets and said “Play the 5 down low.” I knew roughly what she meant—the “5” is the fifth note in any scale, an © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_2

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important note in the standard 1–4–5 blues progression. She was saying that I ought to play that note in the lower register of the horn, which is appropriate for the baritone saxophone, the lowest-pitched horn in the band. Possibly because I didn’t respond immediately, she then asked “What’s the 5 for F?” I understood the question, but couldn’t think of the answer. I felt confused, and slightly humiliated. I wanted to say, “That’s not the way I think…” but I let it go. So, in the first case I was wrong, in the second I was right, and in the third, a little confused. In each case a fellow human had made an unsolicited effort to alter or add to what a psychologist might call my “mental state”—what I knew and was assumed to be thinking. The doorman, it seemed, had seen the elevator door open, the elevator empty, then, moments later, me trudging down the stairs. Knowing I was staying on the 8th floor, and quite likely having seen evidence of the same B/G confusion many times before, he must have supposed that I was operating under the influence of the understandable but incorrect assumption that G stood for “ground floor,” and felt obliged to correct my false belief . Tommy Lee’s comment was less subtle, less obviously necessary, but, to me, more useful. He must have assumed that, as an amateur musician, I’d appreciate feedback from an accomplished professional, and so he gave it. Sometimes it’s just as helpful to know you’re right as that you’re wrong. And in the third case, the trombonist had taken it upon herself to correct my choice of notes, by quizzing me, in the manner of a music teacher. Although products of very different cultures, and living on opposite sides of the planet, both the Chinese doorman and the American musicians had demonstrated two apparently miraculous capacities—and one valuable and fortunate disposition. All were able to read (or at least had reason to think they could) the hidden contents of my brain. All knew how to produce, using their lungs, larynx, teeth, tongue, and lips, specially crafted packets of sound which, moving rapidly through the air, then impinging on my eardrums and thereby exciting complex neural circuitry, had the effect of producing physical changes in my brain, and therefore my mind, representing new thoughts and knowledge. And all

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three of my fellow humans had the inclination to employ these capacities, at some small cost to themselves, altruistically, for my own personal benefit, that is, to teach. This is no small thing. The use of human language to pass along cultural knowledge and skill from expert to novice, and from one generation to the next, down through the ages, is, I hope to convince you, unique to our species. We take it for granted that we humans can make sounds that convey information and ideas, and cause others to behave in certain ways, and that we can make sense of, and learn from, the sounds that others make for this purpose. But this special power, which begins developing in all of us from conception, is no more unique nor astounding than other marvels of nature, including the ability of certain species of tiger moth to jam the sonar of moth-seeking bats (Miller and Surlykke 2001), the eight-figure waggle dance of the honey bee, through which it communicates to other bees the location and quality of a distant food source (von Frisch 1967), and the elephant’s trunk, which among other specialized features, is lined with chemoreceptors capable of detecting a python hidden in the grass a mile away. Uniqueness, as Steven Pinker has reminded us, is common in nature (Pinker 1995). How, when, and why, one must wonder, did our distant ancestors ever become capable of this special form of communication and instruction? What is the relationship between teaching and language? How has human teaching developed over time? What exactly does it consist of? How does it vary across cultures, subcultures, and settings? Are some forms of teaching more effective than others? If so, why? To what extent, and in what ways, does expertise (in both teaching and learning) vary from one individual to another? Given the crucial role of teaching in our own personal lives, in our children’s lives, and, even more importantly, in knitting together, repeatedly, in each generation, the fragile fabric of our civilized, technological society, it is hard to think of more important questions. We still don’t have definitive answers, but thanks to recent findings in a broad range of disciplines, scientists around the world have managed to fit together enough pieces of the puzzle that a recognizable, believable picture is beginning to emerge. And what we’re seeing has profound implications for how we think about ourselves as parents, as teachers, and as citizens

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of Planet Earth. Many of us (but not all) are among the most privileged animals on Earth (think indoor toilets, hot showers, supermarkets), but in other ways we’re the most dangerous. Teaching, as it turns out, is largely what got us into our present predicament—an existential threat not just to countless other species, but to our own species—and teaching, if anything, is what may yet rescue us.

What Is Teaching? Do Birds and Bees Do It? So, what, fundamentally, is teaching? Let’s begin by considering what we mean when we say an individual “teaches” another. If you look back you’ll see my claim was that only humans, alone among animals, use human language (to avoid the tautology I might better have said “human-like” language) to pass along useful information to others and help others learn. I was being careful. If I had used the word “language” alone you might have disagreed, and if I had asserted that only we humans go out of our way to communicate useful information to others, you would almost certainly have disagreed, and rightly so. Many other animals communicate or otherwise modify their behavior in the presence of fellow creatures in a way that might be called teaching. Consider, for example, the case of Temnothorax albipennis, a species of ant. These tiny-brained insects use a bidirectional feedback technique known as “tandem running” to help others learn the route to a food source (Franks and Richardson 2006). The leader, who knows the route, and could go there directly itself, instead modifies its run for the benefit of a naive recruit. The recruit periodically taps the leader’s legs and abdomen with its antenna to signal that it’s keeping up, and the leader will slow down and wait while the recruit circles around in the immediate vicinity, apparently looking for landmarks to help it store the new route in its memory. In other ant species, workers with knowledge of a food source recruit assistance by literally carrying naive workers to the location. In both cases, the teacher ants are modifying their behavior, at some cost to themselves, to help others learn, for the benefit of the colony.1

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I’ve already mentioned another possible example of teaching in social insects—the famous waggle dance that honey bees use to recruit hive mates to fly to a distant flower patch, source of water, or potential new nesting site. The dance consists of a figure-eight pattern, which may be performed as many as 100 times. It was first interpreted by the Austrian ethologist and Nobel laureate Karl von Frisch. The angle of the dance (from the vertical, on the inner wall of the hive) correlates with the direction to the target site relative to the sun, the number of repetitions with distance, and the speed of the movements with the quality of the source, reflected in the dancer’s excitement. In some cases, waggle dancers compete by dancing more energetically, and even attempt to disrupt each other’s dances. But, like human learners, individual honey bees do not automatically heed the teacher’s instructions, no matter how forcefully made. In fact, the waggle dance is effective in recruiting bees to a new site in fewer than ten percent of cases; most bees simply return to the food source they’d previously visited. But, for this complex and sophisticated behavior to have evolved, even a small increment in the colony’s collective knowledge must have been worth the cost to individuals. And yes, birds do it. Mother hens use a combination of staccato food calls and exaggerated pecking at food items on the ground to help chicks learn which food sources are palatable (Nicol and Pope 1996). Somewhat like the bees, the hens increase the intensity of their displays for higher-quality food items, especially if their chicks move away from the food source, or if they make errors. Field observations of raptors—including peregrine falcons, sparrow hawks, and osprey— provide numerous examples of adults helping fledglings learn to hunt by capturing and releasing prey in their presence, or, as in the case of the falcons, diving at, flushing out, but (apparently) intentionally missing small birds they would otherwise have easily caught. Similar behaviors have been observed in meerkats, mongooses, and various members of the cat family, including lions, cheetahs, and domestic cats—all of which catch and release disabled prey in the presence of novice hunters (For these and other instances of “teaching” in nonhuman animals, see Caro and Hauser [1992]). Evidence of something like teaching has also been observed in our much nearer relatives, the other primates. For example, gorillas, yellow

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baboons, rhesus macaques, spider monkeys, and captive chimpanzee mothers have all been observed encouraging their infants to walk and follow them—often simply by waiting for infants to catch up, but, at least in the case of chimpanzees, using certain characteristic gestures (Hobaiter and Byrne 2014). More frequently, nonhuman primates convey useful information through negative feedback. For example, in an experimental study with captive baboons, high-ranking adult males who’d had the unpleasant experience of eating poisoned fruit (courtesy of the researchers) aggressively threatened younger members of the group who showed interest in eating it (Fletemeyer 1978). Also, among vervet monkeys, which famously learn at least three different standard alarm calls—one each for snakes, large cats (e.g., leopards), and flying predators (eagles)—adults take up appropriate calls when first uttered by infants, and either ignore or emphatically scold infants who produce inappropriate ones. You may have noticed that I have been careful, in these examples of teaching in other animals, to describe teaching in terms of providing assistance—not as direct instruction. For example, I recounted how mother hens help chicks distinguish high-quality food items, and how adult raptors help fledglings learn to fly. I did this partly to make my argument more palatable, thinking you might be a little more troubled, as I am, by the notion of a small-brained animal such as a chicken “teaching.” But, teaching even in humans is always at least partly a matter of assisting, never a one-way transaction, because the learner must always do some amount of physical or cognitive work. You can tell me that the “B” button on the elevator control panel represents the ground floor, not the basement, but I must listen to you and make sense of what you mean in my own way. Teaching, in other words, is always bidirectional, requiring active participation on the part of both teacher and learner2 (Unfortunately, the English language does not have a single word encompassing the two concepts. Consider, for example, the term “communication,” which implies both a sender and a receiver). Thinking back across these cases, it’s clear that behaviors which look very much like teaching have evolved in a wide range of animal species— including, to name just a few, ants, bees, chickens, various raptors, whales, cheetahs, domestic cats, and nonhuman primates. And it’s a safe

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bet that the harder and longer scientists look, the more instances they’ll find. Something like teaching, it seems, is widespread in nature. Or is it? Some anthropologists, notably David Lancy at Utah State University, have claimed that teaching is uncommon in remaining traditional, small-scale (hunter-gatherer) human societies (Lancy and Grove 2010). In the absence of direct instruction, Lancy reports, children are left to acquire cultural knowledge and technical skill on their own, primarily by observing adult experts at work, in much the same way, as I’ll soon describe, that young chimpanzees learn how to fish for termites. The implication is that teaching in humans is a modern phenomenon, a practice found mainly in so-called WEIRD (Western, Educated, Industrialized, Rich, Democratic) societies. But there’s a problem here. How can it be that birds and bees do it, but humans, or at least some of us, don’t? As is common in such debates, it’s clear that the underlying issue is definitional. Whether any given behavior constitutes “teaching” depends on how we decide to define the term. Let’s begin with a well-known definition that biologists Tim Caro and Marc Hauser (1992: 153) have given: An individual actor (A) can be said to teach if it modifies its behavior only in the presence of a naive observer (B) at some cost or at least without obtaining an immediate benefit for itself. A’s behavior thereby encourages or punishes B’s behavior, or provides B with experience, or sets an example for B. As a result, B acquires knowledge or learns a skill earlier in life or more rapidly or more efficiently than it would otherwise do, or that it would not learn at all.

Teaching, in short, may be said to occur when experts go out of their way to help novices learn things they would not be able to learn as easily if left to their own devices. This definition works reasonably well for most of the cases of teaching among nonhuman animals we’ve already considered. By this definition, an adult vervet monkey scolding a youngster for crying the equivalent of “Eagle!” when it sees a falling leaf, a mother cat releasing a wounded mouse for its kittens to play with, a mother hen pecking and clucking dramatically at an especially tasty food morsel in the vicinity of her chick—all of these behaviors, by Caro and Hauser’s

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definition, constitute teaching. And using the same definition, an adult human hunter who slows his pace just a little so his son can keep up and participate in the hunt, or who lets the youngster shoot an arrow at a deer, knowing he might miss and frighten the deer away, would also be teaching, however indirectly. But what about those ants and bees? It seems there’s something fundamentally different about merely communicating the location of a single food source, and actually helping a fellow creature learn how to find food wherever the food might be. Compare, for example, the case of a tandem-running ant which leads another ant to the carcass of a large insect lying at the base of a tree, and contrast that with the case of the mother hen pecking dramatically at a kernel of corn in the barnyard. True, the ant learns something useful it might not have learned if it had to explore on its own. But the chick stands to learn something even more useful—corn makes better food than an empty seed pod. It’s the difference between someone giving you a fish and someone teaching you how to catch one. As we’ll see, this distinction becomes important when we turn to the special case of human teaching. For the time being, let’s agree that teaching occurs when relative experts go out of their way to help novices acquire new knowledge or skill that is generally useful, and that the novices could not have learned as easily without such assistance. By this definition, then, bees and tandem-running ants do not teach in the sense I suggest we use it here, because the information these insects convey is useful today, but possibly not tomorrow. Encouraging offspring to walk, giving them opportunities to practice catching prey, scolding them for raising false alarms, and helping them distinguish between palatable and unpalatable food sources may all qualify as teaching. What about warning another member of the group not to eat a certain piece of fruit that has been poisoned by a researcher? Presumably the same kind of fruit will not always be poisoned, so the lesson learned is not generally useful. But the adult’s inclination to warn others might be taken as evidence of an instinct for passing along hard-won knowledge to others who might not otherwise have that knowledge. Surely that’s the essence of teaching.

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The Special Case of Teaching in Humans So no, we humans are not the only animals on the planet who go out of our way to help fellow creatures acquire important knowledge and skill. But there’s clearly a big difference between releasing an injured mouse within a kitten’s reach, and explaining how to operate an elevator, play a saxophone, or, even more clearly, teaching someone how to calculate the area of a circle with a given diameter. Let’s face it. Human teaching is so much more complex and sophisticated than teaching in any other animal that comparisons hardly seem fair. Nonhuman animals do indeed help others learn certain generally useful skills and bits of knowledge, but these teaching behaviors are almost entirely instinctual (subject to little if any cultural shaping), occur in highly restricted settings, and almost always involve adults teaching their own young during the period of juvenile dependency. In contrast, teaching in humans as we’ll see is also instinctual, but only partly so, and occurs throughout our lifespans. Like other primates, human mothers naturally interact closely with their offspring, encourage their early attempts at the basic mechanics of life (such as walking), help them learn what and how to eat, and generally do what they can to make things easier, or at least no harder than necessary. In this sense, human parenting is almost certainly a genetically programmed behavior—not that much different from the instinctual parenting of other animal species, including chickens, cheetahs, and chimpanzees. But human teaching is also subject to cultural influence, and human culture is far, far more complex than anything else in the rest of the animal world. Take for example, a well-known example of nonhuman tool use: the case of “termite fishing” among certain chimpanzee communities, in West, Central, and East Africa. Importantly, this relatively sophisticated behavior is found only in some chimpanzee communities (notably at the Gombe National Park in Tanzania) and seems not to be dependent on the environmental context. In other words, not all chimpanzee communities that could engage in termite fishing do so, implying that the behavior is not instinctual (Goodall 1986). Termite fishing technology consists of two different tools: a stout stick for penetrating the wall of the mound, and a trimmed twig used as a

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fishing probe. The animal pokes a hole in the mound, inserts the twig, waits for some termites to attack and cling to the probe, withdraws it, then devours the termites, reported to taste not unlike cashews. This is smart stuff, involving forward planning, procedural memory, careful tool selection, and patience. And again, it must be learned. Chimps are not born termite fishers. Nevertheless, in a community of chimpanzees at Gombe studied by the primatologist Elizabeth Lonsdorf, all the offspring had learned how to make their own tools and fish successfully by the age of 5.5 years on average (Lonsdorf 2006). Unsurprisingly, the young chimps’ level of skill was highly correlated with the time they spent accompanying their mothers on fishing expeditions. In other words, all the youngsters learned, but not all achieved the same level of skill, and those that were more skillful were, as you might guess, the ones who spent more time participating in the activity. Significantly, the mothers were highly tolerant of their offspring’s error-ridden attempts, even if it disrupted the mother’s own efforts—a classic hallmark of teaching in the Caro and Hauser sense. But Lonsdorf never observed the mothers actively helping the youngsters learn. As it turns out, however, that’s not the whole story. You may find a video on YouTube (provided by the Jane Goodall Institute) showing an infant chimpanzee trying to fish for termites with a big floppy leaf instead of a twig.3 Her older sister notices she’s got the wrong tool, gently takes it away, removes a twig of the proper type from her own mouth, inserts it in the hole, then lets the little one take over from there: a pretty clear case of what educational researchers call “scaffolding” (Wood et al. 1976)—and a touching example of patient big sistering.4 But if you watch the video, notice what the big sister does not do. She does not point at the leaf and shake her head. She does not hold up a stick of the proper type and make her eyes big, as a kind of hint. She doesn’t say, as a human big sister, might, “You can’t fish with a leaf, dummy!” And she certainly doesn’t sit her little sister down and explain why a stick is better than a leaf. She’s a hands-on teacher, but not, strictly speaking, a communicator. And here’s what I think may be most important: she doesn’t try to direct her sister’s gaze to either the leaf or the stick so that it may become what the

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psychologist Jerome Bruner defined as an object of joint attention (Bruner 1972; I’ll have much more to say about joint attention in Chapter 6). With these things in mind, consider a modification of the Caro and Hauser definition that applies only to teaching in our species: Human teaching is a joint attentional activity in which a relative expert, through a series of one or more deliberate communicative acts, goes out of her or his way to help a relative novice acquire some new and generally useful component of knowledge or skill.

Notice that I’ve included a couple of important ideas here aimed at distinguishing human teaching from what biologists observe in other animals. The first is the idea of a joint attentional activity, the shared understanding that “we” are doing something together, with respect to some third thing: the object of joint attention. Although other animals can attend to the same thing—as when one chimp notices another gazing at a certain location in a tree and checks to see what the other is looking at—it seems that only humans have the capacity and inclination to intentionally direct each other’s attention to some third thing, as a way of sharing information and working toward a common goal (Tomasello et al. 2007). As we’ll see, directing attention is a core and indispensable tactic in any human teacher’s repertoire, and may well have been what originally made our special kind of teaching possible. A second important and related idea is that of teaching as a deliberate communicative act. A female meerkat (an African member of the mongoose family) may provide her offspring with a scorpion that she’s disabled to a degree that is roughly commensurate with the youngster’s developing ability to tackle the poisonous insect on its own (Thornton and McAuliffe 2006). The mother does this at some personal cost (she could eat the scorpion herself ), and the skill she’s helping her offspring acquire is generally useful. But I think it would be a stretch to say that providing the disabled scorpion is a communicative act. You might argue that the state of the struggling scorpion constitutes “information,” and possibly even that the young meerkat’s brain treats the state of the victim as information, but it’s much harder to think, at least in my mind, that the mother is intentionally “telling” her offspring anything. In contrast,

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the famous waggle dance of the honey bee, through which it communicates to other bees the location and quality of a distant food source is clearly a communicative act, a kind of telling, but the knowledge it helps naive recruits acquire, while clearly useful, is not generally useful (here today, the flower patch might be gone tomorrow), so it would not qualify as teaching by our definition. Now, here’s why I’m making so much of this distinction between teaching as helping and teaching as intentional communication. Because chimpanzees are our closest living relatives, any major differences between us become important. Why, we need to ask, don’t chimpanzees teach termite fishing through intentional acts of communication directed at objects of joint attention? We can consider at least two answers. For one thing, it seems they don’t need to. All the young chimps in the community that Lonsdorf studied acquired the skill of termite fishing simply by participating in the activity, observing experts, and practicing themselves. Biologists call this social learning , a form of “learning without teaching” (Atran and Sperber 1991). And the novice chimps acquired the skill well within the period of juvenile dependency, which, in chimpanzees, extends to the brink of puberty, at about 10–12 years. Further, termites constitute only a small portion of a chimp’s diet. Even if the youngsters failed to learn to fish, they’d probably be okay. Chimpanzee mothers, in other words, have no pressing need to devote their own precious time and energy to termite fishing instruction. More importantly, even if for some reason they’d needed to (imagine a situation in which termites were suddenly the only available source of protein and survival depended on rapid learning), it seems these chimpanzee mothers probably could not do what a human mother would almost certainly do, instinctively and easily. In one way or another, a human mother would probably help by talking her offspring through the process—or at least she could if she wanted to. Why can’t chimps teach in the way that humans do? Why haven’t chimps, our closest relatives, evolved the capacity for teaching through language? Why can’t chimps be like us? The quick answer seems to be that our human ancestors simply lucked out (Or, considered from another perspective, life on Earth witnessed a bad accident). Somehow, owing to a still-mysterious and highly improbable confluence of environmental and genetic events, possibly as long as

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3.5 million years ago, and probably no later than about 1.7 million years ago, our human ancestors, alone among the great apes, began to evolve the capacity and inclination to teach through acts of communication directed at objects of joint attention. At first their communicative behaviors must have been simple—not too different from signaling systems still employed by the other great apes, involving various combinations of gestures, facial expressions, and vocalizations. But somehow this new way of communicating, which we can call a protolanguage, evolved into modern human language, a biological system whereby small sets of distinct sounds (different for different languages) can be combined, according to a small set of rules (syntax), to represent an infinite number of possible meanings and intentions. The result was the evolutionary explosion that produced a talking ape capable of flying itself to the moon and back. But, even though chimps are our closest genetic relatives, humans are not talking chimps. Rather, as I’ll explain in the next chapter, the various components of our distinct biology—our upright posture, bipedalism, prehensile “power grip,” highly evolved vocal and auditory systems, supersized, energy-hungry brains, extended lifespan, extended juvenile period, and ability to teach in the special ways we do—have come to set us far apart from all other primates. And whereas chimpanzees can get along perfectly well without language, and without the ability to use language to teach, we humans could not survive without these special faculties. Human children, and adults, for that matter, must learn a lot more than how to fish for termites if they want to get along in the world. So how did teaching through language become not just possible, but obligatory among humans?

Food for Thought 1. Have you observed any instances of teaching in other animals yourself? What did you see? What makes you think it was teaching? 2. Why do some animals engage in activities that resemble teaching, while others do not?

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3. What are some evolutionary processes that might yield teaching in some animals and not in others? 4. Can you imagine an evolutionary process that would explain the emergence of teaching (as described in this chapter) in chickens? What about in other animals? 5. Do you agree that teaching is “obligatory” among humans?

Notes 1. These techniques are cost-effective only in ant species that live in smallish colonies, where other methods of broadcasting information, such as through pheromone trails, are inefficient. 2. Readers familiar with educational theory will recognize this as a core principle of “constructivism,” an educational philosophy associated with Jean Piaget and Lev Vygotsky which stresses the important role of the learner in “constructing” her own knowledge. 3. Try the search string: “chimp termite fishing learning.” 4. It is significant that it was a big sister, not the mother, who helped the little one. As I’ll explain in Chapter 8, that older children have the capacity and inclination to help younger ones acquire important cultural knowledge and skill, thus giving adults more time to devote to important chores such as foraging, may well have played an important role in human evolution.

Further Reading Caro, T. M., & Hauser, M. D. (1992). Is there teaching in nonhuman animals? Quarterly Review of Biology, 67 (2), 151–174. A useful survey of evidence for teaching in nonhuman animals. Gärdenfors, P., & Högberg, A. (2017). The archaeology of teaching and the evolution of Homo docens. Current Anthropology, 58(2), 188–208. Co-authored by an anthropologist (Gärdenfors) and an archaeologist (Högberg) the paper provides a good introduction to many of the central themes and arguments you’ll find in the remaining chapters of this book.

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Kline, M. A. (2015). How to learn about teaching: An evolutionary framework for the study of teaching behavior in humans and other animals. Behavioral and Brain Sciences, 38, 1–71. You might find, as I do, that Kline’s rather technical “target paper” is less interesting than the commentaries that follow, primarily for the variety of viewpoints they represent. Also, if you choose to read this article, note the curious lack of attention to the crucial and distinguishing role of language in human teaching.

References Atran, S., & Sperber, D. (1991). Learning without teaching: Its place in culture. In Annual workshop on culture, schooling and psychological development, 4 June 1987, Tel Aviv University, Ramat Aviv, Israel. Ablex Publishing. Bruner, J. S. (1972). Nature and uses of immaturity. American Psychologist, 27 (8), 687. Caro, T. M., & Hauser, M. D. (1992). Is there teaching in nonhuman animals? Quarterly Review of Biology, 67 (2), 151–174. Fletemeyer, J. R. (1978). Communication about potentially harmful foods in free-ranging chacma baboons, Papio ursinus. Primates, 19 (1), 223–226. Franks, N. R., & Richardson, T. (2006). Teaching in tandem-running ants. Nature, 439 (7073), 153. Goodall, J. (1986). The chimpanzees of Gombe: Patterns of behavior. Cambridge, MA: Harvard University Press. Hobaiter, C., & Byrne, R. W. (2014). The meanings of chimpanzee gestures. Current Biology, 24 (14), 1596–1600. Lancy, D. F., & Grove, M. A. (2010). The role of adults in children’s learning. In D. F. Lancy, J. Bock, & S. Gaskins (Eds.), The anthropology of learning in childhood (pp. 145–180). Lanham, MD: AltaMira Press. Lonsdorf, E. V. (2006). What is the role of mothers in the acquisition of termite-fishing behaviors in wild chimpanzees (Pan troglodytes schweinfurthii)? Animal Cognition, 9 (1), 36–46. Miller, L. A., & Surlykke, A. (2001). How some insects detect and avoid being eaten by bats: Tactics and countertactics of prey and predator. AIBS Bulletin, 51(7), 570–581.

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Nicol, C. J., & Pope, S. J. (1996). The maternal feeding display of domestic hens is sensitive to perceived chick error. Animal Behaviour, 52(4), 767–774. Pinker, S. (1995). The language instinct: The new science of language and mind (Vol. 7529, pp. 332–369). London, UK: Penguin. Thornton, A., & McAuliffe, K. (2006). Teaching in wild meerkats. Science, 313(5784), 227–229. Tomasello, M., Carpenter, M., & Liszkowski, U. (2007). A new look at infant pointing. Child Development, 78(3), 705–722. von Frisch, K. (1967). The dance language and orientation of bees. Cambridge, MA: The Belknap Press of Harvard University Press. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17 (2), 89–100.

3 Not a “Third Chimpanzee”

Contrary to the title of a recent book on the subject (Diamond 2014), humans are not really “The Third Chimpanzee.” Far from it. Chimpanzees (and their pygmy sister species, the bonobos) are indeed our closest relatives. Chimps and humans share a single common ancestor, from some 6–10 million years ago, and a common genetic heritage with the other great apes (gorillas and orangutans) dating back some 16 million years. Like all other apes, we’ve lost our tails, and our claws have become fingernails. As with the other apes, both chimp and human babies are born helpless, requiring years of nurturing and protection before we can fend for ourselves. And we’re both social animals, depending on other members of our groups for common defense against enemies, social learning, companionship, and access to the necessities of life, including sex.1 Because of our social interdependence, most great apes spend a great deal of precious time managing often-difficult relationships with other group members. We do our best to build and maintain affiliations with our allies, protect ourselves against exploitation by others, secure relations with sexual partners, and, especially if we are females, nourish and protect our offspring, and help them become fully integrated members of the group. For these and other purposes, © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_3

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we communicate regularly with our fellows using sophisticated though differing repertoires of facial expressions, physical gestures, and, to a lesser degree (in the case of chimps), vocalizations. These close family ties are reflected in the amount of DNA we share with chimps and the other great apes—somewhere between 95 and 99% depending on how the overlap is measured (Wildman et al. 2003). Although much is made of this, it shouldn’t be surprising that our genetic blueprints are so similar. All great apes share the same overall vertebrate body plan, the same mammalian biology, the same number of teeth and bones, the same central nervous system, and the same internal glands and organs—each a complex, highly evolved system in itself. There’s a lot for a developing embryo to build before it gets to the uniquely human parts, such as specialized speech apparatus. In comparison with the fully shared biology, the differences are tiny. But these tiny differences have enormous consequences. Humans are strikingly and fundamentally different from chimpanzees and the other great apes in many important ways and have been for millions of years. We’re the only remaining apes who walk (and run, and dance) easily on our hind legs with an upright posture, our hands and arms freed and adapted for other functions. Other members of the ape family are primarily vegetarians, but we humans are skillful omnivores, fueling our oversized brains (almost three times the size of a chimpanzee’s) with a wide variety of hard-to-acquire, nutrient-rich foods, including the flesh of other animals, sometimes—especially in northern latitudes—as the primary source of nourishment. Recently, in the last 10,000 years or so, we’ve even come to raise our own food in protected fields and pens. We fall in love, ride around in cars, fly airplanes, and swim deep underwater with special breathing apparatus. Twelve of us have walked on the moon. And we can easily produce and understand utterances like, “I’m pretty sure you think you know what I’m thinking, but you’re completely wrong.” Yes, we’re great apes, and no, we’re not the greatest, but, despite our close genetic relationships, it seems we’re as different as cousins could possibly be. Understanding what these differences are, and how they’re related, puts us in a much better position to understand how humans have come to do something chimpanzees and other

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nonhuman apes apparently never do—teach others through intentional acts of communication.

The Hominid Family Tree We don’t know, and may never know, exactly why and at what point early hominins (human ancestors) first started down the narrow evolutionary path that led fatefully to the piece of work that is us, so different from the path followed by other hominids.2 We do know that the journey must have started at some time after the split between the human and chimpanzee lineages, which occurred approximately 6–10 million years ago (Benton et al. 2009). And while it is now generally agreed, based on the DNA evidence, that chimpanzees are our closest living relatives, this does not mean that we are descended from chimps, nor even, as I explain below, that our last common ancestor with chimpanzees was necessarily chimp-like (White et al. 2015; Sayers et al. 2012). To put this evolutionary history in perspective, it’s helpful to look at the larger context of hominid evolution: the separate lineages of the four remaining great apes—humans, chimpanzees, gorillas, and orangutans. Figure 3.1 gives a simplified version of this family tree, with the gibbons (“lesser apes”) added for context (see Stauffer et al. 2001). Hominins (Humans and human ancestors) Pongo (Chimpanzees)

Hominids (Great apes)

Chimps Bonobos

Gorilla (Gorillas) Ponginae (Orangutans) Hylobatids (Gibbons)

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Millions of years before present Fig. 3.1 Highlights of hominid evolution

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To be clear, the ape family tree is considerably more complicated than shown here, both at present, and going back in time. Among other things, there are two species of gorilla, two of orangutan, and eighteen gibbon species. Also, our own lineage is not by any means the clean straight line shown here—it’s more like a tangled bush. As recently as 100,000 years ago, at least four distinct human species were spread across Africa, the Near East, Europe, and Asia. For now, what I’d like you to think about as you study this timeline is how very far back it stretches—and yet what a tiny slice of evolutionary time this represents. True, the 6–10 million years that separate the human and chimpanzee lineages is a long time, evidently long enough for the obvious differences between us to have evolved. But by other measures, even 10 million years is just the blink of an eye! Life itself is thought to have first emerged on Earth some 3.5 billion years ago. If 3.5 billion years is a 24-hour day, then 6 million years is about 2 minutes and 30 seconds. Another thing to think about here is that while some 6–10 million years of natural tinkering was evidently sufficient to produce a naked, brainy, talking, teaching ape, this strange development was by no means inevitable. Organisms evolve in fits and starts, and sometimes remain very much the same in form and function for many millions of years. Consider, for example, the case of “living fossils” such as the horseshoe crab, which has remained largely unchanged for some 400 million years—or the coelacanth, the deep-sea fish once thought to be extinct, now understood to have retained its basic body plan for about the same amount of time as the horseshoe crab. If our habitats were as stable as those of coelacanths and horseshoe crabs, humans and chimpanzees might also have stayed very much the same over the last 6 million years, remaining perhaps not too much different from our last common ancestor. That we humans have grown so far apart from our closest cousins, and indeed from all other primates in such a brief stretch of time is almost miraculous. But as we’ll see, evolution doesn’t need miracles to do its astounding work.

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Evolution of Adaptive Suites Before we get into the specifics of the striking differences that have arisen between chimpanzees and humans, let’s review some of the basics of biological evolution—the process whereby the features of life forms change over time in response to complex combinations of environmental pressures, genetic accidents, and opportunities. Simply put, all newborns differ from each other in subtle ways, representing slightly different (and inheritable) genetic profiles. Some of these differences may be more “adaptive” than others, meaning that individuals who have these traits are at least slightly more likely to survive to adulthood, attract mates, and successfully pass along their genes to offspring. In social animals, which depend on each other’s efforts to survive, traits that benefit the kinship group as a whole (such as instinctual alarm calls in the presence of predators) may also be adaptive even if they don’t benefit the individual, or even put the individual at personal risk (Hamilton 1963). Whether a given feature helps a species survive and propagate depends largely on the nature of its selected habitat, which may change over time, and is more likely to change in some places (such as the surface of the Earth) than in others (the ocean depths, the realm of the coelacanth). Importantly, when we say that a given feature arose for such and such a purpose, what we mean is that the feature turned out to be useful—not that the genetic programs intentionally and intelligently produced the feature with that purpose in mind. It’s misleading to say, for example, that a duck’s webbed feet evolved for “the purpose of ” swimming. Rather, over time, individual ducks with genes that left slightly more membrane between their toes during fetal development could swim just a little faster and more efficiently, and so were more likely to survive and prosper in an aquatic environment, and so pass on these genes. As a result, genetic programs that left webbing between the birds toes (which gets trimmed off during fetal development in other species) spread through the populations of ancestor ducks. Further, it’s not that ducks waited patiently for their webbed feet to evolve before daring to set out on the water. We can guess that ducks must have already been at least partially aquatic for even slightly webbed feet to become more of an advantage than a problem. For the same reason, it’s misleading to say or think that ducks “learned”

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to swim, as if some individual duck or team of ducks got together and invented a new system for aquatic travel. Rather, over millions of years, through a complex combination of genetic accidents, mating choices, and environmental pressures, birds that were once restricted to skies and shores gradually evolved the ability to float and paddle with their webbed feet. (For the same reason, it is wrong to say that humans “invented” language.) This brings us to another important concept, that of biological precursors. Biological systems such as eyes and ears and webbed feet don’t just suddenly appear in an organism like rabbits out of a magician’s hat. Rather, over periods of time—sometimes rapidly, sometimes more slowly—pre-existing systems are reorganized, repurposed, and enhanced by natural selection. Nature is a tinkerer, not an inventor or miracle worker (Jacob 1977).3 Putting aside, if you will, the ultimate mystery of life’s origins, it’s clear that any given trait in any given species must have had a precursor in an earlier version. Among other things, this means there cannot be stark, unbridgeable “discontinuities” between a given species and its evolutionary predecessors—just evolved differences. More specifically, it means that the traits that have come to distinguish humans from chimpanzees (including language and the biological capacity for teaching through language) must have had precursors in our common ancestor. Yet another important concept is the notion of coevolution, which refers to a process whereby changes in one biological system or subsystem impose selection pressure on some other system, or part of a system, which evolves in concert with the other components. Originally used to describe the mechanism whereby changes in the genomes of two or more different species (e.g., pollinating insects and flowering plants) affect each other’s evolution, the term coevolution has also come to be applied to the evolution of related features within the same species. So, aquatic birds tend to have a bundle of features, including a gland for oiling their feathers (which makes the feathers water-resistant), hollow bones, and barbed feathers that lock together, trapping air—all of which work together, and have likely evolved together, because, in combination, the combined features make floating a lot easier, and floating, to ducks and other aquatic birds, is a big deal. As we’ll see, examples in humans

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include the coevolution of the brain, intestinal tract, foraging behaviors, life history, diet, teaching, and language. Often, through a process of niche construction (Odling-Smee et al. 1996), an organism changes its own habitat in a way that alters the course of its own biological evolution. A well-known example is the beaver dam, but others, such as the impact the evolution of human language has had on the evolution of the human brain, are subtler. As human ancestors first began to interact with each other using a simple system of verbal and gestural communication—necessarily evolved from biological precursors in other primates—these early languages would have created a new selection pressure, favoring individuals with brains that were better adapted to acquiring and using the emerging system, thus creating a snowballing effect. Language, now part of the human environment, began to breed for brains that could best do its work. Language itself had become, as the cognitive scientist Chris Sinha has put it, a biocultural niche (Sinha 2017). As we’ll see, this idea is fundamental to understanding how human language, and teaching through language, most likely evolved. It’s an instance of what is sometimes called the “Baldwin effect,” named after the nineteenth-century American psychologist who first defined it. (For an extensive analysis of this proposition as applied to human language evolution, see Deacon 1997: 322–334; Sinha 2015, 2017). More generally, a biological organism is best understood as an integrated, systematically-related package of component traits, also known collectively as its adaptive suite (Lovejoy 2009), which coevolve in response to various selection pressures, accidents, and opportunities. As a package, these traits together ensure the organism’s survival, at least for a time, in whatever habitat it has come to occupy—alongside, typically in a kind of harmony with, the full diversity of organisms in the same habitat. To understand the function of any given trait and how it evolved (whether webbed feet, a proclivity to share food, monogamy, a system of communication, or intentional teaching) it’s necessary to understand how the trait fits into the complete, interlocking package. More to our point, if we want to understand how it is that one species

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actively teaches and one doesn’t, it will be necessary to understand how the capacity and inclination to teach evolved within the larger context of that species’ biology and habitat.

Evolved Differences Between Chimpanzees and Humans This takes us back to our main story. Recall that the lineages of humans and chimpanzees, our closest living relatives, split at least 6 million years ago, which, while the blink of an eye by some measures, evidently provided plenty of time for the fundamental differences between our two species to emerge, as each species followed its own path, with radically different outcomes. This is not to say that it’s wrong to compare our bodies and lifestyles with those of chimpanzees. After all, given the extent of DNA overlap, the comparison is not exactly apples to oranges. That said, we cannot assume that chimpanzees have remained frozen in time, and it is only we who have changed over the millions of years that separate us. We have evidence, for example, that the chimp’s method of terrestrial locomotion (knuckle-walking), may have evolved sometime after the split, as did their long-fingered hands and grasping feet (Almécija et al. 2015). And chimps are almost certainly larger, more muscular, and more physically dangerous than our common ancestor. In fact, it’s not impossible that it was our closest cousins who first threatened to outcompete us in our common ancestral environment, forcing us to try something different or be out-eaten, or eaten. Millions of years later, we keep chimpanzees in zoos and, until recently, used them for biomedical experiments. A side-by-side comparison of some of the features that most clearly distinguish our two species, given in Table 3.1, is illuminating. It bears repeating that each set of traits marks the current endpoint of two different evolutionary pathways, not necessarily a transition from one set of features to another. As you can see (just look at yourself ), despite our genetic similarities and shared history, relatively recent coevolutionary forces have dramatically reshaped almost every inch of our bodies, from head to toe. Further, these physical changes have both enabled, and been

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Table 3.1 Chimpanzees and humans compared Feature

Common chimpanzee

Homo sapiens

Habitat Locomotion Hands

Forest Modified quadrupedal Adapted for tree climbing and knuckle-walking Adapted for tree climbing Enlarged canines Shorter, robust 1.29 384 cc Limited (rocks, sticks) Mainly fruit, leaves, and insects; some meat Visible Polygyny, male competition 40–50 Gestures, some vocalization Minimal

Global Bipedal (obligatory) Adapted for tool use

Feet Dentition Body type Dimorphisma Avg. brain size Tool use Diet Ovulation Male-female pairing Group size Communication Cumulative culture

Adapted for walking, running, etc. Small canines Taller, more “gracile” 1.15 1330 cc Extensive Opportunistic omnivore Hidden Pair bonding, family provisioning 150+ Symbolic language Accelerating

a Dimorphism

refers generally to characteristic physical differences between the males and females of a species. Here I give the ratio of male to female body size, which is thought to be an indication of the degree of male competition for sexual access, and female sexual preferences. Male chimps are about 30% larger than females, compared to about 15% for humans. Apparently driven by selection pressure imposed by “mate guarding,” male gorillas can be nearly twice as large as the females in their harems

caused by, changes in our lifeways, including our diets, technologies, sexual and social relationships, and, most dramatically, our methods of communication. Each of these changes tells a story and is part of a larger story. Let’s begin with the basic shape and construction of our bodies

Anatomical Differences Chimpanzees have become adapted to a physically competitive life in the tall, fruit-bearing trees of a tropical forest. They’ve evolved shorter, more muscular bodies than humans, and thicker bones and skulls. Their

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powerful legs and hand-like feet are especially adapted for shinnying up tall trees, and their hands and long fingers are shaped for grasping tree trunks and swinging from branch to branch in the canopy above. On the ground, chimpanzees walk on all fours, using their knuckles for support, but can also walk, though inefficiently, for short stretches on their hind legs—which they sometimes to do when carrying food or tools (e.g., a stone for cracking nuts) from one location to another (Carvalho et al. 2012). Humans, on the other hand, are fully and comfortably bipedal, which is made possible by a skeletal structure adapted for long-range travel in the open using different gaits, including walking, jogging, and running, not to mention hopping, skipping, jumping, and dancing. Some of us are still good at climbing trees, and swinging from a trapeze, but we’ve lost the ability to grasp branches with our feet. We’re taller, and our bones are thinner—more gracile. Our fingers are shorter and thumbs longer, giving us the ability to hold and manipulate small objects, such as sharp flakes of stone, sewing needles, or tiddlywinks, between our thumb and any finger, the so-called precision grip (Napier 1956). Unlike chimpanzees, we can also fold our shorter fingers into a fist, creating a vice between fingers and palm (the power grip)—good for gripping sticks, spears, and baseball bats, and punching and pounding with our fists alone. Like most other primates, and unlike humans, male and female chimpanzees have both developed enlarged, sharp canines (fangs, what paleontologists call the “sectorial canine cluster,” SCC), used primarily for displays of aggression and defense against other members of the group, but also useful for ripping apart the bodies of small monkeys, which males, accompanied occasionally by females, chase through the trees. Humans (thankfully, you might think) do not have fangs, and there is evidence that the common ancestor of humans and chimpanzees didn’t either (Lovejoy 2009). As we’ll see, the absence of fangs in early human ancestors, along with relatively minor dimorphism (i.e., males only slightly larger than females) has been linked to the emergence of monogamous pair bonding and subsequent de-emphasis on male-to-male competition for mating partners.

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Diet and Food Extraction Technologies Although chimpanzees hunt other animals (see below), meat constitutes less than 5% of their diet, which consists primarily of ripe fruit, nuts, flowers, new leaves (tender and digestible), and insects. Our own diet, as we’ll see, is a fundamental part of what makes us so different, but the differences between chimp and human diets, especially among our hunter-gatherer ancestors prior to the Agricultural Revolution, were subtle. Wild chimpanzees (and, more rarely gorillas) display some ingenuity in extracting foods that might otherwise be difficult to harvest. As we’ve seen, chimpanzees use at least two types of stick to fish for termites—stout ones to poke holes in the mound, and smaller, trimmed sticks, sometimes with intentionally roughened tips, to fish out the termites. By stripping the twigs from sticks and sharpening a point with their teeth, chimpanzees also fashion spears for stabbing bushbabies in their burrows.4 Chimps also select rocks of just the right weight for cracking nuts and carry these to groves of nut trees (Boesch and Boesch 1990). And they fight each other with rocks and sticks. Like chimpanzees, humans are omnivores. And like chimpanzees, we use our brains to provision ourselves. But, as noted above, our brains have grown to be about three times bigger (think, roughly, baseballs to soccer balls), which is of course part of the reason we’re the zookeepers and medical researchers. Our food-related technologies—not least the control of fire for cooking (which seems to have emerged quite recently, on the order of 500,000 years ago5 )—are arguably a lot more than three times as sophisticated. As it turns out, and as I mentioned earlier and will explain in more detail in the next chapter, an interesting and necessary relationship exists between the size of human brains, the length of our intestines, and the foods we eat.

Social and Sexual Arrangements Like all primates, humans and chimpanzees are social animals. We both live in groups and depend very much on our social and sexual arrangements with other group members both for our own survival, and,

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crucially, for the survival of our genes. Group living provides several important benefits, including defense against predators and competing groups, ready access to potential mates, and more efficient foraging through sharing of information regarding location of food sources (Hoffecker 2013), and defense of regular feeding sites (Garber 1987). Group living also supports the establishment and social transmission of useful technical practices, such as termite fishing (chimpanzees), or fishing with nets, hand lines with hooks, or spears (humans). However, living in a group also has costs, chiefly the time and energy required to form and maintain trusting relationships with others, and the stresses that can quickly build when something goes wrong—costs which potentially increase with group size and resultant crowding (Dunbar 1998). In order to manage these costs and leverage the benefits of group life, both humans and chimpanzees devote, and must devote, a considerable amount of time every day to the establishment and maintenance of social relationships with group members. For chimpanzees, the primary means of developing and maintaining a sense of trust and mutual obligation is social grooming (“allogrooming”), which can occupy as much as 20% of a chimp’s day (Dunbar 1998). Group members take turns smoothing and plucking at each other’s fur, picking away dirt, dead skin, and parasites (ticks, leeches, lice), especially in hard-to-reach places, such as the middle of the back. In addition to its hygienic benefits, allogrooming has been found to have neurophysiological effects that serve to strengthen bonds between groomers. The physical contact of fingers on flesh can trigger the release of stressreducing endorphins and the hormone oxytocin, a natural opioid which has been found to promote both sexual and parental bonding and cooperative behaviors in a wide range of mammals, including humans. Interestingly, in chimpanzees the level of oxytocin released is related to the existence of a prior social bond between the two animals (Crockford et al. 2013). In other words, you can’t overcome a stranger’s aggression or reluctance to cooperate with a just bit of grooming. It takes time and repeated positive encounters for the effect to work. In any case, chimpanzee mothers regularly groom infants and are groomed by them. Subordinate males groom dominant males, who may return the favor.

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Humans also groom each other (in barbershops, beauty salons, and nail parlors) and engage in social and sexual touching (handshakes, fist bumps, hugs, kisses, etc.) However, talk, not grooming or touching, is clearly the primary means by which humans have come to build and maintain trusting relationships. If sexual partners stop talking with each other, something is wrong. We shake hands or exchange fist bumps with new acquaintances and smile, but we also say how glad we are to make the person’s acquaintance. We praise each other, and, to build social solidarity, tell each other stories designed to highlight shared experiences (“That reminds me of the time…”). And we further strengthen our relationships with each other by exchanging information and opinions about third parties. Human language, it has been argued, originally arose primarily as a substitute for grooming, the so-called gossip hypothesis of language origin (Dunbar 1998). I’ll have more to say about this (which I will argue is at best only partly correct) in Chapters 4 and 5.

Group Size and Social Dynamics Social animals differ in the size and composition of the groups they live in, the extent to which individuals remain in the same group over time, the spatial distribution and cohesion of groups over time, the nature of their interactions with other groups, and the nature of their sexual lives. In some of these ways, humans and chimpanzees are quite similar—in others, strikingly different. Both chimpanzee and human societies are “nested.” Chimpanzees organize themselves in bands of approximately 50 individuals, who together occupy a single foraging territory, and defend it against other bands. Within these bands, chimps form close grooming and foraging coalitions of some 5–10 members, with shifting dominance hierarchies and composition. At puberty, females typically wander off to join other groups. Males may leave a group in the face of bullying by other males, or join when they see an opportunity to climb the group’s social ladder. Human groups are also nested, but with more levels. In traditional hunter-gatherer societies, in addition to family groups, individuals form camp groups of approximately 50 members (equivalent in size

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to chimpanzee communities), clans of about 150 members, endogamous (mating) communities of 500, and ethnolinguistic groups (tribes) of approximately 1500 (Dunbar 1998). In modern human societies, individuals consider themselves members of even larger groupings— including religious groups, political parties, and nation-states—with millions of members. So, the major difference is not so much group size, but the number of different layers.

Chimpanzee Sex Apart from our general appearance, perhaps the most striking differences between chimpanzees and humans lie in our sexual and associated social arrangements. Like humans, chimpanzees live in mixed groups of females and males, and so the matter of who has sex with whom is especially important. In keeping with one of evolution’s core design principles, males and females in both species have a strong self-interest in securing attractive mates (i.e., those with physical and behavioral traits that advertise their genetic fitness for survival in the selected habitat), and in ensuring that as many offspring as possible reach the point of sexual maturity. It is in this way that males and females in both species seek to ensure the survival of their own selfish genes. Although the self-propagation problem is the same, and despite our close genetic relationship, chimps and humans have evolved radically different solutions. Put simply, the difference is that between promiscuity and monogamy. As in most primate species, male chimps have a strong, testosterone-driven urge to mate with as many fit-looking females as possible and are especially well-equipped for their sperm-spreading mission. Their testicles are three times as large as those of human males, they can ejaculate more frequently and copiously, their sperm is less likely to be defective (5% versus 25% in humans), and their seminal fluid tends to coagulate, thus creating a vaginal plug against the sperm of competing males (Birkhead 2000). Interestingly, female chimps only partly cooperate with the male strategy. When a female is in “estrus” (ovulating), everyone knows, because her genitals become swollen and bright pink. Males naturally

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find this exciting: the female gets plenty of attention and is not averse to actively seeking attention herself. During any given period of estrus, which can last for up to 14 days (approximately 40% of a 36-day cycle), a female can have as many as 100 copulations with multiple males (Stumpf and Boesch 2005). But that’s not the whole story. Female chimps face a conflict between the risk of infanticide (baby killing)—practiced by many male primates, including chimpanzees and, occasionally, modern humans—and the need to select breeding partners with evident fitness. The dilemma is this. If a female is highly selective—only mating, say, with a single attractive male—then an infant’s paternity will be a matter of public knowledge. Other males, confident the baby is not their own, will be tempted to kill it as a means of bringing the female back into estrus more quickly, for their own selfish purposes. In defense of her offspring, a female can seek to confuse paternity by mating with multiple partners, but this means she largely relinquishes control over the destiny of her DNA.6 Chimpanzee females, it turns out, solve this problem with a clever, two-pronged strategy. The important factor is that during the period of estrus, the probability of fertilization is especially high for only about four days. Females take full advantage of this discrepancy, taking on all comers during their less fertile periods, then, during periods of maximum fertility, seeking out and encouraging advances from males they perceive as especially fit (Stumpf and Boesch 2005).7 In this way, the female’s promiscuity during the less fertile period increases her popularity and standing in the group, and at the same time reduces the chance of infanticide by obscuring paternity. But when it matters most, she chooses Mr. Right. (As we’ll see, the female chimp’s ability to make such a choice may provide a clue to the origin of a radically different strategy—pair bonding—in human ancestors.) Although the female chimp’s two-pronged mating solution is a good one, it’s not perfect. For one thing, it doesn’t eliminate the risk of infanticide, because males must still weigh the odds that a given infant is theirs, and may calculate that if a female has had many partners, the baby is likely not theirs. Worse, because males cannot be certain that an infant carries their own DNA, they have no good reason to help look after it,

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leaving mothers without protection or provisioning assistance. As single moms, chimps manage nicely, of course, but…

What’s Love Got to Do with It? …from our perspective, the human solution surely seems a better one. Like swans, crows, ravens, mourning doves, bald eagles, marmoset monkeys, and prairie voles (but not montane voles, nor any of the other great apes), we humans are prone to fall in love, settle into more or less monogamous relationships, and raise families together, a strategy known as cooperative breeding (Hrdy 2017; Isler and Van Schaik 2012).8 Crucially, our tendency to form relatively long-term, monogamous sexual relationships—unique among the great apes—is not something we humans decide to do only under pressure from lovers, concerned friends, religious institutions, or family. Rather, as it is with swans and prairie voles, human monogamy is deeply biological. Just as chimps are made for promiscuity, so are we made for monogamy. This plan is evident throughout our genetic program: in the relatively equal size of male and female bodies, the nature of our reproductive organs, our brain chemistry, our life histories, and even the size and shape of our teeth. This does not mean that human pair bonding is obligatory, nor that pair bonds are invariably permanent. Promiscuity, adultery, divorce, polygamy, and other departures are part of the human condition, just as they are in other pair-bonding animals—even swans are known to “divorce” (Minton 1968). But if pair bonding had not evolved as our ancestors’ preferred mating strategy, we would be a very different animal. For one thing, as noted above, humans are significantly less dimorphic than the other great apes, meaning that male and female bodies are more nearly the same size. This, and our lack of fangs, have been interpreted as direct indicators of pair bonding in our most distant ancestors (Lovejoy 2009).9 Relieved of the need for continuous competition for sex, males don’t need to invest as much in strength and physical stature, and—although this is likely not the only reason—males and females alike can get away without the large, sharpened canine teeth found in other apes.10 Reduced emphasis on promiscuity, sperm competition, and

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male-on-male intragroup violence is also reflected in changes in human reproductive organs. Over the course of hominin evolution, and possibly early on (long before the invention of clothing), ovulation in human females became largely hidden. As a result, males couldn’t so easily tell when a female was fertile, and so, given the reduced odds of successful impregnation at any given point in time, it became much costlier—even if measured only in terms of sperm investment—to mate with multiple females. This seems to explain, at least partly, why human males, having avoided the sperm competition game, can get away with smaller testicles, smaller amounts of ejaculate, and less healthy semen than chimpanzees. The tendency in humans to focus attention on a single sexual partner over relatively long periods of time is supported by a chemical “attachment mechanism” quite likely evolved from the same mechanism, widespread among mammals, that binds mothers and their children (Fletcher et al. 2015). A key ingredient is oxytocin—the same hormone released when one chimpanzee scratches another’s back. Oxytocin release is also triggered in mothers by cervix expansion during birth, and by stimulation of the nipples during breast-feeding. The hormone is known to have multiple functions in many animals: as an aid in milk production, in the strengthening of bonds between mothers and their babies, and, in all pair-bonding animals, the formation and maintenance of strong emotional bonds between mates. For example, it turns out that the chief difference between pair-bonding prairie voles and promiscuous montane voles is a richer set of oxytocin (and vasopressin receptors—a male equivalent) located near the “pleasure centers” of the brains of the former species (Insel 2010). Oxytocin has also been found to play a more general socializing role in humans, intensifying, for example, feelings of in-group solidarity (Stallen et al. 2012). The advantages of monogamous pair bonding are numerous, for both males and females. From the female perspective, mating with only one male at a time encourages male provisioning. If a male can be reasonably certain that a child is his, then it makes good sense to help protect and feed it, not kill it. In this way, pair bonding gives the male more direct control over the fate of his own genes and saves him the time, energy, and physical risk required to guard multiple female companions against the attentions of other males. Further, both males and females

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benefit from efficiencies associated with division of family chores. Freed from the need to husband a harem or hang around and fight for access to multiple females—and having some reason to trust in his mate’s fidelity—a pair-bonded male can extend his foraging range, thereby increasing the likelihood of returning with food to share with family members. Pregnant females, and those with small children, can remain behind and concentrate their energies on child-rearing, local foraging, and camp chores without having to worry so much about fending off the unwanted attentions of roving male suitors. But if monogamous pair bonding is such an advantage over promiscuity and sperm competition, why haven’t all animals—particularly chimpanzees and the other great apes—evolved the same solution? Part of the answer, it seems, is that maintaining strong, ongoing social bonds with mating partners and other family members requires substantially more brainpower than the cognitively simpler mating practices adopted by so many other animals. In a survey of bird species, for example, a positive correlation has been found between brain size and monogamous pair bonding (Shultz and Dunbar 2010). The more exacting cognitive demands imposed by cooperative breeding in birds—building nests, sharing food, provisioning young, and defending local territories— apparently created pressure for a bigger brain. So much more so for primates, who must keep track not only of their own and others’ places in complex dominance hierarchies, but also their kinship relationships. It matters, for example, whether the alpha male in your group is your uncle, or someone else’s. Managing a long-term, cooperative partnership with a member of the opposite sex on top of these other daily tests of social intelligence requires even more brainpower. So, unless the additional cost of feeding a larger brain can be offset by stable access to a sufficiently nutritious diet, pair bonding remains out of reach for many animals. This has apparently been the case for the other great apes, all of whom have chosen to remain single in one way or another. In short, against the odds, at some point human ancestors became relatively monogamous. True, modern humans engage in different kinds of arrangements across cultures, including polygamy; paternity is sometimes in question; males do not always provision and protect mothers of

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their offspring, in spite of the biological incentive to do so; and infanticide is not unknown. But, as we’ll see, a tendency toward monogamy, pair bonding, and cooperative breeding turns out to be an integral component of the full suite of behavioral traits—including our unique, biologically-determined life history—that sets humans apart from the other great apes.

Differences in Life History The life history trajectories of chimpanzees and humans are broadly and qualitatively similar, with some significant quantitative differences. Human babies spend about a month longer in the womb and are thus slightly heavier at birth. However, in both species, newborn infants are helpless, completely unable to get around on their own (unlike antelope and giraffes and grazing animals, which are up and running within hours), and completely dependent on their mothers for food. Both species have an extended juvenile period compared to other animals— though the childhood phase is significantly longer (about 1.4 times longer) in humans. Juvenile chimpanzees stay close to their mothers until they reach puberty, which occurs at around age 10 for females and a bit later for males, similar to the human values. Chimpanzee females first give birth at around 15 years, compared to 20 in modern huntergatherers, but in humans this is likely at least partly the result of cultural norms. The biggest lifestyle differences show up later. For one thing, humans live considerably longer. Although captive chimpanzees can live into their 60s, this is as likely as a human living to be a 100. At age 15, average chimpanzee life expectancy is an additional 15 years, as compared to 39 more years for humans. Less than 10% of wild chimpanzees survive to age 40, but more than 15% of modern hunter-gatherers can expect to survive to age 70 (Kaplan et al. 2000). Another major difference is that whereas human females live out more than one-third of their adult lives after menopause, chimpanzee females seldom survive much beyond their reproductive period.

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There are also distinct differences in the timing of brain development. Human brains grow more rapidly—ultimately, as noted above, becoming approximately three times the size of a chimpanzee brain at around the onset of puberty, the point at which chimp brains also stop growing. However, human brains retain plasticity and continue to mature and rewire themselves well into adulthood (Miller et al. 2012). As we’ll see, this pattern is consistent with the longer period required for social learning and teaching of cultural knowledge and technical skills in humans.

Cognition, Communication, and Collaboration Chimpanzees and humans use their brains for many of the same purposes, and many of the same purposes our common ancestor ape must have used its brain for in the jungles of the Miocene some 6–10 million years ago. At root, both human and chimp brains are offthe-shelf mammal brains, specialized for solving problems of survival in a dangerous and complex terrestrial habitat, a world of highly skillful predators and competitors, limited mating opportunities, shifting weather patterns, and patchy food sources of uncertain and varying quantity and quality. In other words, pretty much the same problems that any earthbound animal faces, and which it must have the intelligence to solve if it is to survive in whatever habitat it has chosen or been forced into. As a result, among other inherited cognitive capacities, both chimps and humans can build mental maps of their territories, learn the location of important sites within this territory (food patches, nesting sites, watering holes, and, in humans, subway stops), learn and recall safe and efficient routes from one location to another, and, given one location, compute the shortest route to another. Both chimps and humans can also build and populate mental categories (e.g., friend vs. foe, suitable vs. unsuitable mate, palatable vs. unpalatable food item, useful vs. less useful stick or stone or screwdriver) and decide what category a given object or individual falls into based on observable features. And both species can

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learn and choose from among strategies for accomplishing critical feats such as avoiding predators, capturing prey, attracting mates, and, drawing on experience, can improve these strategies over time. In addition to these basics, chimpanzees and humans, as primates, have also inherited brains specialized for life in a social group. This means we can learn and quickly recall the identities of those around us, distinguish between family, kin, and friends, and learn how each can be expected to behave and what they may expect from us. We can also remember and recall the present state of our current relationships based on past dealings, such as whether another individual might bear a grudge against us, or owe a debt. We can develop theories about the intentions of others based on their past and current behavior toward us and others (e.g., we can identify a snarled threat), and select from among a repertoire of countermeasures. And we can figure out where we stand within a given social hierarchy, how best to behave given this standing, and what we might do to improve our standing. All of this said, humans and chimpanzees clearly have different cognitive capacities, arising from, and allowing, different solutions to the same social problems. The issue is what these cognitive differences are, and what they tell us about our divergent evolutionary paths. One important question—still not completely answered—is the extent to which chimpanzees have the sociocognitive skills necessary for thoughtful collaboration with each other. A case in point is the debate about the nature of chimpanzee predation, especially their hunting of red colobus monkeys, a tree-dwelling Old World monkey that shares habitats with chimpanzees, notably in the Tai, Gombe, Ngogo, and Mahale National Parks of Tanzania, where much of the research has been done. The questions are why wild chimpanzees hunt monkeys, how hunts are organized, and, more specifically, whether the behavior of the hunters is an example of intentional cooperation, or alternatively, the result of the selfdirected and ultimately self-centered behavior of individuals. In other words, is what looks like teamwork just the result of individuals doing their own thing? Since the role of hunting is a key issue in the study of human evolution, since hunter-gatherer populations—especially in northern latitudes—depend on hunting as a major source of nutrition, and since modern humans are clearly capable of highly sophisticated

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and intentional teamwork, with individually assigned roles and responsibilities (as exemplified in team sports such as football and basketball), answers to questions about the dynamics of chimpanzee monkey hunting have important implications.

The Case of “Cooperative” Monkey Hunting The red colobus is the preferred prey, though not by far the primary food source, of wild chimpanzees in East Africa. Like all primates, these monkeys are social animals and forage in troops as large as 30–40—just slightly smaller than chimpanzee bands. Troops consist of females (some carrying infants), juveniles, and males. Like all monkeys, the colobus are specialized for arboreal acrobatics, capable of leaping a distance of some twenty feet from one tree to the next, and are also good at fending off predators. Troop members take up a chorus of alarm cries when a chimpanzee is sighted, especially when large numbers of females with infants are in the group. And like crows after an owl, the monkeys will mob a single chimpanzee, forcing it to the ground (Stanford 1998). Successful colobus hunting is therefore partly a numbers game—the more recruits the better—and partly a matter of knowing what to do and when. According to one account, when a chimpanzee sights a troop of monkeys and decides to give chase (some are more enthusiastic hunters than others), members of the recruited hunting party can seem to assume different roles, apparently designed to maximize the chances of group success. “Blockers” station themselves at obvious escape routes, while “drivers” chase the fleeing monkeys through the trees, apparently not trying to make the catch themselves, but instead guiding the victims in the direction of “ambushers” and “chasers,” who attempt the actual kill. If a hunt is successful, the carcass is shared according to a system that seems to reward individuals in accordance with the significance of their contributions to the group’s success (Boesch 2002). Interpreted in this way, such a hunt seems an example of human-like, goal-oriented teamwork. But is it really?

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Clearly colobus hunting is a sophisticated and cognitively challenging activity, depending on quite a lot of individual thinking, though not, perhaps, collective thinking. To be successful, chimp monkey hunters must each track the path of the fleeing monkey, guess its intentions, and react accordingly—blocking a potential escape route, laying an ambush, or rushing in for the kill. Individuals must also keep track of the position of the other hunters, guess their intentions, and deploy themselves in a way that makes the most sense within the rapidly changing circumstances of the mad scramble. Finally, after a successful hunt, the group must somehow divide the bounty based on some calculation of individual effort, if that is indeed what is going on. At any rate, the question is not whether the chimps have the intelligence to engage in collective hunting and food sharing—as they clearly do—but rather whether this is an example of (a) what the primatologist Michael Tomasello has called shared intentionality (based on an understanding that “we” are engaging in this activity together), or (b) what has been called “by-product mutualism,” the idea that the appearance of intentional teamwork simply emerges from the intelligent, self-directed behavior of individuals (Tomasello and Carpenter 2007). Are the chimps really acting as a self-coordinating team, each playing agreed roles, or does it just seem that way? The question is a critical one because it takes us to the crux of what is arguably the most important difference between human and chimpanzee cognition. Is a “blocker” thinking something like “I think I ought to stay here at the base of this tree because, even though it reduces my own chance of making a kill, it’s going to help the other guys, and they’ll take care of me later?” Although we can’t get inside a chimp’s head, there are at least three reasons why this is almost certainly not what the blocker is thinking. First, and most obviously, chimpanzees don’t talk like that, and so they almost certainly can’t think like that. Second, and more subtly, an explanation based on the emergent behavior hypothesis is sufficient, and thus more economical, than one that requires chimpanzees to have evolved a capacity for shared intentionality (Gilby and Connor 2010). Individual robots, after all, can be programmed to behave in ways that resemble coordination, without the need to communicate with each

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other (Franklin 1996). Third, as I’ll explain, laboratory evidence suggests that chimpanzees are in fact not quite capable, cognitively, of true shared intentionality. They seem not to think of themselves as playing an individual role in an intentional activity that “we” are engaging in together, simply because their minds don’t work that way. Support for the “byproduct” (emergent behavior) explanation comes, first, from at least two important pieces of evidence about colobus monkey hunting. One has to do with the possible reasons why chimpanzees make the effort to hunt monkeys in the first place. Three hypotheses have been proposed. First is the “diet supplement” hypothesis. Given that monkey meat provides a concentrated source of protein, significantly more nutritious than a fistful of berries, it could be that the chimps are interested primarily in supplementing their standard diet of fruit, nuts, leaves, and insects. The second is a “meat for sex” hypothesis. To the extent that the spoils of the hunt are shared with females, you might think the males are hoping to improve their mating chances, the equivalent of a romantic dinner in a fancy restaurant. The third explanation might be called the “guy thing” hypothesis. Given that hunting is largely a male activity, and that males mostly share meat with other males, it may be less about food or sex than about getting along with other males. As it turns out, the evidence suggests that chimpanzee monkey hunting is indeed a guy thing, not unlike, say, nighttime raccoon hunting among men in rural Mississippi (Young et al. 2001). As in coon hunting, it seems the purpose is not so much to acquire a rich source of protein, or to provide a taste treat to a special female friend in the hope of gaining a sexual favor. Rather, it seems that chimpanzee monkey hunting is bound up with male dominance hierarchies and opportunities for an individual to elevate his standing by participating in the hunt, showing off his skills, and sharing the spoils with other hunters (Mitani and Watts 2001). At least three pieces of evidence support the male bonding hypothesis. First, monkey hunting occurs most frequently when other food resources are plentiful, which seems to rule out the food scarcity hypothesis. The nutritional value of a small monkey (most victims are infants or immature juveniles) split into five or ten pieces, is apparently not worth the energetic costs of chasing a troop of fast and frightened potential victims

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through the trees, and possibly failing to catch a single one—unless you already have something in your stomach and so can afford to make the effort. In other words, monkey hunting is a rich chimp’s sport. The second piece of evidence, already noted, is that successful monkey hunters are more likely to share food with other males than with females. Third, the total number of males in a group is a better predictor of monkey hunting frequency than the number of ovulating females. If monkey hunting were about sex, you’d expect to see more of it with more interested females around to impress. This is not to say that sex and nutritional benefits don’t play a role. After all, chimps wouldn’t invest the considerable energy it takes to catch a fleeing monkey if the monkey’s flesh were poisonous or completely devoid of nutritional value. And increasing one’s status in the male hierarchy ultimately (though indirectly) increases mating opportunities. It’s just that food and sex seem not to be primary purposes. As we’ll see, this interpretation has relevance to the emergence of hunting in hominins, which some scientists believe evolved for much the same reasons that seem not to be reasons for chimpanzee monkey hunting—as a nutritional supplement and means of provisioning kinship groups. Another important thing to know about chimpanzee monkey hunting is that it takes years to learn the requisite skills, just as it does for humans in remaining hunter-gatherer societies. Young male chimps start to participate in hunts at around the age of 10, but don’t acquire full expertise until around the age of 20, about the same length of time it takes chimps to become expert at cracking open nuts with rocks (Matsuzawa 1996). There’s also a range of interest in hunting among males in any given group. Certain males, so-called impact hunters, demonstrate more interest in hunting than their ages would suggest, and are more likely than others to initiate hunts (Gilby et al. 2015). In short, it seems the differing behavior of individual chimpanzee monkey hunters, and the collective behavior of the hunting party as a whole, is best viewed through the lens of social learning, shifting dominance hierarchies, and male coalition building. The roles that individuals seem to take on—blocker, driver, ambusher, or chaser—could easily be a function of some combination of factors including age and current level of skill, position in the dominance hierarchy (including a desire

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to improve one’s status), and the rapidly shifting circumstances of the chaotic hunt itself. And those who make the kill share the bounty with others not necessarily out of a sense of fairness, or to reward those who serve by staying in place, but rather as a way of building and maintaining relationships with other males. It seems that monkey hunting is a collective enterprise, but not necessarily a cooperative one. As I suggested earlier, yet another reason to think that chimpanzees are not capable of truly cooperative intentional activity is based on experimental laboratory evidence. In one protocol, chimpanzees learn to cooperate with human researchers to obtain food by pulling on two ropes, the chimp on one and the human on the other. After bringing another chimpanzee into the room, the researcher departs, leaving the two chimps alone. Even though continued access to food depends on the expert getting its naive partner to help, the expert makes no attempt to teach the requisite skills, leaving the novice to work them out for itself if it can. Just as mother chimpanzees are almost never seen actively instructing their offspring in the craft of termite fishing, the experts trained by humans in the rope-pulling technique do not turn around and teach their chimpanzee partner. They do not point at the novice’s rope and make pulling gestures, despite the incentive to do so. Why not? Perhaps the best explanation is that chimpanzees have a kind of cognitive blind spot. It just doesn’t occur to them that a simple action—such as pointing or pulling on a rope—could have a communicative function, in the sense of conveying information that could be processed by a fellow creature and lead to a change in that creature’s behavior. As we’ll see, captive chimpanzees learn to point “imperatively” at objects they want humans to give them. But wild chimpanzees hardly ever point, and even captive chimpanzees seem never to point as a way of sharing information about the location of an object for the benefit of another chimpanzee, apparently because they don’t conceive of others as having minds of their own which could be influenced through an intentional communicative act. In stark contrast, by around 12 months, human children have begun pointing for a wide variety of purposes, including identifying the location of objects for the benefit of others. At around the age of 3 or 4, they begin employing increasingly sophisticated strategies for teaching other children—such as how to play a game—using combinations of physical

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demonstrations, verbal instructions, and negative and positive feedback (Strauss et al. 2002).

Chimpanzee and Human Communication Compared This brings us to what is arguably the most consequential difference between humans and chimpanzees: our different methods of communication, and what these differences tell us about our ways of thinking. Chimpanzees are active and effective social communicators, drawing on a repertoire of facial expressions, vocalizations, and more than 60 different gestures to manipulate the behavior of others (Hobaiter and Byrne 2011). Chimps and bonobos, along with other primates, have also been reported to engage in something very much like gestural turntaking. For example, a mother chimp may make a gesture inviting an infant to follow her, wait for a response, and then, if the infant fails to follow, or indicates confusion, the mother may make a different gesture as a way of clarifying her intent (Fröhlich et al. 2016). You can, if you want, call this a kind of “language” in the sense that different signals have distinct meanings. But, while it incorporates all these elements—facial expressions, cries (e.g., in English, “Whew!” “Ouch!” “Yay!”), and arm and hand gestures—human language is deeply different, and evidently unique in the animal kingdom. In human language, a small set of distinct sounds (phonemes) can be combined, in accordance with rules of morphology, to form an infinite number of possible units of meaning (words). And these words can in turn be combined, in accordance with the rules of syntax, to produce an infinite number of meaningful utterances (sentences). Most remarkably, while utterances in human language can refer to things in the world (“Watch out for that car!”), they can also refer to things that exist only in the mind of the interlocutor (“Watch out for cars” or “For ground floor, press B.”) But, if language is so useful, and if we share such large chunks of DNA with chimpanzees, why didn’t language also evolve in our sister species? It seems we already have part of the answer. Ever since our lineages split,

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no more recently than about 6 million years ago, and despite our close genetic relationship, the ancestors of modern chimpanzees and humans followed radically different evolutionary pathways, evolving markedly different means of locomotion, different sexual practices, different social arrangements, and different life histories and lifeways. As we’ll see, these stark differences in other aspects of our adaptive suites go a long way toward explaining why humans alone evolved language, and the ability to use language for teaching and learning. This is the topic of the next chapter.

A Note on the Issue of “Human Uniqueness” This seems as good a place as any to address the question of “human uniqueness.” As you will have discovered by now, a central theme of the book is that, for reasons scientists have only begun to understand, we humans have diverged radically from the evolutionary paths followed by our primate relatives, and indeed all other animals on Earth, so much so that we hardly recognize ourselves for what we are: nearly hairless, bipedal, large-brained, language-using, love-prone, teaching apes. These and other traits make us truly special, but we mustn’t let that go to our heads, or fool ourselves into thinking that we’ve somehow set ourselves completely apart from the rest of nature. In truth, we are no more unique, nor truly intelligent, than elephants, bats, octopuses, ravens, or any other such marvels—just unique and intelligent in different ways. At the same time, we mustn’t let a misplaced humility prevent us from recognizing how special we are. Yes, many other animals have sophisticated methods of communication one might be tempted to call “language.” Other animals use tools, practice agriculture, mourn the death of friends, and teach. So, right, we aren’t that different. And in fact, if we were totally different, completely unlike any other animal, our unique traits would be a lot harder—no, impossible—to explain. We’d have to look elsewhere for our origins, beyond natural evolutionary processes. Perhaps, you might think, our distant ancestors were aliens and traveled here on spaceships.

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True, as we think about differences between humans and other animals—especially when these have to do with how other animals think and communicate with each other—we have to admit that we don’t really know what’s going on. Perhaps other animals ask each other questions, make promises, and tell stories and lies. Perhaps the songs of humpbacked whales are something more than love serenades. It seems you’d have to have a whale’s brain to know for sure. But, for the same reason, other animals can’t know what humans talk about, or how we think, because they don’t have human brains. In short, the question is really not whether humans are unique: we aren’t and we are. The real question is how, in just 6 million years, we came to be unique in the special ways we are.

Food for Thought 1. Exactly why is it wrong to say that humans are a “third chimpanzee?” 2. If chimpanzees and humans share so much DNA, why do chimpanzees seem so different from us? 3. Do chimpanzees have minds? If so, how are chimpanzee minds different from human minds? 4. Are humans more intelligent than chimpanzees? 5. Can a chimpanzee fall in love?

Notes 1. Although all great apes are social to some degree, some are more so than others. Many adult orangutans, for example, are drifters, leading largely solitary lives. 2. In accordance with recent usage, the term hominin refers to the group consisting of modern humans and all our direct ancestors. Hominids include hominins, and the other great apes. 3. Throughout the book, when I refer to Nature in this way, understand this as shorthand for the natural processes of biological evolution.

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4. Bushbabies (galagos) are small nocturnal primates native to continental Africa. 5. At this writing, dating of the earliest use of fire for cooking remains a matter of debate. Estimates range from some 1.8 million years ago to as recently as 400,000 years ago. For a recent review, see Stahlschmidt, M. C., Miller, C. E., Ligouis, B., Hambach, U., Goldberg, P., Berna, F., et al. (2015). On the evidence for human use and control of fire at Schöningen. Journal of Human Evolution, 89, 181–201. 6. Some researchers argue that mating with multiple partners is beneficial in that it increases genetic diversity. For example, if only some males carry a gene that protects against a certain pathogen, then mating with multiple males increases the likelihood that at least one of the female’s offspring will have the protective gene. See Quinlan, R. J. (2008). Human pair-bonds: Evolutionary functions, ecological variation, and adaptive development. Evolutionary Anthropology: Issues, News, and Reviews, 17 (5), 229. 7. I don’t mean to suggest that female chimp is conscious of this strategy in the way that a female human might be when she decides whether to take a birth control pill. 8. You may think I’m being excessively romantic to describe swans (even more so, prairie voles) as capable of falling in love. Read on. 9. As discussed later in the chapter, we have to be careful in assuming that the last common ancestor (LCA) of chimpanzees and humans was promiscuous. It’s possible that the LCA was a pair-bonder, and that promiscuity in chimpanzees is a derived trait, having emerged after the split between the two lineages. 10. The relationship between pair bonding and the lack of large, sharpened canines in humans is controversial. Other explanations include changes in diet, and the development of weapons, which made fangs unnecessary. See Washburn, S. L. (1960). Tools and human evolution. Scientific American, 203(3), 62–75. Lovejoy (2009) argues that the loss of what he calls the “sectorial canine cluster,” and, by extension, pair bonding, occurred in Ardipithecus ramidus—much earlier in hominin evolution than either the emergence of weapons or changes in diet.

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Suggested Reading Mitani, J. C., & Watts, D. P. (2001). Why do chimpanzees hunt and share meat? Animal Behaviour, 61(5), 915–924. The source of the explanation given in this chapter. Tomasello, M., & Carpenter, M. (2007). Shared intentionality. Developmental Science, 10 (1), 121–125. A good introduction to an important difference between human and chimpanzee cognition, central to a major theme in the book. White, T. D., Lovejoy, C. O., Asfaw, B., Carlson, J. P., & Suwa, G. (2015). Neither chimpanzee nor human, Ardipithecus reveals the surprising ancestry of both. Proceedings of the National Academy of Sciences, 112(16), 4877–4884. The source of the title of this chapter.

References Almécija, S., Smaers, J. B., & Jungers, W. L. (2015). The evolution of human and ape hand proportions. Nature Communications, 6 , 7717. Benton, M. J., Donoghue, P. C. J., & Asher, R. J. (2009). Calibrating and constraining molecular clocks. In S. B. Hedges & S. Kumar (Eds.), The timetree of life (pp. 35–86). New York: Oxford University Press. Birkhead, T. (2000). Promiscuity: An evolutionary history of sperm competition. Cambridge: Harvard University Press. Boesch, C. (2002). Cooperative hunting roles among Tai chimpanzees. Human Nature, 13(1), 27–46. Boesch, C., & Boesch, H. (1990). Tool use and tool making in wild chimpanzees. Folia Primatologica, 54 (1–2), 86–99. Carvalho, S., Biro, D., Cunha, E., Hockings, K., McGrew, W. C., Richmond, B. G., & Matsuzawa, T. (2012). Chimpanzee carrying behaviour and the origins of human bipedality. Current Biology, 22(6), R180–R181. Crockford, C., Wittig, R. M., Langergraber, K., Ziegler, T. E., Zuberbühler, K., & Deschner, T. (2013, March). Urinary oxytocin and social bonding in related and unrelated wild chimpanzees. Proceedings of the Royal Society B, 280 (1755), 20122765.

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Deacon, T. W. (1997). The symbolic species: The co-evolution of language and the human brain (pp. 322–334). New York: W. W. Norton. Diamond, J. (2014). The third chimpanzee. Oneworld Publications. Dunbar, R. (1998). Grooming, gossip, and the evolution of language. Cambridge: Harvard University Press. Fletcher, G. J., Simpson, J. A., Campbell, L., & Overall, N. C. (2015). Pairbonding, romantic love, and evolution: The curious case of Homo sapiens. Perspectives on Psychological Science, 10 (1), 20–36. Franklin, S. (1996). Coordination without communication (Unpublished manuscript). University of Memphis. Fröhlich, M., Kuchenbuch, P., Müller, G., Fruth, B., Furuichi, T., Wittig, R. M., & Pika, S. (2016). Unpeeling the layers of language: Bonobos and chimpanzees engage in cooperative turn-taking sequences. Scientific Reports, 6 , 25887. Garber, P. A. (1987). Foraging strategies among living primates. Annual Review of Anthropology, 16 (1), 339–364. Gilby, I. C., & Connor, R. C. (2010). The role of intelligence in group hunting: Are chimpanzees different from other social predators. In E. V. Lonsdorf, S. R. Ross, & T. Matsuzawa (Eds.), The mind of the chimpanzee: Ecological and experimental perspectives (pp. 220–233). Chicago: University of Chicago Press. Gilby, I. C., Machanda, Z. P., Mjungu, D. C., Rosen, J., Muller, M. N., Pusey, A. E., & Wrangham, R. W. (2015). ‘Impact hunters’ catalyse cooperative hunting in two wild chimpanzee communities. Philosophical Transactions of the Royal Society B, 370 (1683), 20150005. Hamilton, W. D. (1963). The evolution of altruistic behavior. The American Naturalist, 97 (896), 354–356. Hobaiter, C., & Byrne, R. W. (2011). The gestural repertoire of the wild chimpanzee. Animal Cognition, 14 (5), 745–767. Hoffecker, J. F. (2013). The information animal and the super-brain. Journal of Archaeological Method and Theory, 20 (1), 18–41. Hrdy, S. B. (2017). Comes the child before man: How cooperative breeding and prolonged postweaning dependence shaped human potential. In M. E. Lamb & B. S. Hewlett (Eds.), Hunter-gatherer childhoods (pp. 65–91). New York and Oxfordshire: Routledge. Insel, T. R. (2010). The challenge of translation in social neuroscience: A review of oxytocin, vasopressin, and affiliative behavior. Neuron, 65 (6), 768–779. Isler, K., & Van Schaik, C. P. (2012). How our ancestors broke through the gray ceiling: Comparative evidence for cooperative breeding in early homo. Current Anthropology, 53(S6), S453–S465.

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Jacob, F. (1977). Evolution and tinkering. Science, 196 (4295), 1161–1166. Kaplan, H., Hill, K., Lancaster, J., & Hurtado, A. M. (2000). A theory of human life history evolution: Diet, intelligence, and longevity. Evolutionary Anthropology: Issues, News, and Reviews, 9 (4), 156–185. Lovejoy, C. O. (2009). Reexamining human origins in light of Ardipithecus ramidus. Science, 326 (5949), 74–74e8. Matsuzawa, T. (1996). Chimpanzee intelligence in nature and in captivity: Isomorphism of symbol use and tool use. In W. C. McGrew, L. F. Marchant, & T. Nishida (Eds.), Great ape societies (p. 196). Cambridge: Cambridge University Press. Miller, D. J., Duka, T., Stimpson, C. D., Schapiro, S. J., Baze, W. B., McArthur, M. J., et al. (2012). Prolonged myelination in human neocortical evolution. Proceedings of the National Academy of Sciences, 109 (41), 16480–16485. Minton, C. D. T. (1968). Pairing and breeding of Mute Swans. Wildfowl, 19 (19), 41–60. Mitani, J. C., & Watts, D. P. (2001). Why do chimpanzees hunt and share meat? Animal Behaviour, 61(5), 915–924. Napier, J. R. (1956). The prehensile movements of the human hand. The Journal of Bone and Joint Surgery: British Volume, 38(4), 902–913. Odling-Smee, F. J., Laland, K. N., & Feldman, M. W. (1996). Niche construction. American Naturalist, 147, 641–648. Sayers, K., Raghanti, M. A., & Lovejoy, C. O. (2012). Human evolution and the chimpanzee referential doctrine. Annual Review of Anthropology, 41, 119–138. Shultz, S., & Dunbar, R. I. (2010). Social bonds in birds are associated with brain size and contingent on the correlated evolution of life-history and increased parental investment. Biological Journal of the Linnean Society, 100 (1), 111–123. Sinha, C. (2015). Language and other artifacts: Socio-cultural dynamics of niche construction. Frontiers in Psychology, 6, 1601. Sinha, C. (2017). Language as a biocultural niche and social institution. In Ten lectures on language, culture and mind (pp. 138–154). Leiden and Boston: Brill. Stallen, M., De Dreu, C. K., Shalvi, S., Smidts, A., & Sanfey, A. G. (2012). The herding hormone: Oxytocin stimulates in-group conformity. Psychological Science, 23(11), 1288–1292. Stanford, C. B. (1998). Chimpanzee and red colobus: The ecology of predator and prey. Cambridge: Harvard University Press.

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Stauffer, R. L., Walker, A. L. A. N., Ryder, O. A., Lyons-Weiler, M., & Hedges, S. B. (2001). Human and ape molecular clocks and constraints on paleontological hypotheses. Journal of Heredity, 92(6), 469–474. Strauss, S., Ziv, M., & Stein, A. (2002). Teaching as a natural cognition and its relations to preschoolers’ developing theory of mind. Cognitive Development, 17 (3), 1473–1487. Stumpf, R. M., & Boesch, C. (2005). Does promiscuous mating preclude female choice? Female sexual strategies in chimpanzees (Pan troglodytes verus) of the Taï National Park, Côte D’Ivoire. Behavioral Ecology and Sociobiology, 57 (5), 511–524. Tomasello, M., & Carpenter, M. (2007). Shared intentionality. Developmental Science, 10 (1), 121–125. White, T. D., Lovejoy, C. O., Asfaw, B., Carlson, J. P., & Suwa, G. (2015). Neither chimpanzee nor human, Ardipithecus reveals the surprising ancestry of both. Proceedings of the National Academy of Sciences, 112(16), 4877– 4884. Wildman, D. E., Uddin, M., Liu, G., Grossman, L. I., & Goodman, M. (2003). Implications of natural selection in shaping 99.4% nonsynonymous DNA identity between humans and chimpanzees: Enlarging genus Homo. Proceedings of the National Academy of Sciences, 100 (12), 7181–7188. Young, A. L., Tuma, M., & Jenkins, C. (2001). The role of hunting to cope with risk at Saragossa Plantation, Natchez, Mississippi. American Anthropologist, 103(3), 692–704.

4 An Evolutionary Explosion

From the perspective of a mind like mine (and I assume yours) which measures its own sadly fleeting existence in decades, and thinks of human history in units of just hundreds or thousands of years, it seems about right that it would have taken nature 6–10 million years to produce such a piece of work as us. But, in evolutionary time, even 10 million years, starting with an ordinary, small-brained, languageless ape, and ending up with a creature capable of flying itself to the moon and back is quick work, nothing less than an evolutionary explosion. What in the world happened? A standard explanation for evolutionary explosions (or a series of explosions) such as the one that seems to have produced a talking, teaching ape is that of an evolutionary arms race. The term traditionally applies to predator-prey arms races, such as that between echolocating bats and tiger moths, where two organisms pressure each other into evolving increasingly sophisticated offensive and defensive systems. As desperate moths got just a bit better at jamming the bat’s rudimentary echolocation system, this put selection pressure on hungry bats to evolve slightly more effective detection, thus putting additional pressure on the moth’s methods of avoiding detection, and so on, over a period of at © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_4

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least 50 million years (Miller and Surlykke 2001). Similar arms races can occur within a species, often driven by sexual selection pressures—as in the case of the peacock’s gaudy tail and the elaborate nests of the bowerbird—where males find increasingly elaborate ways to flaunt their fitness, and females become increasingly picky in response (see Dawkins 1986; Sinha 2015). In the case of the arms race that produced the explosion that sent twelve humans to walk on the moon (which, estimated at 6 million years, took roughly one-tenth the time it took bats and tiger moths to evolve their own wondrous but far less sophisticated technologies), we can identify at least five main participants: brains, language, technology, teaching, and nature itself. Understand that I’m not just saying that this arms race pitted our ancestors against nature, although that was indeed part of it. What I mean, as strange as it may seem, is that hominin brains, language, teaching, and technology must have coevolved in a kind of arms race with each other, under pressure from the demands and opportunities of the natural world. And, here’s the relevant point for the purposes of this chapter: our capacity and inclination for teaching through language seems to have been in the thick of the race from the very beginning. It’s probably not the case that language evolved first and so eventually made teaching possible. Rather, it seems far more likely that the need to teach, and to engage in other kinds of joint attentional activity (such as collaborative foraging and the complex business of family life), put pressure on language to become increasingly sophisticated, which in turn put pressure on brains to keep up.

The Participants Let’s first be clear about the participants in the race: brains, language, teaching, and technology—against the backdrop of the natural world. First, recall that what has become our human capacity for language—and teaching through language—needs to be understood as part and parcel of our full biological package, including not just our brains, and not just the complex, brain-driven vocal and auditory apparatus that eventually

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came to make speech possible, but also those parts of our biology, life history, and lifeways that evolved under pressure to feed our increasingly large, energy-hungry brains. Second, when we think of technology, we must not only think about the hardware—the sticks and stones and twisted fibers our ancestors first began using to extract resources and defend themselves against predators. We must also be thinking about the mindware, including the expertise required to manufacture and use these tools, and our ancestors’ increasing store of hard-won knowledge about the location and behavior of local flora and fauna, including predators, prey, edible and poisonous plants, seasonal fluctuations in the natural environment, and so forth. Tools make no sense outside the context of their use. Third, when we think about language, we need to be thinking not only about what language has become in its fully modern complexity (including high-speed speech, a lexicon of tens of thousands of words, and complex, recursive syntax) but also what language must have been like when it was much simpler: a protolanguage (Bickerton 1992). At the same time, we need to be careful not to imagine a single, discontinuous leap from protolanguage to fully modern language; rather, the evolution of language, and teaching through language, must have unfolded in fits and starts, each phase of which had its own time and place in our evolutionary history. Finally, when we think about the natural world, we need to be thinking about the almost limitless complexity of our changing habitats: the multiplicity of plant and animal species and all their different parts, inside and out, bones and tendons, feathers and intestines; the complex behaviors of predators and prey; their identifying colors and textures; the location of useful rocks and minerals; the location of ponds, lakes, rivers, and streams; weather patterns and seasonal habitat changes; the configuration of stars as they sweep through the night sky; the flow of time, and the need to orient events and actions with respect to time—in short, so very much to know, to talk about as precisely as possible, and, if possible, to teach. So, working backward, we can say that the complexity of modern human language—which allows us to combine a finite set of symbols (words, composed of sounds), in accordance with a highly complex but

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finite set of rules, to convey a potentially infinite number of meanings, intentions, commands, questions, and requests—is a reflection of the nearly infinite complexity of the natural world, and the advantages that accrue from being able to talk about the world, coordinate action in the world with others, teach what we know, and ask about what we don’t. If we lived in a simple world of blocks, and all that mattered was the ability to direct another person’s attention to a certain block and, say, have the person put one block on top of another to form a pyramid, then human language wouldn’t need to be nearly so complex.1 The complexity of modern technology (which now allows scientists, among other things, to cut and paste strands of DNA) is another reflection of nature’s complexity. The same, of course, goes for our brains. If the world were simpler, our brains would be simpler. Technology would be simpler. Teaching would be simpler. But, here again is the 6-million-year question: Why hasn’t every animal on Earth evolved a supersized brain, complex language, complex technology, and complex pedagogy? Inhabitants, with other animals, of the same complex world of risk and opportunity, how did we, and only we, get so special so quickly? If we want to understand what teaching is, how it related to our ancestors’ lives, and, most importantly, how teaching has become essential to our own fragile existence as a species, we must try to understand how this happened.

The Anatomy of an Explosion Any case of explosive evolutionary change raises four important questions. First, as in any explosion, we need to ask about the initial state of the system(s) involved. What was going on before it occurred? Just as fire needs both oxygen and combustible material to get started, so, in the case of evolutionary explosions, evolution needs precursor mechanisms to tinker with. Bowerbird males must already have had the ability to build nests before they started getting fancy. Bats must already have been able to hear their own squeaks. Tiger moths must already have been able to produce tiny clicks—apparently a method of advertising their poisonous flesh (Miller and Surlykke 2001). Similarly, the precursors for

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human language, and teaching through language, must already have been present, in nascent form, in the DNA of the last common ancestor of humans and chimpanzees. Second, we need to know what pressures created the conditions that led to the explosion. Why were our distant ancestors’ existing bodies and ways of life no longer adequate to meet these pressures? What existential threat created the need for change? Either something in these creatures’ accustomed environment began to change, or they had begun moving, likely forced to move, into a new environment. Third, what triggered the explosion? Pressure for change alone is not sufficient. If organisms don’t react quickly enough to existential threats, they die out. What unlikely chain of events set up the positive feedback mechanisms that led to such a massive change, allowing an ordinary ape to survive in such an extraordinary way? Finally, we need to ask what forces constrained the changes, eventually bringing the arms race under control. Once a race gets started, something like runaway growth is theoretically possible, as seems presently the case with modern technologies. But any arms race in the biological world must eventually run up against limits, which lead to standoffs and periods of relative stasis. We don’t know what first inspired male bowerbirds to begin competing for female attention by building increasingly elaborate nests, but we do know that bowerbird nests have never become as elaborate as Antoni Gaudí’s Sagrada Familia, which seems to have been designed with a similar aesthetic! It wasn’t so much that a SagradaFamilia-level bowerbird nest wasn’t worth the effort, or that increasingly demanding bowerbird females finally became satisfied. In the end, the males just didn’t have Gaudí’s capabilities and resources, and what they did produce, which may be unrivaled in this category of male fitness flaunting, was good enough. For similar reasons, while our brains are three times as large as a “normal” primate brain, and, proportional to body size, seven times as large as an average mammal brain (HerculanoHouzel 2009), they are not one hundred times the size. At some point the costs of further growth or complexity in any biological system outweigh the advantages, the system stabilizes, and the explosion dies down to a slow smoldering.

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In short, to understand how it happened that an ordinary ape, and only this particular one, developed such extraordinary powers in just a few hours of evolutionary time, we need to know something about the early descendants of our last common ancestor with chimpanzees and their habitat: what pressures they faced and what initially held them back; what triggered the initial changes and how these changes might have led to the potential for runaway growth; and finally, what constrained the explosion—why, in other words, we aren’t now pushing our brains around in wheelbarrows like giant pumpkins, why we don’t know trillions of words, and why we can’t teach by telepathy.

The Hominin Adaptive Suite Building on ideas introduced in Chapter 3, Fig. 4.1, on the next page, shows some of the traits that comprise the hominin adaptive suite and which contrast most sharply with the corresponding traits of the other great apes (gorillas, chimpanzees, orangutans). Note that the suite includes aspects of our physical anatomy and life history, foraging strategies, cognition (ways of thinking), social arrangements, and culture.2 I’m afraid there’s a lot to take in here. Before we get to the particulars, let’s recall that the key thing to understand about any adaptive suite— whether that of ducks, moths, bats, chimpanzees, or humans—is that an organism’s distinctive traits are highly interrelated, having assumed their form and function in natural and necessary coordination with the form and function of the organism’s other traits, and in response to selection pressures imposed by the more rapidly shifting conditions of its chosen habitat.3 These shifting conditions result from the activities of other coexisting or competing organisms in the same environment, the organism’s own activities and impact on the environment, and local and global climate change, which, as in the case of man-made climate change, may be produced by the organism itself. Because the traits in an organism’s adaptive suite are so tightly coupled, both with each other, and with the features and conditions of the organism’s habitat, changes in any part of the system—including changes

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Fig. 4.1 Hominin adaptive suite. A highly schematic depiction of 26 evolved traits and behaviors that distinguish humans from other species of great ape. The thicker, up-and-down arrows indicate whether the trait has increased or decreased in prominence over time. The thinner, double-ended arrows indicate possible causal relationships; each of the traits, including teaching, is understood to be related, directly or indirectly, to every other (Adapted from Lovejoy 2009)

internal to the organism itself and changes to its external environment—place new selection pressures on other traits to realign themselves accordingly. Specific adaptive responses to these pressures, however, are neither predictable nor inevitable. Looking at the world around us, it seems that evolution has succeeded in engineering every plant and animal to be optimally suited to its place in the world: bats that use a system of echolocation to detect flying moths; moths that jam the moth-detection systems of bats; giraffes and geese with elongated necks, the better to reach food sources that would otherwise be out of reach; dancing honey bees; and so forth. But this vision of ingenious perfection is an illusion. We see only the successful results of the strivings and lucky accidents that have

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favored the millions of species that presently maintain their precarious hold on existence; not the billions of other species that for one reason or another failed to adapt quickly enough to survive natural catastrophes or competition from others, and so became extinct. Life on Earth is risky—especially on the planet’s exposed, rapidly changing surface— and continuing survival depends on an organism’s ability to continually evolve sustainable solutions to an unending and relentlessly shifting series of problems. The basic question we want to address in the remaining sections of this chapter, then, is what existential problems language, and teaching through language, solves—and how the solution relates to other components of our adaptive suite. We know that modern humans are obligatory teachers and language users. We could not long survive as a species in the absence of either teaching or language. We also know that chimpanzees, our closest relatives, are, at best, desultory teachers. They’re good communicators, using combinations of physical gestures, facial expressions, and (to a lesser extent) vocalizations, but they don’t have anything approaching the sophistication of human language. And they don’t do much in the way of teaching. Why not? Again, we already have part of the answer. Given that the capacity for language and teaching through language are part and parcel of the full package, and given also that the components of any organism’s adaptive suite are highly correlated—the result of cascading, multidirectional selection pressures—then it must be that humans teach (and chimpanzees don’t) because teaching in humans has become an essential part of our species-unique solution to the problem of existence on Earth, and, as such, teaching must be both enabled by, and contribute to, the other components of the solution in ways that are not present in other primates. To understand how teaching has evolved in humans, and the existential role it has come to play in our lives, we need to understand how it fits into this larger picture.

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Problems and Solutions Let’s begin with the observation that all organisms are faced with at least three major problems: how to feed themselves, how to avoid being eaten, and how to promote the survival of their own genes. As we’ve seen, chimpanzees solve these problems by feasting on fruit (which, conveniently, wants to be eaten) and tender green leaves, a diet supplemented by nuts, insects, and, occasionally, small animals; by operating in small bands near the safety of trees; and by practicing a kind of group sex, in which males mate with as many willing females as possible, in competition with other males. These strategies, in combination, have worked well for millions of years. Humans, in contrast, have evolved a riskier, more complex, and thus more cognitively challenging set of solutions to the same fundamental problems. Instead of relying on easily accessed food sources in a relatively safe and circumscribed environment, human ancestors for some reason adopted (or were forced to adopt) a more complex set of foraging strategies, focused on extracting high-quality, difficult-to-acquire food resources across an expanded and considerably more dangerous foraging territory. To ward off predators, our ancestors foraged in cooperative groups and eventually developed an array of sophisticated tools and weapons. And in place of group sex, they settled on the rare and more cognitively challenging strategy of monogamous pair bonding, whereby male and female couples come to form relatively permanent relationships with each other and begin to assume, with other members of their extended families, shared responsibility for the care and provisioning of their offspring. In combination, these strategies led to, and came to depend on, sophisticated social arrangements, including a new division of labor (adult males hunting and foraging within an expanded range, mothers, grandmothers, and aunts attending to juveniles and gathering provisions more locally), and a complex repertoire of foraging knowledge and skill, which had to be passed on from experts to novices in each generation. Simply put, it seems that language, and teaching through language, evolved as solutions to the dual problem of social coordination and

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transmission of cultural knowledge and skill. But as I explain in the following sections, the full story is not quite so simple.

Bipedalism as a Catalyst? Of all the traits that distinguish humans from the other remaining great apes, it seems likely that terrestrial bipedalism—which may have originated as early as 7 million years ago, in Sahelanthropus, a possible common ancestor of both chimpanzees and humans—came first and, in a loose, highly complex chain reaction, led to corresponding changes in both bodies and brains.4 As with many other human traits (indeed, all but the cultural ones), bipedalism in our ancestors would have had precursors in their ancestors. Among the remaining great apes, orangutans practice arboreal bipedalism, walking on slender branches with two feet while holding onto upper branches with their arms for support (Thorpe et al. 2007). Gorillas, chimpanzees, and bonobos are all capable of walking on their hind legs for short stretches, and so it is reasonable to think that our last common ancestor with chimpanzees did so as well. However, beginning at some point around the time of the divergence from the chimpanzee lineage, and possibly before, our hominin ancestors, either gradually or (more likely) in a series of fits and starts, grew committed to an upright posture and bipedal gait, which eventually left hands and arms free to perform other functions, such as reaching and grasping, throwing, swimming, carrying young, carrying food and other supplies, fighting off predators, handling weapons and other tools, and gesturing communicatively with arms, hands, and fingers. The other great apes can also do these things—just not nearly as well. The path to full bipedalism—and ultimately, the ability, among an elite few of us, to run 100 meters in under 10 seconds, 26 miles in about two hours, swim 100 meters in under 48 seconds, and dive to a depth of 100 meters in a little over 4 minutes—would take millions of years to evolve.5 According to a recent review of the fossil evidence, our hominin ancestors passed through two major transitions along the way (HarcourtSmith 2015; see Fig. 4.2).

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Fig. 4.2 Ancestral habitats, cranial capacity, and degrees of hominin terrestrial bipedalism. The horizontal axis represents (a) the expanding hominin habitat over time, from the fruit-bearing trees of the Miocene African rainforest (the domain of our last common ancestor with chimpanzees), into the woodlands, onto the savannah, then out into the rest of the world, and (b) the estimated timing of the transitions from occasional to habitual to obligatory bipedalism. The vertical axis (the shaded graph) represents the growth in hominin brain size, from under well under 500 c.c. in early ancestors—including the last common ancestor of humans and chimps (the “LCA,” hiding in the forest)—to around 1300 c.c. or larger in Denisovans, Neanderthals, and modern humans (Neanderthal brain capacity was slightly larger than the average for modern humans; although no Denisovans skulls have been found to date, their close genetic relationship to Neanderthals suggests cranial capacity was in the same range). Note the step increases in cranial capacity associated with the transition from occasional to habitual bipedalism in the Australopithecus species, and from habitual to obligatory bipedalism in H. habilis

First, around 4.5 to 3 million years ago, several hominins, including Ardipithecus ramidus, began to show traits associated with habitual bipedalism, suggesting they had adopted a regular bipedal gait, but had not yet evolved the specialized skeletal structure and anatomy— including longer legs, shorter toes, large semicircular canals in the ears (which help with balance while running)—that would make their descendants, millions of years later, efficient distance runners. Then, during the period 2.5 to 1.8 million years ago, around the time of the

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emergence of the Homo habilis, our ancestors made the transition to full, obligatory (“obligate”) bipedalism. Despite more than a hundred years of scientific theorizing, exactly how and why the momentous transition from occasional to obligatory bipedalism got started remains a great mystery. Darwin’s explanation was that bipedalism freed the hands to become specialized for the use of tools. Indeed, the earliest evidence for the use of manufactured stone tools currently dates to some 3.4 million years (McPherron et al. 2010), which is within the estimated window of the transition to habitual bipedalism. However, if the use of tools was the initial driver, we must ask why it was that chimps, who also use tools, and who walk on their hind legs when carrying them, did not also eventually become bipedal themselves (More about this below). Another set of explanations—connected with the now partiallydiscredited “Savannah Theory”—attributed the rise of bipedalism to the environmental conditions on the African savannah. An upright posture, it was argued, emerged because it made seeing over tall grass easier and at the same time provided better thermal control through reduced exposure of the body to sunlight—compared to the exposure for an animal on all fours—especially around noon (Wheeler 1988). However, while this is true, and may indeed partially account for our ancestors’ eventual success as endurance hunters (capable of running down antelopes and other fleet quadrupeds to the point of heat exhaustion), it is now understood that bipedalism first arose not on the open savannah, but in wooded areas, on the edge of the receding jungles. By the time of the australopithecines, when the foraging area had expanded out onto the savannah, hominins were already firmly bipedal. Yet another explanation, which remains especially controversial, is that bipedalism arose as a solution to the problem of wading into deeper and deeper water in search of shellfish—what was once called the “Aquatic Ape Hypothesis” (Hardy 1960) and more recently the “Waterside Ape Theory” (Evans 2019). In its strongest version, its advocates propose that our hominin ancestors passed through an early, semi-aquatic phase, during which they acquired what would become several uniquely human traits, including—in addition to bipedalism—hairlessness, a layer of subcutaneous fat, enhanced breath control, and a downward-pointing

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nose—all of which might be considered adaptations to a semi-aquatic lifestyle (Vaneechoutte et al. 2011). It is also proposed that a diet of fish and shellfish would have provided the nutrition (including fatty acids) necessary to feed a larger brain, which in turn enabled language. A weaker, less controversial version of the waterside hypothesis notes that hominins have always lived close to water, that humans are indeed by far the most aquatic of apes, and that our unique anatomical and physiological capacities for swimming and diving likely reflect, to some large degree, the habitats and foraging behaviors of our distant ancestors. While we can be confident that the emergence of bipedalism—and subsequently, swimming and diving—had something to do with selection pressures imposed by a changing habitat and our ancestors’ unique genetic and behavioral responses to these pressures, it is difficult, and arguably wrong, to look for a single explanation. Exactly which problem bipedalism first solved (if indeed there was just one), and how exactly it first arose, seems less important than the multiple advantages it offered once it began to emerge, which would have opened a path to further adaptations. It is also important to acknowledge that bipedalism is not a simple trait which could develop all at once, in a single habitat: not just the woodlands, the savannah, or by the side of rivers and lakes. Rather, as suggested in Fig. 4.2, bipedalism is enabled by a complex set of skeletal, muscular, and nervous system adaptations that must have evolved over millions of years, as our hominin ancestors migrated through an increasingly varied range of habitats: from dense forest, through woodlands, onto the savannah, alongside lakes and rivers, into coastal areas, and eventually (once our unique adaptive suite was firmly in place) out of Africa into the rest of the world. Further, as a process of niche construction, the relationship between forms of location and habitat must have been coevolutionary. An increasingly efficient bipedal gait, along with other traits, including the ability to swim and dive, gave our ancestors access to an expanding foraging territory, which further shaped our brains and bodies. An increasingly bipedal ape could wade deeper, see farther, walk farther in open terrain (more efficiently, if not faster), and use its newly-freed forelegs and paws (now arms and hands) for holding and carrying objects, swimming, throwing stones, gesturing, and, as I am doing now, typing on a keyboard.

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Again, Why Only Us? But still we must ask, however it evolved, if hands-free walking and running is such an advantage, why didn’t our chimpanzee cousins also become fully bipedal? Why not gorillas, orangutans, baboons? Why was it only our ancestors who eventually committed to the upright, bipedal gait that eventually took us around the world, and to the moon and back? Clearly this is just one of many such mysteries in our story. We can just as well ask, and should ask, why other primates didn’t come to use complex tools (say a termite fishing stick with a thin probe hafted onto a stout poker), why other primates don’t actively help each other learn and, arguably the biggest question of all, why didn’t other primates develop such an obviously useful thing as language, when they all had a chance? But that’s just the thing. It seems our distant primate relatives didn’t have a chance. The very particular and unlikely confluence of genetic and environmental factors that first set our ancestors trudging awkwardly out into the open woodlands and savanna, away from the safety of the forest—and eventually into coastal areas and a new and tremendously successful, though still precarious way of being in the world—simply did not come together for other primates. In fact, we can see that the deck is stacked against any major change in an animal’s way of being because, having specialized in one particular way of getting along in the world, it is naturally and inherently unsuited to most others, a proverbial fish out of water. For such a radical change as our transition to obligatory bipedalism to be adaptive, other changes must also have fallen into place, in just the right way. Indeed, it now seems clear that bipedalism worked and was eventually sustainable for our ancestors because, and only because, it was accompanied by changes not only in the method of locomotion, but throughout our full biological package, including all the basics— methods of acquiring food, staving off predation, selecting mates, and raising young—each of which required other major changes in the genome. And because these changes were so profound, they could not have occurred in an African afternoon. Some, such as a reworking of the mechanics of forepaws into hands, would almost certainly have begun

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to develop along with the adoption of habitual bipedalism; others, such as the restructuring of basic social arrangements (notably the transition from polygyny to monogamous pair bonding and cooperative breeding) and life history (notably an extended juvenile period and lifespan), almost certainly took millions of years to fully emerge in their modern versions. As we’ll see, scientists are still learning about, and debating, the exact sequence, timing, and nature of the evolutionary steps whereby a single and otherwise ordinary species of small-brained African primate somehow became capable, some 6 million years later, of building and flying commercial aircraft. However, thanks to contributions from several different disciplines, a plausible account has begun to emerge.

Foraging, Territory and Diet We can begin by recalling that major evolutionary changes almost always occur under pressure from major environmental changes, sometimes in the form of competition with other species, but often in the form of climate change. Animals don’t just choose to pursue a new lifestyle— something forces them to. Days get colder or warmer, ocean levels rise or fall, more rain falls, or less, lakes expand or dry up, food sources get consumed or contaminated—all with major consequences for local organisms and the organisms that feed on them. It seems this was the case in East Africa during the Pliocene, 5.3 to 2.6 million years ago, when a period of global cooling and drying, which had begun some 5 million years before that, led to a reduction in heavily forested areas and the consequent spread of woodlands and savannas—large open grassy areas with patches of small trees, lakes, and rivers (Herbert et al. 2016). As a result, traditional primate food sources, notably clumps of fruit-bearing trees in equatorial tropical forests, became fewer and farther apart. Given that a bipedal gait, while slower (try running for a short distance with a dog), is eventually more energy-efficient in open terrain (Sockol et al. 2007), this situation would have favored individuals with even slightly better mechanics for walking upright from one patch of trees to another, and, more generally, from one food source to another.

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Although the diet of early hominins is debated, it is clear that food sources around the edges of the forests, and stretching out into the newer grassy and waterside areas, would have become more various and richer in nutrients, but, on average, harder to acquire (Sayers and Lovejoy 2014). Patches of fruit- and nut-bearing trees would have still been available, though in limited quantity. But out in the grassy areas, with plenty of exposure to sunlight, small plants with edible roots and storage organs could be found and dug up. Lakes and streambeds would have offered shellfish. Beehives would have offered honey, though not without a fight. Other, potentially richer opportunities came in the form of large grassfeeding mammals such as antelope and deer. The adults of these species (though perhaps not their infants) would have easily escaped newly and awkwardly bipedal primates. However, more accomplished and betterequipped predators, such as members of the cat family, would have left occasionally unattended carcasses. These could be scavenged, though at considerable risk—both from the predators themselves (who could return unexpectedly to the kill), and from other hunter-scavengers. There was also the problem of tearing raw flesh from the carcasses, and breaking bones and skulls to get at the marrow and brains, which, even uncooked, would have provided an especially rich source of nutrients. Since getting at the contents of femurs and skulls is not too much different from the problem of extracting meat from nuts, which modern chimpanzees do with rocks, it seems likely that human ancestors were already using rock hammers to extract marrow and brains from scavenged carcasses at least 3.4 million years ago, when we find the first physical evidence for the use of sharp rocks for cutting flesh from bone (McPherron et al. 2010).

Habitat, Bipedalism, and Pair Bonding This may be a good place to discuss the likely relationship between changes in habitat, bipedalism, and the emergence of pair bonding and cooperative breeding in human ancestors. First, recall from Chapter 3 that at some point in our evolution a tendency toward monogamous pair bonding—along with bipedalism, hidden ovulation, familyoriented provisioning, dependence on a high-quality, omnivorous diet,

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an extended juvenile period, and an extra-large brain—became distinctive components of the human adaptive suite. An important question is how and when human ancestors managed to make the transition to relative monogamy. Several competing accounts have been offered. One has to do with the number of females in a group, their geographical distribution, and the local habitat. Looking across animals in general, it seems pair bonding is preferred for certain species when a male’s access to females is limited (as when females live relatively far apart), where food is relatively difficult to obtain, and where offspring are especially vulnerable to predation. Under these circumstances it may make more sense for a male to settle down with a single available female and concentrate on helping to provision and protect a relatively small number of offspring—instead of trying to spread his sperm far and wide in competition with other males. This may explain, for example, why prairie voles—which tend to live in tall-grass prairies where food availability is low, and hence females are more widely scattered and lack the safety of numbers—tend to pair-bond. And why meadow voles, which live in areas with more concentrated food sources and thus larger numbers of females, are promiscuous (see Young 2003). An alternative explanation, applied particularly to the emergence of pair bonding in human ancestors, considers the role of individual choices within a social hierarchy. In this account, in a social group where a few dominant males control access to a large number of females, nondominant males don’t have many females to choose from—the same problem facing male prairie voles, but for different reasons. Under these circumstances, it makes sense for a non-dominant male to try to form relatively permanent bonds with a single partner, if she will have him (Quinlan 2008). But will she? Recall the have-your-cake-and-eat-it-too strategy employed by female chimps, who mate freely with just about any male during periods of low fertility (thus enhancing their popularity and maintaining multiple affiliations), then become highly selective during the short, four-day window of maximum fertility. This tells us two things. First, even in a primate society where promiscuity is the norm, females can exercise choice, fending off all but the most attractive suitors when it really matters. But if the most attractive suitors are the strongest,

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most physically-fit males, what chance would a non-dominant male have of winning a special place in a single female’s heart? But, if a male’s perceived fitness comes to involve traits other than his ability to commit acts of physical violence, and if non-dominant males (by definition, the majority of males in a group) can exhibit such traits, then, under the right circumstances, perhaps pair bonding can begin to take hold—as it evidently did in our ancestors. Putting this all together, the move into an expanded foraging territory, away from the safety of the jungle, might well have favored the emergence (or strengthening) of a tendency toward monogamous pair bonding. The precursors were already in place. Like all primates, our hominin ancestors already had the necessary brain chemistry and circuitry—consisting of oxytocin and vasopressin receptors—the neural basis for pair bonding and romantic love. A standard primate social structure, dominated by a few physically dominant males, would have left a majority of males ready to concentrate their attention on whatever attractive female would have them. And females, like the females of any species, were capable of applying measures of fitness to their choice of mates. It seems the tipping point might have come when females in these groups began favoring brains over brawn! Which, it seems, is exactly what survival in the new habitat required.

Selection Pressure for Enhanced Cognition As we’ve already discussed, a wide variety of high-value food resources would have been there for the taking in the woodlands, waterside areas and open savannah, but with new risks and costs. True, the diet of an omnivore is safer because, if one type of food grows scarce, there’s always a backup, even if lower in quality. But an omnivore diet based on multiple food sources in multiple locations is also costly in terms of energy investments. Part of this is just the energy cost of travel from one location to another, but such a diet is also more demanding cognitively, requiring a lot more energy-consuming brainpower than it takes to sit under a tree and eat green leaves or fallen fruit. Among other things, an animal that depends on widely scattered and varied sources of food needs

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to learn and remember where the food sources are, when (in the case of fruits and nuts) they become palatable, different methods of extraction (digging, trapping, spearing), and so forth. Another cognitive demand would have stemmed directly from the need to maintain social relationships with other members of the foraging group. As in the case of modern chimpanzees and baboons, the optimum size of foraging groups would have grown with the risk of predation, especially from leopards and other large hunting cats, which would have been especially interested in a single two-footed primate out in the open. Individuals in a cooperative foraging group would have felt safer and would have indeed been a bit safer if they had truly cooperated, which would have required a certain level of trust, familiarity, and communication, all of which are costly in terms of time and cognitive energy. Any foraging animal, of course, needs a sense of where it is at any given time, where the others are, and how to get back to where it came from. It also needs to record and recall other important locations, and the most efficient routes from one location to another (a version of the traveling salesman problem). In the woodlands and open grasslands of the African savannah, some of these locations—such as clumps of distant trees with ripening fruit, rivers, watering holes—constitute relatively permanent landmarks, while others, such as the location of a fresh carcass or marauding panther, are temporary, but can be conceptualized (and described, if you have language), with respect to known landmarks. The larger the foraging range, the more landmarks and other locations an animal may usefully identify, store in memory, and recall. In such an environment, individuals who are just a bit better at navigating will be more effective foragers, and will be more likely to arrive home safely bearing gifts of food: which translate into healthier offspring, and the promise of sexual favors and additional offspring. Further, a lifestyle based increasingly on foraging expeditions away from a home base also requires, and selects for, increasing cognitive capacity for planning ahead, and passing up immediate gratification for future rewards. Successful foragers need to keep track of where fruit is ripening, and plan visits to these locations accordingly. The location and state of decay of a large animal carcass must also be kept in mind. Also, as

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the foraging range increases, the types of different food sources become more various, placing a premium on the ability to quickly distinguish between, say palatable and unpalatable foods, or the tracks of predators and prey, to remember these distinctions, and, eventually, to be able to communicate these distinctions to others. This brings up the issue of recruitment. Here, the size of the foraging group again makes a difference. If the group is too small, the risk from predators increases, but if the group is too large, it comes inefficient, because there may not be enough food to go around at the sites it visits. So, for a given foraging range, with patchy food resources, and a danger of predation, there will be an optimum size for a single foraging group. In certain circumstances, however, such as when one group discovers a large new carcass, it will make sense to recruit others to this location for assistance with butchering and fighting off competing scavengers. And recruitment, as we saw in the cases of honey bees and tandemrunning ants, requires some form of communication (see Bickerton 1992; Bickerton and Szathmáry 2011; Számadó 2010). As a further complication, cooperative scavenging and hunting create the need for a new kind of prosociality (e.g., an instinctive willingness to engage in altruistic sharing) and, at some point, an agreement about how much to share—based on factors such as the needs of individual families (assuming there are such things), and the relative contributions of individual foragers to successful acquisition of the food resource. In other words, equitable sharing requires solutions to computational problems involving weighting and proportional reasoning. Finally, there are the cognitive demands of pair bonding, cooperative breeding, and life in a large group, all of which have been correlated with brain size (Dunbar and Shultz 2007; Isler and Van Schaik 2012). As we’ve already discussed, successful group living requires that individuals be able to recognize individuals, store and recall facts about their typical behaviors, modify their own behaviors accordingly, and devote some optimal amount of time to cultivating positive relationships with the other members (“social grooming”). The strong positive correlation between pair bonding and brain size in birds and other species suggests that the demands of a monogamous relationship are similar, but intensified.

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So, our ancestors had apparently chosen, whether willingly or unwillingly, to pursue a lifestyle that placed heavy new selection pressure on enhanced cognitive capacity—for navigation, planning, taxonomic identification of plants and animals, equitable sharing of food supplies, and the myriad demands of family-oriented social life—thus creating a need for larger, energy-hungry brains. Under these pressures (and given the correlation between brain size and cognitive capacity), brains should grow larger and larger to accommodate the needs of their users. But, as is ever the case in nature, there are limits. The energy requirements of modern human brains, which consume over 20% of our total energy requirements while constituting just 2% of body mass, are almost 10 times greater than the average for the body as a whole, and as noted above, more than 7 times what is expected for a mammal of our size (Herculano-Houzel 2009). As we’ll see, the story of how we managed to push so far beyond the limits on brain size accepted by other primates is the story of how we became human.

Fueling a Supersized Brain: A Missing Piece Nature seldom provides a free lunch. Of the many ways to support a large brain, all involve some sort of trade-off or natural limitation. A prolonged period of gestation can give the brain more time for prenatal growth, but the circumference of the birth canal places a hard limit on the size of an infant’s brain at birth, and, as the size of the brain approaches that limit, births become more difficult, riskier for both the mother and the baby. Brains can also be given more time to grow postnatally, but this lengthens the period of juvenile dependency, thus increasing the energy costs associated with parental investment, and (especially problematic for a species that depends on the contributions of individuals to group welfare), postponing the point at which offspring can begin pulling their own weight. Another way of making a large brain affordable is to reduce the energy requirements of other parts of the body (such as muscles or the digestive system), making more energy available for brain tissue (Aiello and Wheeler 1995). But this is a risky strategy. You can afford

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to be slower and physically weaker than your competitors, but only if you can continue to outsmart them. You can simplify your digestive system by eating high-quality, easily digestible foods, but this is also risky because now you have to eat those kinds of foods. You can develop more energy-efficient methods of foraging and extraction of nutrients, including methods involving combinations of cultural knowledge, skill, and technology, but as a result you become dependent on these strategies, and will need to somehow pass them along to your children if they are to survive without your help. Finally, in a social animal that has a way of sharing its knowledge with others, it becomes possible to distribute cognitive capacity across individual members of the group, in effect creating a “super-brain” (Hoffecker 2013) capable of collective problem-solving, and social transmission of new knowledge and skill both within the group, and from one generation to the next. But, again, having started down this road, the continuing welfare of the group becomes and remains very much dependent on the continuing cooperation of individuals, and the ability and willingness of the group’s experts to share crucial knowledge, and to help novices acquire the technical and social skills necessary to the group’s continuing welfare and success—in other words, if possible, to teach. As it turns out, over some unknown period, evolution seems to have solved our hominin ancestors’ oversized brain problem using not just a single solution but a system of interlocking solutions. Starting in the womb, and continuing in the first year or so of life, human brains grow more rapidly than those of other primates, ultimately, as noted above, becoming three times the size of the average primate brain. The energy costs are offset largely through a shift in the direction of a high-quality, nutrient-rich diet, in turn made possible by sophisticated, and cognitively demanding foraging strategies, and leading to a reduction in the length of the gut, thus freeing up more energy for the brain (Isler and Van Schaik 2012). It seems that the initial success of this new set of interlocking solutions was set to trigger a snowballing effect with ramifications throughout the adaptive suite. Better fed individuals lived longer, were more fertile, and

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gave birth to healthier offspring. As females lived beyond their reproductive period, they became available for helping to care for their own and others’ grandchildren (Hawkes et al. 2000), which along with other factors, meant that children didn’t need to grow up so fast, and had more time to learn how to use their large brains and acquire the group’s cultural norms and skills. Also, as group size increased, and as experts became older and wiser, the computational capacity and contents of the group’s collective super-brain grew accordingly, leading to increases in group foraging efficiency, and increasing the odds that any given group would include individuals with above-average ingenuity. But something is missing from this picture—language. Without language, it would not have been possible to engage in the kinds of cooperative, joint attentional activities that allowed hominin ancestors to afford their enlarging brains, thrive in the ancestral environment, spread across the face of the Earth, and fly to the moon. Without language, and teaching through language, it would not have been possible to pass along the cultural norms, knowledge, and skill that sustains our species from one generation to the next, and has made these and countless other risky ventures possible. In this account, language, and teaching through language, was the missing piece, a sort of linchpin without which the other components of the solution would not hold. Whether this is a likely outcome for any bipedal primate, it is clear, in retrospect, that at some point our own continuing survival as a species required a solution to the problem of intragroup communication, and that solution was language.

Teaching as a Biocultural Activity The question is when language, and teaching through language, first began to play a role in human evolution. Did it emerge in historical time—say just the last tens of thousands of years—or did it arise in evolutionary time, meaning hundreds of thousands if not millions of years? Another way of asking this question is whether teaching is primarily biological or cultural . Let’s acknowledge that this is an important question. If teaching is biological, a kind of instinct, then it must be that all of us, in every

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culture, are natural and necessary teachers, with all the opportunities and responsibilities this implies. But if teaching is primarily a cultural practice, like farming or cooking, then it could conceivably have developed quickly and recently, in historical time, say in just a few tens of thousands of years. And if teaching is largely cultural, then, in a way, we’re individually off the hook. If we can’t teach, don’t want to, or for some other reason don’t do a lot of it, that’s just the way we were raised, or the role society assigned to us. Someone else can do it. The answer, as you may suspect, must be that teaching is both instinctual (biological) and cultural. Teaching is fundamentally and inextricably rooted in our genes and our life experiences. It’s a biological imperative, cultural gift, and social responsibility. In fact, there’s a good word for this sort of thing—teaching in humans is biocultural . Further, teaching is not just a biocultural behavior: teaching is productive, making human culture, and the transmission of human culture from one generation to the next, possible. For this reason, archaeological evidence for the emergence of human culture may be taken as at least partial evidence for the emergence of teaching. And, as we’ll see in the next chapter, the available archaeological evidence suggests that teaching may indeed have a very long history in human evolution, possibly going back some three million years or longer. That’s nearly two hundred thousand generations of experts helping novices learn.

Food for Thought 1. How exactly can the coevolution of brains, language, teaching, and technology be considered an “arms race.” 2. What was the role of the natural world in the arms race? 3. Do you think it makes sense to claim the emerging hominin adaptive suite was “unstable” in the absence of language? Why or why not? 4. Why haven’t other social animals evolved human-like language? 5. What does it mean to say that teaching is a “biocultural” activity?

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Notes 1. The reference here is to a program designed by the MIT scientist Terry Winograd which was capable of responding to commands typed in English. See Winograd, T. (1971). Procedures as a representation for data in a computer program for understanding natural language (No. MAC -TR-84). Cambridge: Massachusetts Institute of technology. 2. Note that I’m treating the human capacity for language acquisition and teaching (natural pedagogy) as part of the bundle of brain-based cognitive capacities, and the use of individual languages and ways of teaching as cultural behaviors. 3. When I write that adaptive traits evolve “more or less slowly,” I’m alluding both to issues of timescale, and to the fact that some traits can evolve very quickly indeed. For example, it has been proposed that it would take less than 364,000 years for a functional vertebrate eye to evolve from a patch of photoreceptors. Nilsson, D. E., & Pelger, S. (1994). A pessimistic estimate of the time required for an eye to evolve. Proceedings of the Royal Society B (1345), 53–58. 4. Note this implies chimpanzee adaptations for an arboreal lifestyle (e.g., longer fingers, grasping toes) would have evolved after the split in our lineages. See Lovejoy (2009). 5. Despite the clear relationship between the two, the role of swimming in human evolution receives has received much less attention than that of bipedalism. Certainly it cannot be a coincidence that humans are the only bipedal apes, and also (by far) the strongest swimmers.

Suggested Reading Dawkins, R. (1986). The blind watchmaker: Why the evidence of evolution reveals a universe without design. New York: W. W. Norton. A good introduction to some important concepts in Darwinian evolution. In particular, see pages 252–285 for a highly readable introduction to the notion of an evolutionary “arms race.” Fletcher, G. J., Simpson, J. A., Campbell, L., & Overall, N. C. (2015). Pair-bonding, romantic love, and evolution: The curious case of Homo sapiens. Perspectives on Psychological Science, 10 (1), 20–36.

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Well worth reading. The title says it all. Lovejoy, C. O. (2009). Reexamining human origins in light of Ardipithecus ramidus. Science, 326 (5949), 74–74e8. The source of the figure on page 63. Contains similar material to White et al. (2015), included in the “Suggested Reading” section at the end of Chapter 3. Sinha, C. (2015). Language and other artifacts: Socio-cultural dynamics of niche construction. Frontiers in psychology, 6 , 1601. A challenging but rewarding discussion of the coevolutionary relationship between language, understood as a “biocultural niche” and the human mind.

References Aiello, L. C., & Wheeler, P. (1995). The expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution. Current Anthropology, 36 (2), 199–221. Bickerton, D. (1992). Language and species. Chicago: University of Chicago Press. Bickerton, D., & Szathmáry, E. (2011). Confrontational scavenging as a possible source for language and cooperation. BMC Evolutionary Biology, 11(1), 261. Dawkins, R. (1986). The blind watchmaker: Why the evidence of evolution reveals a universe without design. New York: W. W. Norton. Dunbar, R. I., & Shultz, S. (2007). Evolution in the social brain. Science, 317 (5843), 1344–1347. Evans, P. H. R. (2019). The waterside ape: An alternative account of human evolution. Boca Raton: CRC Press. Harcourt-Smith, W. E. (2015). Origin of bipedal locomotion. In W. Henke & I. Tattersall (Eds.), Handbook of paleoanthropology (pp. 1919–1959). Berlin: Springer Berlin Heidelberg. Hardy, A. (1960, March 17). Was man more aquatic in the past? New Scientist, 7 (174): 642–645. Hawkes, K., O’Connell, J. F., Blurton Jones, N. G., Alvarez, H., & Charnov, E. L. (2000). The grandmother hypothesis and human evolution. In L. Cronk, N. Chagnon, & W. Irons (Eds.), Adaptation and human behavior: An anthropological perspective (pp. 237–258). New York, NY: Aldine de Gruyter, Inc.

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Herbert, T. D., Lawrence, K. T., Tzanova, A., Peterson, L. C., Caballero-Gill, R., & Kelly, C. S. (2016). Late Miocene global cooling and the rise of modern ecosystems. Nature Geoscience, 9 (11), 843. Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaledup primate brain. Frontiers in Human Neuroscience, 3, 31. Hoffecker, J. F. (2013). The information animal and the super-brain. Journal of Archaeological Method and Theory, 20 (1), 18–41. Isler, K., & Van Schaik, C. P. (2012). How our ancestors broke through the gray ceiling: Comparative evidence for cooperative breeding in early homo. Current Anthropology, 53(S6), S453–S465. McPherron, S. P., Alemseged, Z., Marean, C. W., Wynn, J. G., Reed, D., Geraads, D., et al. (2010). Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia. Nature, 466 (7308), 857–860. Miller, L. A., & Surlykke, A. (2001). How some insects detect and avoid being eaten by bats: Tactics and countertactics of prey and predator. AIBS Bulletin, 51(7), 570–581. Quinlan, R. J. (2008). Human pair-bonds: Evolutionary functions, ecological variation, and adaptive development. Evolutionary Anthropology: Issues, News, and Reviews, 17 (5), 227–238. Sayers, K., & Lovejoy, C. O. (2014). Blood, bulbs, and bunodonts: On evolutionary ecology and the diets of Ardipithecus, Australopithecus, and early Homo. The Quarterly Review of Biology, 89 (4), 319–357. Sinha, C. (2015). Language and other artifacts: Socio-cultural dynamics of niche construction. Frontiers in Psychology, 6, 1601. Sockol, M. D., Raichlen, D. A., & Pontzer, H. (2007). Chimpanzee locomotor energetics and the origin of human bipedalism. Proceedings of the National Academy of Sciences, 104 (30), 12265–12269. Számadó, S. (2010). Pre-hunt communication provides context for the evolution of early human language. Biological Theory, 5 (4), 366–382. Thorpe, S. K., Holder, R. L., & Crompton, R. H. (2007). Origin of human bipedalism as an adaptation for locomotion on flexible branches. Science, 316 (5829), 1328–1331. Vaneechoutte, M., Kuliukas, A., & Verhaegen, M. (Eds.). (2011). Was man more aquatic in the past?. Fifty years after Alister Hardy-Waterside hypotheses of human evolution: Bentham Science Publishers. Wheeler, P. E. (1988). Stand tall and stay cool. New Scientist, 118(1613), 62– 65.

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Young, L. J. (2003). The neural basis of pair bonding in a monogamous species: A model for understanding the biological basis of human behavior. In K. W. Wachter & R. A. Bulatao (Eds.), Offspring: Human fertility behavior in biodemographic perspective. Washington, DC: National Academy Press.

5 The Coevolution of Language, Brains, and Technology

As illustrated in Fig. 5.1, estimates for the timing of the emergence of language in humans vary dramatically, from as recently as 100,000 years ago to more than 3 million years. I’ll soon get to the specifics, but what I want you to think about now is how very far apart these estimates are. Scientists working in a broad range of disciplines—linguists, biologists, anthropologists, paleontologists, and archaeologists among others—have pursued active interests in the question of human language origins from well before the time of Darwin, who proposed that language first arose in the form of song, associated with mating rituals (for a discussion of Darwin’s theory of language evolution, see Fitch 2010: 470–481). Since that time, thousands of books and academic papers have been devoted to the question. How can it be that now, well into the twenty-first century, so little agreement prevails concerning a matter so central to our understanding of ourselves and our history that scientific estimates can differ by a full order of magnitude? Imagine if there were as much disagreement, say, about the distance of the sun from Earth! There are at least two explanations for these discrepancies. One is a simple matter of terminology, having to do with how different scientists © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_5

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Fig. 5.1 Five estimates for the first emergence of human language

decide to define language, and what aspects of language, so defined, they focus on. If “language” means full-blown, modern language as we use it today (as you and I are using it now)—with massive symbolic reference, high-speed speech, complex recursive syntax (not to mention reading and writing)—then it makes sense to assume a relatively recent origin. It seems the brains of earlier hominins were just too small, and their technological accomplishments too meager, to suggest they had access to anything like the capacity for language you and I are demonstrating here. On the other hand, if we acknowledge that modern versions of language must have had precursors in earlier forms (protolanguages), then we’ll need to look back much farther in our history for evidence of a fledgling language. Note that these two ways of thinking about language are not inconsistent. Just as the modern city of Rome is built on and around the remnants of more ancient settlements, and just as our modern human brains are built on and around the brains of our distant primate ancestors, so the human language faculty could be both new and ancient. But this raises another problem. Human language may indeed have ancient roots (almost certainly it does) but the farther we go back in time, the thicker the fog. As is often pointed out, language, as an observable behavior, doesn’t fossilize. If we want to build a case for the emergence of language, or protolanguage, at any point in our distant past, we need to rely on scattered bits of indirect, circumstantial evidence. Assuming, for example, that our capacity for language must have evolved from more primitive systems in ancestral primates, we can study the communicative behaviors of contemporary primates, compare these to our own, and— knowing something about the pace of evolutionary change in other biological systems—think how much time (if only in orders of magnitude) it must have taken for evolution to have built a language-using

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human brain from an off-the-shelf, language-less primate brain. Also, assuming a relationship between brain size, cognitive capacity, language, and technology, we can study the cranial capacity of fossil hominins, examine the tools they left behind, and look for evidence of significant increments that might signal milestones in the emergence of language as a support for technological innovation and generational transfer. We can also look at the languages currently acquired and employed by indigenous hunter-gatherers in remote corners of the world (such as the interior of Australia or the Amazon rainforest) and think what these might tell us about the linguistic capacities of our immediate Stone Age ancestors. On the basis of these and other fragments of circumstantial evidence, we can try to piece together reasonably plausible scenarios for the timing of language evolution among human ancestors. Admittedly, whatever we come up with may not be testable theories in a rigorous scientific sense. We can’t use the evidence to make a prediction about the linguistic capacities of, say, H. erectus, jump in a time machine, and go back and check. Indeed, you might well question whether the methods scientists have been using to construct their hypotheses and estimates about the origins of language are sufficiently scientific, and you might wonder why I’ve devoted so much typing— which I’m now inviting you to read—to what is likely to remain, at least for the foreseeable future, a largely unsolved mystery. Here’s why. First, it seems to me that having some idea, even if only partly correct, of how language—and teaching through language—might fit into the larger picture of human evolution seems far better than having no idea at all. While the available evidence is still sparse and open to different interpretations, there’s a lot more to go on than there was in 1866 when the French Academy of Sciences famously banned further scholarly discussion of language evolution. To the extent we want to know where we came from, and how we became the most sophisticated teachers in the animal kingdom, it seems wrong not to grasp at the available straws, especially the larger, seemingly more buoyant ones. Second, trying to develop at least some sense of when and why language likely first emerged in our long history is important because it forces us to consider the circumstances under which language must have arisen, and the crucial relationship between these contexts and the

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nature of language, and teaching through language, from the very beginning. It’s one thing if language emerged, for example, in relatively recent times (as some have suggested)—say after our ancestors had learned to kill large animals with spears, to control fire, and to make adhesives by heating pitch mixed with ochre. If that were the case, language would be a kind of supplement to an existing set of tools—a special sort of “cognitive gadget” (Heyes 2018). It’s a very different thing if we imagine that our ancestors had some form of language more than 3 million years ago, or even nearer the time of our divergence from our language-less cousins, the chimpanzees, some 3–7 million years before that. If so, language— and, as I will argue, teaching through language—must be fundamental to who we are, and must have been so from far back in our murky history. In the following pages, I’ll make a case for an early origin of language, in the form of a protolanguage, at least some 1.8 million years ago, in H. erectus, and quite possibly before that. Among other things, I claim that the astounding complexity of human language as a biological system argues for an ancient origin. Just as Rome was obviously not built in a day, so it seems clear that the complex physical anatomy (lungs, larynx, cartilage, bones, teeth, tongue, ears) and even more complex neural circuitry that enables modern human language—and the consequent ability to produce packages of sound that carry symbolic meaning, thereby altering the contents of another person’s brain, thereby making it possible to teach—could not have been the result of a single “macromutation,” but must instead have been millions of years in the making. And yet there is indeed evidence that something special happened in our ancestors’ brains within perhaps just the last 100,000 years, and that this development gave rise to language as we use it today. My goal for us is to reconcile these two ways of interpreting the available evidence.

What Do We Mean by “Language?” Before we begin, it will be necessary to be as precise as possible about the term “language.” To make things simple, let’s just agree that we’re talking about modern human language, that is, the unique system of communication used, in thousands of mutually unintelligible varieties

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(how’s your Pirahã these days?), by all living humans. As with all animal communication, this system consists of a set of arbitrary, conventionalized signals, which senders produce with the apparent purpose of affecting the behavior and mental state of signal receivers—almost always (except, for example, in cases of predator-prey deception) members of the same species. In modern human language, these signals consist of a large but finite set of meaningful sounds and gestures (in human sign language, only gestures, including facial gestures), which, when combined according to a complex set of grammatical rules—rules far more complex than the ones your high school English teacher may have tried to teach you—can be used to convey an infinite number of possible meanings, including any human belief, intention, or desire. To be clear, by modern language we shouldn’t be thinking of particular modern languages like French and Italian (as opposed to “ancient” Latin) which emerged just a few centuries ago, nor should we be thinking about any particular language spoken in the world today. Rather, we’re talking about the brain-based capacity that all living humans use to learn and use any human language. We’re talking about a highly evolved, almost incredibly intricate system of neural circuitry and physical anatomy which allows us to rapidly produce and interpret the strings of rule-based sounds that carry the meanings, at the rate of some 10–15 phonemes per second. In a very general sense, the system is used for “information transfer” (Pinker 2003), but this is only one of its many functions. Language can be used to make assertions, to ask questions and answer them, to direct attention, to instruct and explain, to make requests, suggestions, promises, and threats, to give thanks, to give praise, to ask for forgiveness, and forgive. As such, human language is a system not just for, nor even primarily for, “exchanging information.” It is above all a means of getting things done with others in our chosen habitats, consistent and fully aligned with the needs of a social animal whose individual welfare depends on group cooperation and the transmission of cognitive capital—cultural knowledge and skill—from one generation to the next. Let’s also agree that human language in its modern form, rooted as it is in highly complex, intricately connected and nested systems of neural circuitry and physical anatomy, could not possibly have blossomed into

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being all at once, in one giant leap, from nothing. Language, as we now know and use it, must have had an evolutionary history, probably a very long one, and earlier forms must have been simpler, with consequently simpler neural and anatomical systems for production and interpretation of signals. In short, when we speculate about when human language arose, we must not limit our thinking to what we know of human language in its modern form. Rather, we need to consider the existence of earlier, archaic versions of human language—protolanguages (Bickerton 1992)—dating back in time at least to some point after the split between human and chimpanzee lineages some 6–10 million years ago.1 The roots of language may not run that deep (I mean to convince you that they do), but that’s the timescale we have to work with.

The Relationship Between Language and Technology So, when did this unique signaling system first begin to emerge in our ancestors? An important source of circumstantial evidence about language origins involves the relationship between language and the use of tools. The existence of various forms of technology, from very simple to highly complex, from sharpened sticks to spacecraft, has much to tell us about the existence and use of language, including the use of language for teaching. However, as we review this evidence, we need to keep in mind that material culture, especially the subset of material culture preserved in the archaeological record, is only one source of information about the likely origins of language and teaching through language, and thus can’t tell the full story. For one thing, tools and other cultural artifacts made of organic matter (sharpened sticks, fiber lashing, nets, deadfall traps, prepared animal skins, wooden rafts, and boats) do not preserve well over the millions of years we consider here. Stone tools, which do preserve, are therefore a highly biased sample. Moreover, as we’ve just discussed, language has other uses beyond teaching others how to make and use tools. While complex tools imply complex language and pedagogy, the apparent absence of complex tools, especially physical tools, need not

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imply the absence of some form of language. In fact, language itself is a tool, as we can see, for example, in the large vocabularies that modern hunter-gatherers use to identify and describe such things as the flora and fauna in their territories, important locations in their habitats, both real and imaginary,2 and intricate kinship relationships. As noted, language is also a tool for getting things done with others, for directing, requesting, demanding, and agreeing. In short, the apparent absence of physical tools in the archaeological record, or the persistence of certain kinds of tools— as we’ll see, our ancestors used similar tools for millions of years—is an imperfect indicator of the presence or absence of language. That said, the presence of complex technology does indeed imply the presence of complex language. As we’ve seen in the case of chimpanzee termite fishing, the use of simple tools can be acquired through simple observation and emulation, without the need for language, and without intentional instruction. But highly complex technology implies complex language, and multiple generations of teachers and innovators. If you’re not convinced, then imagine, as a thought experiment, that an alien space probe enters our atmosphere, a parachute opens, and the craft drifts gently to Earth, emitting strange sounds. Greetings? Threats? We wouldn’t know, but we could be certain that the intelligent beings who built and launched the probe must have had their own complex form of communication, and had used it to teach, and work together in teams, over many, many generations. Looking back at our own recent history, we know that the lunar landings of the late 1960s and early 1970s would not have been possible had it not been for the collaborative work of hundreds of thousands of scientists, engineers, and technicians. We also know that their vast collective knowledge and expertise had been built on the work of many previous generations of thinkers and doers, and that all of this work (a form of distributed cognition in space and time), and the transmission of complex knowledge and skill it implies, was enabled and conducted largely through the medium of complex modern language, both written and spoken.

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Evolution of Stone Tool Manufacture So, how did this all begin? We can start by recalling again that chimpanzees frequently use rocks to crack open nuts, using a hammer and anvil technique. At some point, early hominins must have noticed, as chimps apparently do not, that when you strike certain kinds of rock (such as quartz, flint, obsidian, or basalt) at certain angles, with a certain force, the rock fractures, creating a sharp flake, and leaving a scar, also sharp near the edge. The flake itself can be useful as a scraping or cutting tool. And by repeatedly rotating the rock (called a core or cobble) and striking it with another rock or stick in just the right way (a process called knapping ), it is possible to put a sharp edge on the core, creating a simple chopper. With additional work, you can make a flat, double-edged (and symmetrical) cutting tool. If you make it small and flat enough, and form a notch at the bottom, you can bind it to a stick with twisted plant fiber, using heated pitch and ochre as a kind of glue (Wadley 2005). Now you have a stone-tipped spear, which, if you can get close enough, you can use to seriously injure a large animal. And if you can figure out how to throw the spear with another notched stick, you can launch it at speeds well over 40 meters per second (90 m.p.h.; Hutchings and Brüchert 1997). What is interesting is that it took our ancestors at least 3 million years to figure all this out—from simple sharpened stones to high-velocity spear throwers. More accurately, that’s how long it took their brains to evolve to the point that they became capable of learning how to methodically haft sharpened stones to sticks, and, once learned, capable of teaching others how to do it. Our ancestors apparently started intentionally fracturing rocks at least 3.3 million years ago, but did not develop stone-tipped spears until around one million (Kelly 2005) to 500,000 years ago (Thieme 1999).3 Spear throwers don’t start to show up until around 30,000 years ago (Wilkins et al. 2012), almost 3 million years after the first appearance of sharpened rocks. Why did it take so long? And why, by the way, didn’t chimpanzees acquire the same technology? It seems the answer to both questions is the same. It took that long, and chimps never learned how to use stone-tipped spears to fling at monkeys, because it takes a very special kind of intelligence to make a

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spearhead out of stone, attach it to a stick, and learn how to throw it with another stick—and a brain capable of exercising that kind of intelligence needs a certain kind of body, and a certain set of behaviors, and a certain social structure, and a certain life history, and such a creature does not get like that inevitably, but only as the result of a highly unlikely sequence of genetic accidents and environmental challenges and opportunities. To get a sense of the likely timing of the evolving relationship between language and technology (and bearing in mind that stone tools are a biased sample of a culture’s technology), let’s now review what is known about the history of stone tool manufacture in hominins, then consider what this might tell us about the evolution of language, and the use of language for teaching. As we’ll see, the relationship between technology, teaching, and language is only part of the story, but it’s an important part.

Tool Use in Ancient Hominins A reasonable starting point is Ardipithecus ramidus, the bipedal ape that first appears in the fossil record some 4–5 million years ago, at least a million years, and as many as 5 million, after the hominin and chimpanzee lineages apparently diverged. While we have no direct evidence that Ardipithecus made tools, it can’t be ruled out. Quite likely, like modern chimpanzees, these creatures sharpened sticks for stabbing small animals in their burrows and used rocks as hammers and anvils to crack open nuts. Further, recent studies suggest that the last common ancestor of humans and chimpanzees had relative short fingers, and that the longer fingers of chimpanzees, which make tool use more difficult, evolved after the split (Skinner et al. 2015). So it may have been that our last common ancestor was even better with tools than chimps have come to be. As discussed below, it seems that tool use and human anatomy indeed coevolved, but the changes occurred less in the hands themselves than in those areas of the brain that enable fine motor control over the hands and fingers, one of which, Broca’s area, is also implicated in the control of speech and the phonological loop (Aboitiz et al. 2010), a matter we will consider in the next chapter.

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At this writing, the earliest direct evidence of intentional stone tool manufacture dates from about 3.3 million years ago, based on artifacts unearthed at an archaeological site in West Turkana, Kenya, known as Lomekwi 3 (Harmand et al. 2015). The artifacts include an assemblage of stone flakes, worked stone cores, and stones that appear to have been used as anvils and hammers. Taken together, the tools demonstrate what the archaeologists who found them judged to be a “developing understanding” of how to exploit the fracture properties of certain kinds of rock. The assemblage was found in the geographical and chronological vicinity of fossil remains of Kenyanthropus platyops, a species that is either related to, or an early example of, Australopithecus. Animal bones with cut marks, suggesting that stone tools had been used to cut away flesh, have been dated to more than 3.4 million years ago, some 100,000 years earlier (McPherron et al. 2010). Given that these recent findings pushed back the date for the earliest known stone tool manufacture by nearly a million years, and given also the unlikelihood that Lomekwi 3 just happened to be the actual site where early hominins first began knapping stone, it seems a safe bet that future discoveries will push back estimates even farther.

The Oldowan Tool Industry Prior to the discovery at West Turkana, the earliest evidence of stone tool manufacture had been dated to 2.5 million years ago, based on artifacts found at Olduvai Gorge, Tanzania. These are considered the first examples of the so-called Oldowan tool industry, which spread throughout much of eastern Africa, then into the Middle East, Europe, Central Asia, and northern China. The industry is associated with late Australopithecus and early species of Homo such as H. habilis, H. ergaster, and early H. erectus, suggesting a line of cultural transmission. Oldowan tools encompass a variety of types, including both knapped cobbles and sharp flakes, and are generally smaller, but not profoundly different from the earlier Lomekwian artifacts. A feature which distinguishes them from the later “Acheulean” technology (below) is that these earlier tools tend to show evidence of knapping only along a single edge,

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with no apparent concern for symmetry or consistent design. Nevertheless, it is now generally agreed that the Oldowan toolmakers were already skillful stone knappers, capable of systematically removing scores of sharp flakes from a single core by striking it with a hammerstone at careful angles, suggesting that the skill was learned from experts, and required considerable practice, if not explicit instruction (Morgan et al. 2015). Significantly, the Oldowan industry remained the dominant method of stone tool manufacture among human ancestors for some 700,000 years.

The Acheulean Tool Industry Then, about a million years later, around 1.7 million years ago, we find the first evidence of the Acheulean tool industry, evidently adopted first by H. erectus, and later by early H. heidelbergensis. Unlike the Oldowan choppers, which were typically lopsided, and knapped along only one side, Acheulean tools were bifacial (knapped on two sides), symmetrical, and became increasingly thinner. Softer hammers made of wood, bone, or antler were apparently used to fine-tune the edges, and grooves were cut in the striking platform to help hold the core in place during the final sharpening stage. The Acheulean industry remained largely unchanged for more than a million years.

The “Human Revolution”4 Finally, beginning at some point around 300,000 years ago, we see the gradual appearance of fossil remains in Africa with traits similar to those of modern humans, including cranial capacity nearing, and in some cases exceeding our own. Over the next 200,000 years, the archaeological record reveals the gradual accumulation and intensification of so-called modern human behaviors throughout Africa, including bone tools, increased geographic range, specialized hunting, use of coastal resources, long-distance trade, systematic processing and use of pigment, and art and decoration (McBrearty and Brooks 2000).5 Then, intriguingly, beginning some 60–80 thousand years ago, the DNA evidence points to a narrow demographic bottleneck in which a

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population of human ancestors fell to just a few thousand individuals, originally centered in a small area of southern or eastern Africa. Importantly, the same region was experiencing sudden shifts in annual rainfall during this period, which would have been especially challenging for local residents. Starting at around this time the archeological evidence shows a marked increase in the complexity of technical and other cultural artifacts, including new techniques for working animal skins (e.g., scrapers and awls); more sophisticated weapons such as throwing spears; and ornaments made of shells (Mellars 2006). The human populations responsible for these behaviors soon spread out into Europe and Asia, reaching Australia by boat or raft some 65,000 years ago (Clarkson et al. 2017), along the way replacing (either merging with or killing off ) existing inhabitants, including H. erectus, Neanderthals, and Denisovans.

The Mousterian Tool Industry By around 500,000 years ago, coinciding with the emergence of H. heidelbergensis—considered to be the last common ancestor of Neanderthals and modern humans—the Acheulean industry had evolved into what is called the Mousterian tool industry. This industry is characterized by a greater variety of smaller cutting tools using the so-called Levallois technique. Named after the Levallois-Perret suburb of Paris where examples were first found in the nineteenth century, the technique involves a two-stage production process involving an initial knapping of a suitably-shaped blank, which is then refined into the final product. The Mousterian is associated mainly with the Neanderthals, who used roughly the same method of tool manufacture, carried over from their African ancestors H. heidelbergensis, for some 400,000 years—yet another example of the persistence of useful technologies.

When Did Language Emerge? So, where does the evolution of human language and teaching through language fit into this history? Broadly speaking, and as noted at the beginning of the chapter, researchers have been drawn to at least five

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quite different estimates for the first emergence of language—from as recently as 100,000 years ago (some 200,000 years after the first appearance of “anatomically modern” humans in Africa), to more than 3 million years ago—at least back to the point at which we have the first direct evidence of intentional stone tool manufacture. Let’s take a look at each claim and the evidence supporting it. As I’ll explain, it seems that the earlier dates are more likely, but the timing depends very much on how we define “language” and to what extent we’re willing to credit indirect evidence for a language capacity in human ancestors. Also, as we’ll see, the evidence in support of each claim, early or late, can be assembled in support of a single reasonably plausible scenario and timeline.

Modern Onset (~100,000–300,000 Years Ago) We can identify at least two versions of the Modern Onset hypothesis. The first rests, rather precariously, on a narrow linguistic definition of language, and also reflects a staunch unwillingness to entertain hypotheses based on what its supporters view as “woefully incomplete or absent evidence” for an earlier date (Hauser et al. 2014) In this view, the only truly unique aspect of human language is the faculty of recursion, which allows the embedding of phrases within phrases, as in the center-embedded sentence “We’ll eat the goat the leopard killed yesterday tomorrow.” Recursion (also called “unbounded Merge”) is proposed to have arisen fewer than 100,000 years ago when, as the linguist Noam Chomsky has put it, “a rewiring of the brain took place in some individual, call him Prometheus, yielding the operation of unbounded Merge, applying to concepts with intricate (and little understood) properties” (Chomsky 2010). A less restrictive view, which I discuss below, assumes that various critical features of modern language—including recursion and other features of complex syntax, but also high-speed speech and massive symbolic representation—is in some way correlated with the appearance of a small population of modern humans, some 200,000 years ago in southern Africa, followed by a significant expansion and dispersal of these populations out of Africa and into the rest of the Old World beginning around 80,000 years ago (Mellars 2006).

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In any case, whether it was just 100,000 years ago, or a bit farther back, we have at least two reasons to think that full-blown modern language emerged relatively recently, and that it involved not so much a complete “rewiring” as the continuing (though jerky) evolution of existing, language-enabling brain circuitry. Here’s the argument. First, recall that recursion allows the deep embedding of phrases within phrases, constrained only by the limits of short-term memory. A single example should suffice. Compare the following: 1. We saw a leopard yesterday. That leopard killed a goat. We chased the leopard away. We ate the goat. I think that leopard is standing over there. 2. I think the leopard we chased away that killed the goat we ate yesterday is over there. Notice that both convey roughly the same information. The second is arguably more efficient, equally precise, but awkward, constructed mainly to make my point: which is that the language centers in my brain allow me to produce such a sentence, and yours, just as remarkably, allow you to process and interpret it, without causing either of us a great deal of difficulty. Our modern human brains, in other words, can evidently handle deeply embedded linguistic structures. Given that all modern human brains can do so, including those of the indigenous peoples of Australia and the Americas, whose ancestors left Africa some 80,000 years ago, we know that a capacity for recursion and other forms of embedding must have emerged in humans before that time.6 But recursion isn’t all that’s going on here. Note that the separate utterances and the single utterance that packages the individual utterances so neatly together both depend on the magic of symbolic reference—our brain’s unique ability to establish connections between certain sounds and the images or ideas that these sounds represent (notably, the idea of yesterday) in the minds of the interlocutors. Both also depend on syntax (e.g., recursion and the rule-based ordering of subjects, verbs, and objects), and both depend on the ability to produce and process high-speed speech sounds—or, in the case of the sentences you just read, written versions of those speech sounds. The difference is a matter of efficiency and processing capacity. By allowing symbolic references to

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be embedded inside other references in such an elegant way, the property of recursion lets a single utterance do the communicative work of multiple independent utterances, thereby significantly boosting the representational power and efficiency of language in real time. However, for this to work, the circuitry involved in processing a recursive utterance must be capable of maintaining interpretations of the embedded clauses briefly, in short-term memory, while the brain continues to analyze the whole. A likely candidate underlying this capacity is the so-called phonological loop I mentioned earlier—a specialized set of circuits in the human brain that have been implicated in the processing of recursive linguistic structures. Proposed as a “key innovation” in the evolution of the human brain, the phonological loop is apparently derived from similar structures in nonhuman primates that allow the animal to maintain a representation of an auditory signal (say an alarm cry) in short-term memory just long enough to rehearse it— that is, to play it back to itself. Significantly, the phonological loop physically overlaps, and may have coevolved with, a more ancient circuit involved in hand manipulation and interpretation of gestures (Aboitiz et al. 2010). I’ll have more to say about the phonological loop later in the chapter. The important thing to note here is that recursion and other forms of embedding is arguably best understood as the recent culmination of an evolving linguistic capacity—including symbolic representation, complex syntax, and high-speed speech—that must have already been present in the individual (more accurately, a population of individuals) that Chomsky refers to as “Prometheus.” Recursion does indeed provide an elegant means of forming utterances that might otherwise be packaged in more awkward, time-consuming forms, and is a near universal feature of human languages.7 But the capacity for recursion could not have been a solution to the general problem of a need for language, which must already have existed in some form when the rewiring Chomsky postulates took place. Rather, recursion must have evolved under selection pressure imposed by a need for more efficient linguistic structures, and must have been enabled by expanding brain resources, including expanded short-term linguistic memory, in a brain that had already been shaped by, and for, language use.

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This said, another reason to take the Modern Onset hypothesis seriously, or at least concede it a certain amount of truth, has to do with the archeological record. As noted above, following the first appearance of anatomically modern humans around 300,000 years ago, we begin to see a surge in technological and cultural innovation throughout Africa, culminating, beginning around 80,000 years ago with the rapid spread of modern humans out of their ancestral continent and into the rest of the world, apparently following a coastal route—at a rate of approximately 4 kilometers a year—up from the Horn of Africa, along the tropical coast of the Indian Ocean, on to Southeast Asia, and eventually to the Americas (Macaulay et al. 2005). A landmark event was the colonization of Australia around 65,000 years ago, which, as discussed at length ahead in Chapter 10, would have required crossing more than 70 kilometers of open ocean. As I explained earlier, since we know that indigenous Australians, the descendants of the original colonists, were speaking fully modern languages at the time of the first contact with European colonists during the latter part of the eighteenth century, and are, like any modern human, capable of learning and using any existing human language, we can be nearly certain that the capacity for recursion, and other brainbased features of modern language, must have been in place no later than this last exodus out of Africa, but not necessarily much earlier.8 All of this said, the nature of the relationship, if any, between a hypothetical surge in the power and efficiency of language and the recorded surge in human populations and the capacity for technical innovation is unclear. Possibly, beginning around 300,000 years ago, a more powerful and efficient version of human language, emerging from existing linguistic capacity, eventually triggered the so-called Human Revolution, leading to the final African exodus. Alternatively, it may have been that cultural forces, including new technologies and expanding populations, created pressure for more efficient and powerful versions of language. More likely, the causal chain was bidirectional, creating an explosion within an explosion. Increased capacity for language yielded more complex technologies and cultural practices, which in turn placed pressure on language to become even more powerful. But keep in mind that even 300,000 years is just a tiny slice of our history compared to the 6–10 million years since the last common

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ancestor of humans and chimps walked the Earth. It seems that 300,000 years, a span that admittedly encompasses the great bulk of human cultural evolution, may well be just the most recent chapter in a much longer story. If so, what came before? And did language, and teaching through language, play a role?

Late Onset (~500,000 Years) In fact, many researchers, perhaps the majority at this writing, believe that human ancestors had most likely acquired considerable capacity for language much earlier, by at least 500,000 years ago, around the time of H. heidelbergensis, a descendent of H. erectus, and the last common ancestor of modern humans, Neanderthals, and Denisovans (Dediu and Levinson 2013; MacWhinney 2005). H. heidelbergensis already had a significantly larger brain than H. erectus—nearly as large as those of Neanderthals and modern humans, suggesting these ancestors already had substantially increased cognitive capacity, and the ability to somehow meet the energy requirements of such an oversized primate brain. Indeed, the increase in brain size in H. heidelbergensis is consistent with evidence of increasingly complex tool use and the appearance of cultural behaviors that fall within the subset of behaviors that are considered modern. At around 500,000 years ago, H. heidelbergensis was already controlling fire for cooking, and manufacturing carefully shaped wooden spears, some with hafted stone points. These groups may have buried their dead and used red ochre as a pigment. The Neanderthals apparently inherited and refined these practices, managing to eke out a precarious existence in the harsh, subarctic climate of Western Europe and Central Asia until some 40,000 years ago. Neanderthals almost certainly had sewn skin clothing and footwear. They hunted large animals cooperatively, hafted stone tools with pitch heated with fire, and used fire for cooking meat and various kinds of starchy foods. They seem to have buried their dead at least some of the time, possibly with ritual offerings, and took care of elderly and infirm members of their groups (Wynn and Coolidge 2011). Thus, although proponents of the Modern Onset hypothesis write dismissively of “talking Neanderthals” (Hauser et al. 2014), we have good

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reason to believe that Neanderthals indeed had access to some form of spoken language, though quite possibly not as sophisticated as that of the anatomically modern humans who eventually replaced them. For one thing, reconstructions of vocal tract and ear anatomy, together with the presence of human versions of the FOXP2 gene (known to be involved in the fine motor control of speech organs), suggests that all three groups— H . heidelbergensis, Neanderthals, and early modern humans—may well have been capable not just of language, but of articulated speech. Beginning about 300,000 years ago, Neanderthal skeletal structure shows evidence of enhanced breath control, through enlargement in the vertebral canal encasing the bundle of nerves controlling muscles in the chest and abdomen. Unless Neanderthals and modern humans developed the capacity for language separately (a case of convergent evolution), it seems likely that human language, and (as argued below) teaching through language, may well date back at least 500,000 years.

Ancient Onset (~1.8 Million Years Ago) But could language be even older than that? Yes, it could. In fact, it seems reasonable to suppose that the founders of the Acheulean tool industry, H. ergaster/erectus, may have developed the capacity for an early version of language, a protolanguage, some 1.8 million years ago, and could have inherited at least the rudiments of this capacity from the australopithecines, who may have evolved a language-like form of communication, along with the use of manufactured stone tools, much earlier. Both scenarios are based, in part, on the assumed relationship, discussed earlier, between language and technology—specifically, the idea that “high-fidelity” transmission of sophisticated technical skill from one generation to the next is dependent on intentional instruction through language. Following this line of reasoning, a key question is whether knowledge of the fracture qualities of different types of rock, and the considerable skill in exploiting this knowledge as evidenced by Oldowan and Acheulean artifacts, could have been acquired without intentional instruction, i.e., by observation and emulation alone—in the same way

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that young chimpanzees learn to fish for termites and crack open nuts with rocks. Knapping, it turns out, requires a lot more skill than cracking nuts or sharpening a stick with your teeth. To turn a pebble or stone into a sharp cutting tool, you need first to find a specimen of suitable size and the right fracture qualities. Depending on the size of the core you select, you either hold it in your hand (for a larger piece), or place it on another rock to serve as an anvil. Then, using yet another rock or hard piece of wood as a hammer, you strike the target at a certain angle (about 60% off center) with just enough force to split off a single flake. You then study the resulting scar and consider the implications for your next hammer strike. If you see you’ve made a mistake, you can attempt to repair it. Otherwise, you rotate the core a bit to bring a new target point into line, then strike again carefully at perhaps a slightly different angle, with slightly different force. You continue until you’re satisfied with the results—or your teacher is. But that, of course, is just the question. Do the relatively crude, but nevertheless skillfully made stone choppers that show up in the archaeological record more than 3.4 million years ago imply intentional instruction among the australopithecines? And if not, what about the more complex tools that began appearing around 1.8 million years ago, in H. habilis, with their evident concern for conventional forms, including symmetry? Here it’s worth thinking again about the observed instances of tool use, and tool learning, in chimpanzees. Recall that wild chimps in certain groups (but not in others) learn to crack open nuts using a hammer and anvil technique, without intentional instruction. Full expertise requires a lot of trial-and-error practice over a period of several years. Mothers help, primarily by tolerating the efforts of the very young. Efforts of older juveniles are less tolerated, and mothers may even chase their own offspring away from the nut-cracking site if they get too much in the way. In the process of cracking nuts, a chimp may miss and strike off a flake by accident. However, the flakes seem never to get used. Interestingly, chimpanzees can be taught to knap, sort of. In the early 1990s, researchers successfully trained a bonobo (pygmy chimp) named Kanzi to create flakes by striking stone cores with a hammer, then to

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use one of the flakes to cut a rope to open a box with a food reward inside (Toth et al. 1993). However, even with patient instruction over a period of three years, Kanzi was never able to strike consistently with the right force and proper angle, and his cores and flakes never came to resemble Oldowan artifacts. Sometimes, instead of attempting to copy the motions of his human teacher, he cracked the rock by throwing it against a hard surface—in other words, he was emulating the result, a cracked rock, rather than imitating a specific set of hand gestures aimed at obtaining that result. What was missing? What was the difference between Kanzi’s situation, and that of our australopithecine ancestors, who are now known to have begun mastering the technique more than 3 million years ago? Of course, there were many differences. For one thing, Kanzi, with his long fingers, short thumbs, and locked wrists (adapted for knuckle walking) had a lot of difficulty gripping the implements properly. In cutting the rope, he used an awkward up-and-down sawing motion, not using his wrist as a human would, because his hands weren’t built for that. Australopithecines likely wouldn’t have had that problem: recent analysis suggests that they and related species were capable of humanlike hand postures by around the time the first evidence of knapping appears (Skinner et al. 2015). Another way to think about this is to imagine what it must have been like if the expert australopithecine stone knappers had behaved in the way chimpanzee mothers behave around youngsters trying to learn to use foraging tools. In the case of chimpanzees, it seems the mothers’ main contributions are (a) to allow the youngsters to tag along on foraging trips to termite mounds and nut-cracking sites; and (b) to tolerate their infant’s early clumsy attempts, even if it interferes with the mother’s own work. In some cases, chimpanzee mothers have been observed providing an infant with a suitable twig for termite fishing, but it seems this is nearly always in response to the infant’s own begging. Could australopithecine youngsters have learned the complex craft of stone knapping if their parents had demonstrated such little interest, and provided as little help, as chimpanzee mothers do? At some point in our history, we know that technology became far too complex to master by simple observation. Think of open-heart surgery. But at what point?

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Could human ancestors have been teaching, and using language to teach, 2 million years ago or earlier? As it turns out, there’s been some empirical work designed to answer this question. In an experiment designed by the anthropologist Thomas Morgan and his colleagues at the University of St. Andrews, graduate students were taught Oldowan knapping techniques, then asked to teach another student using the same method, and so on through a transmission chain of six individual learners/teachers (Morgan et al. 2015). The experiment involved five different “transmission conditions:” (1) reverse engineering (subjects had to figure out how to knap just by inspecting an example); (2) imitation/emulation (subjects observed an expert, then tried themselves, without any instruction); (3) basic teaching (the expert carefully demonstrated the skill and in some cases physically guided the learner’s hands); (4) gestural teaching (teachers and students were free to interact using any gesture they chose but could not talk; and (5) verbal instruction, meaning that students and teachers could both gesture and talk. As you might suspect, teaching, and especially verbal instruction, gave the best results. Learners who received explicit instruction, especially verbal instruction, produced a greater percentage of high-quality flakes, and worked more rapidly, than learners who were left to learn from a physical example (reverse engineering) or by observing the expert, but without help. Admittedly, evidence from laboratory experiments with modern humans—who already have access to modern language, and are accustomed to teaching and learning with language—is little more than suggestive. Nevertheless, the results are consistent with intuition. It makes sense that novices will acquire a complex skill more quickly and efficiently with expert assistance than without it, and expert assistance is more likely to be effective if the experts and novices have some way of communicating with each other. To be clear, we don’t need to think that an early form of language would have allowed its users to say something like “Hold the core in your left hand and, holding the hammer with your right hand, carefully strike the edge of the core at a 60-degree angle using medium force, then rotate and repeat.” At a minimum, for language to be useful for instruction, a teacher would need to be able to direct attention (“Watch.” ) and

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give positive and negative feedback (“Wrong.” “Right.”). As I’ll suggest in the next chapter, attention might have been directed through gesture alone (by pointing), and feedback could have taken the form of vocalizations, facial expressions, or some combination. That’s minimal, but it could have made the difference between a young australopithecine learning how to sharpen a stone, and thus contributing to the life of the group—or losing interest and giving up the effort in frustration. That might not have been such a big deal, unless the technology had somehow become essential to group survival. Then it would have mattered. So, that’s the basic argument. The craft of stone knapping is seemingly too complex to be acquired easily by simple observation and individual trial-and-error learning. A novice might eventually learn just by watching and imitating an expert. But if the expert takes an interest in helping the novice learn (an important first step), then it will be more efficient to demonstrate and communicate, not just demonstrate. The key idea here is that time is a precious commodity, and the cost of instruction must be compensated by the benefit gained from having a capable assistant. When the complexity of a critical skill reaches a certain point, the balance tips toward the need for explicit instruction, creating selection pressure for efficient communication through some form of language. Given all of this, it bears repeating that remaining physical manifestations of ancient hominin material culture—primarily left to us in the form of sharpened rocks and cut marks on bones—constitute what is quite possibly a biased sample of extant material culture. Artifacts made of wood, vines, or other organic materials, potentially at least as difficult to manufacture as hand axes (think, for example of nets made of knotted fiber) would have rotted away long ago. Nor do we have any way of telling what sort of knowledge about local habitats our distant ancestors had begun to accumulate after the split from the chimpanzee line 6–10 million years ago, or to what extent they may have relied on an early form of language in support of cooperative foraging.

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The Timing of Language Origins: A Synthesis Take a look now at Fig. 5.2. As before, the timeline begins, arbitrarily, some 4 million years ago, during the time of Australopithecus, the hominin for which we have the first reasonably strong evidence of tool use. It extends to the present time, just some 300,000 years after the emergence of the first anatomically and behaviorally modern humans (Hublin et al. 2017). The vertical axis represents cranial capacity in cubic centimeters, an indicator of brain size, and, less directly, cognitive capacity, in the different hominin genuses and species thought to represent our ancestors. Note again the step-like increments in brain size moving forward in time, marked by increasingly shorter plateaus and steeper rises, each step roughly associated with the emergence of a different ancestor species with a somewhat larger brain.

Fig. 5.2 Evolution of hominin brains, tools, and language. The horizontal axis represents (a) the emergence of increasingly sophisticated (but long-standing) hominin tool industries; and (b) hypothesized step increases in the complexity of language, from primate precursors, through increasingly complex protolanguages, to modern human language. The shaded graph on the vertical axis represents step increases in cranial capacity for representative hominin species

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Finally, take a look at the graph in the middle, meant to represent steps in the emergence of human language over time, from precursor capacities assumed to have been present in ancestral primates (as discussed in the next chapter), though increasingly powerful versions of protolanguage, assumed to have begun emerging, in rudimentary form, at around the time of the first appearance of intentionally-manufactured stone tools some 3.3 million years ago, and culminating in the appearance of fullblown modern language within the last 200,000 years, and possibly within the 100,000. As you can see, the graph depicts a gathering explosion (something like a multistage rocket), building fairly gradually over the first few million years of our history, growing in intensity over the last 500,000 years (beginning around the time of H. heidelbergensis), then, beginning as recently as 100,000 years ago, culminating in the explosion that sent humans into space and continues to rock the planet. Three features of this graph are worth reviewing: (a) the deep antiquity of language; (b) the long period of apparent stasis (as evidenced by the persistence of the Oldowan and Acheulean Tool Industries over millions of years; and (c) the power of the gathering explosion itself, especially over the last 300,000 years.

On the Antiquity of Language As I suggested at the beginning of the chapter, we need to think of language as at once ancient and modern. The argument for the antiquity of language, as I’ve made it, goes something like this. First, at the time of the split between human and chimpanzee lineages some 6–10 million years ago, hominins almost certainly already had a running start (represented by the dotted line in the language graph) down a new evolutionary path. As we’ll discuss in more detail in the next chapter, several biological precursors must already have been in place, including, but not limited to: (a) neural circuitry supporting sophisticated social intelligence (Kummer et al. 1997), which, among other things, allows individuals to recognize group members, recall their past behavior, and modify their

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own behavior accordingly; (b) a conventionalized signaling system— involving combinations of physical gestures, postures, vocalizations, and facial expressions—used intentionally to influence the behavior of other group members; and (c) the ability to engage in communicative turntaking. Assuming these capacities were available to the last common ancestor of humans and chimpanzees some 6–10 million years ago, human ancestors would already have had, by that time, a solid foundation from which to begin evolving the massively enhanced signaling system we call “language.” Second, as discussed in Chapter 4, language, and teaching through language—along with pair bonding, alloparenting, cooperative foraging, and reliance on high-value, difficult-to-access food sources—are arguably essential components of the human adaptive suite, and as such would have been under, and would have produced, increasing selection pressure as this new way of living in the world emerged. This does not mean that human-like language and teaching were fully present at the beginning, but until they did emerge, their absence would have been limiting factors, tending to make the new system unstable. This, combined with the existence of essential precursors, and the evident success of our early ancestors, suggests that early versions of language (protolanguages), and teaching through language, played an important role in hominin evolution from the start. Third, as discussed in this chapter, reasonably direct evidence for an early origin of language (and teaching through language) may be found in the archaeological record, especially in the presence of intentionallyflaked stone tools more than 3 million years ago, and in the apparent use of these tools for butchering of large animals—suggesting that at this point our ancestors were already engaged in cooperative foraging of high-value, difficult-to-acquire food sources, and may have already begun to teach. Also, recall again that language can be useful for purposes other than the transmission of technical skills—generally, for managing social relationships with others, including collaborative hunting and gathering—and, for these reasons, as a fitness signal. An individual who is more adept at using language to communicate with others is likely to make a more suitable mate and parent. All of this suggests that language,

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or at least the roots of language, may well predate the first appearance of manufactured stone tools.

On the Persistence of the Oldowan and Acheulean Tool Industries If language, brains, and technology all coevolved in a kind of arms race—increasing brain size supporting increases in language capacity, language capacity supporting technological innovation, technological innovation supporting more efficient extraction of difficult-to-acquire nutrients required by an increasingly hungry brain—then, once language got started, why didn’t it immediately lead to rapid, continuously accelerating technological innovation? If australopithecines had language, why did the Oldowan tool industry, largely unchanged, for a million years? Why did the Acheulean industry, once it got started some 1.7 million years ago, remain essentially unchanged until around 200,000 years ago? Here’s a clue: truly useful “primitive” technologies can persist for very long periods of time, even into the modern era. Surrounded as we are by a rapidly changing technological environment, where every hour brings new discoveries and inventions around the globe, we have a sense that technological development is inevitably rapid and all-encompassing, affecting every aspect of our lives. But this is not entirely true. We may cook with microwaves, but we still eat with knives, forks, and spoons, the basic shapes of which haven’t changed much in centuries.9 The bicycle I ride today is not that much different from the one I rode as a child. The saxophone I play every day, manufactured in Japan just a few years ago, is not very much different from the 1920s version I use as a back-up, which in turn is based on the same basic design invented by Adolphe Sax in the early 1840s. With these examples in mind, it’s not hard to imagine that the stone choppers of the Oldowan, and the slightly more sophisticated ones of the Acheulean, were used as long as they were simply because they were plenty good enough. The crank-and-ratchet mechanism of cultural transmission has many gears, some drive more slowly than others, and

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not all are engaged at the same time. The Oldowan and Acheulean industries may have persisted as long as they did, along with cranial capacity and, by inference, linguistic capacity, simply because these technologies, and the brains that invented them, were sufficiently powerful to ensure the continuing survival of their users, for millions of years.

The Gathering Explosion But then something happened. Owing to some factor, or combination of factors, the crank-and-ratchet mechanism of cultural transmission started to spin a little faster that it had before. Evidence of modern behaviors—already apparent in artifacts left by H. heidelbergensis from around 500,000 ago—becomes more widespread with the first appearance of anatomically modern humans around 300,000 years. But still there’s a lag of some 100,000 to 50,000 years before DNA evidence indicates an evolutionary bottleneck followed shortly afterward by the surge in population growth, technological sophistication, and diversity of cultural practices that continues, with increasing acceleration, to this day. What caused the lag? And why the relatively sudden surge? Although the evidence is no more than circumstantial, it seems reasonable to suppose that anatomically modern humans had already acquired sophisticated capacity for language as early as 300,000 years ago, which is consistent with the early emergence of modern behaviors (such as ritual burial and the use of jewelry) at around this time. In other words, some basic pre-conditions were in place, but these were not sufficient to catalyze the accelerated pace of change that was to follow. Then, beginning around 100,000 years ago, the challenges to survival posed by uncertain weather patterns (oscillating wet and dry periods) created new selection pressures for technical innovation, more efficient methods of foraging, and enhanced capacity for language. Groups with larger numbers of innovators outcompeted other groups, squirmed through the bottleneck, and managed to expand their numbers. This demographic expansion would have had a twofold effect. First, technical innovations (under pressure to innovate) would have become even more likely, simply because there were larger numbers of talented

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individuals around to innovate. In turn, in a snowballing effect, more complex technologies would have selected for individuals with the cognitive capacity to use them, and to pass expertise along to upcoming generations. Also, these successful new foraging strategies and the consequent population growth would have forced (and enabled) expansion into new foraging territories, which would have required further technical innovation (e.g., coastal ocean navigation), and put these migrants in contact with other groups and other cultures as they trekked (or boated) out of Africa and into regions of the world already inhabited by H. erectus, Neanderthals, and Denisovans. In this way, we can make some sense of the basic evidence available for the past 300,000 years of human evolution. Simply put, while anatomically modern humans, showing evidence of modern behaviors, first emerged some 300,000 years ago, it apparently took another 200,000 years of more or less gradual change before some combination of factors converged, relatively suddenly, to trigger the population expansion and propel the crank-and-ratchet mechanism of cultural evolution forward at an unprecedented rate. Whether these ancestors had already evolved the capacity for recursive syntax (Chomsky’s “Unbounded Merge”) prior to the point of takeoff, or whether the necessary rewiring occurred at around this time and was perhaps a primary driver, remains unclear. In either case, an argument can be made for the emergence of fully modern linguistic capacity in humans at least within the past 300,000 years or so, and likely more recently—but no more recently than about 100,000 ago when the ancestors of modern humans everywhere, all of whom can easily acquire any human language, first began the long trek out of Africa into the rest of the world.

So That’s When, But How? In summary, it seems that this thing we call language, which has come to set us apart from all other animals, and which has, for better or worse, given us dominion over them, is at once ancient and modern. The roots of language run deep, far back into our primate past. At some point after the split from the chimpanzee lineage some 6–10 million years ago,

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our distant ancestors, faced with unknown pressures and opportunities, apparently began to evolve a new suite of adaptive traits that came to include habitual bipedalism, pair bonding, cooperative parenting and foraging, an increased juvenile period and overall lifespan, and a dependency on high-quality, difficult-to-acquire food sources to feed their increasingly oversized brains. At some point, language, and teaching through language, became a fundamental part of this new package. A reasonable guess is that a simple protolanguage emerged at least 2–3 million years ago, and, through a process of niche construction, soon became part of our ancestor’s surroundings. Children born into these early hominin groups now had a new task—to learn this new signaling system as quickly as possible. And language itself had an obligation—to assume forms that newborns and youngsters could learn as quickly as possible. As a result, language and developing brains settled into a long-term, coevolutionary relationship. As the use of language became essential to both individual and group survival, individuals born with genetic programs that made them, in one way or another, slightly better at using language (e.g., slightly better short-term memory, slightly better at taking another person’s perspective) became slightly more likely to survive into adulthood, find suitable mates, and raise offspring to adulthood. At the same time, language itself evolved in directions shaped by its users’ needs for optimum power, efficiency, disambiguation, and learnability. In a sense, human language and human brains become one—a single integrated system that, millions of years later, has allowed me to write this book, and you to read it. But there’s an even deeper mystery. Exactly how could language, and teaching through language, have gotten started in the first place? What particular combination of natural pressures and opportunities could explain such an extraordinary development?

Food for Thought 1. What is the relationship between protolanguage and language itself? 2. What are the distinguishing features of modern human language? 3. Was the evolution of language gradual or jerky? Explain.

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4. How do we know that fully modern human language had emerged by at least 80,000 years ago? 5. Do you think it’s important to have at least some sense of how old human language is? Explain.

Notes 1. The term protolanguage, as employed by Bickerton (1992), is both useful and misleading. It’s useful in that it allows us to refer to earlier forms of human language, e.g., languages that lacked the complex syntax, phonology, and intricate systems of symbolic reference characteristic of all present-day human languages. It’s misleading in that the term may suggest an abrupt transition from protolanguage to full-blown language, which, given the intimate relationship between our evolving brains and language, seems highly unlikely. In using the term “protolanguage,” I therefore don’t mean to imply the existence of a single point in time when human communication jumped a gap between protolanguage and language. Instead, like all evolutionary processes, especially those occurring over millions of years, language must have grown in fits and starts, with periods of relative stability, gradual change, and sudden surges and shifts. In short, the term “protolanguage” is a useful way of referring to what must have been multiple early forms of language as these developed over time, with varying and increasingly modern characteristics. 2. Here I am thinking, for example, of the mythical islands in the cognitive maps employed, and passed down from one generation to the next, by the seafaring navigators of Micronesia. See Gladwin, T. (1970). East is a big bird: Navigation and logic on Puluwat. Cambridge, MA: Harvard University Press. 3. Currently, the earliest wooden throwing spears come from Germany, from a site dated to about 500,000 years ago. See Thieme, H. (1999). Lower Palaeolithic throwing spears and other wooden implements from Schöningen, Germany. Hominid evolution: Lifestyles and survival strategies (pp. 383–395). Gelsenkirchen: Edition Archaea. However, the German specimens show a sophisticated understanding of balance, suggesting an advanced state of a more ancient technology, possibly dating back another 500,000 years or longer. See Kelly, R. C. (2005). The evolution of lethal intergroup violence. Proceedings of the National Academy of Sciences, 102(43), 15294–15298.

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4. The term “Human Revolution,” also known as the Upper Paleolithic Revolution, was first used to describe what was considered a sudden flowering of symbolic culture—in the form of carved figurines and cave paintings— some 40,000 years ago among H. sapiens in Europe. See Mellars, P. A. & Stringer, C. (Eds.). (1989). The human revolution: Behavioural and biological perspectives in the origins of modern humans. Edinburgh: Edinburgh University Press. It is now understood that the emergence of modern human behaviors began much earlier, on the order of 250,000 to 300,000 years ago in Africa. See the following note. 5. See McBrearty, S., & Brooks, A. S. (2000). The revolution that wasn’t: a new interpretation of the origin of modern human behavior. Journal of human evolution, 39 (5), 453–563. To be clear, some of these practices, including the use of bone tools, are also found in Neanderthals (as in the case of bone tools), and H. erectus (as in use of coastal resources). 6. Recently the linguist Daniel Everett has claimed that Pirahã, an isolated language of the Brazilian Amazon spoken by approximately 700 illiterate hunter-gatherers, has no recursive structures of the type the sentence I have here typed illustrates (Everett 2012), a finding apparently confirmed by Sakel and Stapert (2010). Instead of saying “John’s brother’s house,” Everett reports that a Pirahã speaker must say the equivalent of “John has a brother. The brother has a house.” Everett has used this evidence to claim that recursion is not, as Chomsky and others have claimed, a defining feature of language in general, and thus not an aspect of Universal Grammar. Everett’s claim has been disputed on two grounds: first, that Pirahã, as evidenced in the data provided in Everett’s doctoral thesis, does indeed allow clausal embedding of a type that meets at least one definition of recursion (Nevins et al. 2009). It has also been pointed out, by Chomsky and others, that just because the brain-based faculty of language makes recursion and other forms of embedding possible, this does not mean that recursion, especially in the sense of central embedding, needs to be present in every language. Our brains place constraints on the different forms that language can take, but quite obviously do not dictate these forms, which is why we see languages as diverse as Pirahã and English. If a baby born to a Pirahã speaker but raised from birth an English-speaking household turned out to be incapable of producing and understanding English recursion, that would indeed upset the whole applecart. 7. See Note 6. 8. An alternative hypothesis is that the capacity for modern language, including, for example, the capacity for recursion, evolved independently

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in scattered populations of human ancestors after the final migration out of Africa some 100,000 years ago, as a case of convergent evolution. I have not been able to find any support for this in the relevant literature. 9. One of the most striking examples of the persistence of technology I know of is the use of bamboo scaffolding in the construction of modern skyscrapers in Hong Kong and other parts of Asia. On a recent visit, in 2016, I noticed that the workers had started using plastic lashing (instead of bamboo strips), and were wearing modern climbing shoes; the scaffolding itself, however, was bamboo—lighter, cheaper, and at least as strong as steel.

Suggested Reading Ambrose, S. H. (2001). Paleolithic technology and human evolution. Science, 291(5509), 1748–1753. A short, useful introduction to the relationship between stone tool manufacture and other factors in human evolution. Bickerton, D. (2017). Language and human behavior. Seattle: University of Washington Press. An important new book on the coevolution of language and human cognition, by the author who first introduced the term “protolanguage” into the discussion of language origins. Dediu, D., & Levinson, S. C. (2013). On the antiquity of language: The reinterpretation of Neandertal linguistic capacities and its consequences. Frontiers in psychology, 4, 397. An argument that spoken language had likely emerged by at least 500,000 years ago. Hauser, M. D., Yang, C., Berwick, R. C., Tattersall, I., Ryan, M. J., Watumull, J. (2014). The mystery of language evolution. Frontiers in Psychology, 5, 401. An argument that spoken language, as defined by the use of “unbounded Merge” is of relatively recent origin. Chomsky is one of the coauthors. Morgan, T. J. H., Uomini, N. T., Rendell, L. E., Chouinard-Thuly, L., Street, S. E., Lewis, H. M., et al. (2015). Experimental evidence for

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the co-evolution of hominin tool-making teaching and language. Nature Communications, 6 , 6029. Reports on an experiment in which graduate students learned to “knap” with (and without) various forms of instruction, including instruction through language. Pinker, S. (2003). Language as an adaptation to the cognitive niche. Studies in the Evolution of Language, 3, 16–37. An important and useful paper in which Pinker argues for language is a biological adaptation of ancient orgin. Tomasello, M. (2009). The cultural origins of human cognition. Cambridge: Harvard University Press. An important book on the role of the “ratchet effect” in human evolution.

References Aboitiz, F., Aboitiz, S., & García, R. R. (2010). The phonological loop: A key innovation in human evolution. Current Anthropology, 51(S1), S55–S65. Bickerton, D. (1992). Language and species. Chicago: University of Chicago Press. Chomsky, N. (2010). Some simple evo devo theses: How true might they be for language. In R. Larson, V. Déprez & H. Yamakido (Eds.), The evolution of human language (pp. 58–59) Cambridge: Cambridge University Press. Clarkson, C., Jacobs, Z., Marwick, B., Fullager, R., Wallis, L., Smith, M., et al. (2017). Human occupation of northern Australia by 65,000 years ago. Nature, 547 , 306–310. Dediu, D., & Levinson, S. C. (2013). On the antiquity of language: The reinterpretation of Neandertal linguistic capacities and its consequences. Frontiers in Psychology, 4, 397. Everett, D. L. (2012). What does Piraha grammar have to teach us about human language and the mind? Wiley Interdisciplinary Reviews: Cognitive Science, 3(6), 555–563. Fitch, W. T. (2010). The evolution of language. Cambridge: Cambridge University Press.

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Harmand, S., Lewis, J. E., Feibel, C. S., Lepre, C. J., Prat, S., Lenoble, A. (2015). 3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya. Nature, 521(7552), 310–315. Hauser, M. D., Yang, C., Berwick, R. C., Tattersall, I., Ryan, M. J., Watumull, J. (2014). The mystery of language evolution. Frontiers in Psychology, 5 (1), 401. Heyes, C. (2018). Cognitive gadgets: The cultural evolution of thinking. Cambridge: Harvard University Press. Hublin, J. J., Ben-Ncer, A., Bailey, S. E., Freidline, S. E., Neubauer, S., Skinner, M. M., ... & Gunz, P. (2017). New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens. Nature, 546 (7657), 289. Hutchings, W. K., & Brüchert, L. W. (1997). Spearthrower performance: Ethnographic and experimental research. Antiquity, 71(274), 890–897. Kelly, R. C. (2005). The evolution of lethal intergroup violence. Proceedings of the National Academy of Sciences, 102(43), 15294–15298. Kummer, H., Daston, L., Gigerenzer, G., & Silk, J. B. (1997). The social intelligence hypothesis. In Human by nature: Between biology and the social sciences (pp. 157–179). Mahwah: Erlbaum. Macaulay, V., Hill, C., Achilli, A., Rengo, C., Clarke, D., Meehan, W., et al. (2005). Single, rapid coastal settlement of Asia revealed by analysis of complete mitochondrial genomes. Science, 308(5724), 1034–1036. MacWhinney, B. (2005). Language evolution and human development. In Ellis, B. J., & Bjorklund, D. F. (Eds.) Origins of the social mind: Evolutionary psychology and child development. Guilford Press. 383–401. McBrearty, S., & Brooks, A. S. (2000). The revolution that wasn’t: A new interpretation of the origin of modern human behavior. Journal of Human Evolution, 39 (5), 453–563. McPherron, S. P., Alemseged, Z., Marean, C. W., Wynn, J. G., Reed, D., Geraads, D. et al. (2010). Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia. Nature, 466 (7308), 857–860. Mellars, P. (2006). Why did modern human populations disperse from Africa ca. 60,000 years ago? A New Model: Proceedings of the National Academy of Sciences, 103(25), 9381–9386. Mellars, P. A., & Stringer, C. (Eds.). (1989). The human revolution: Behavioural and biological perspectives in the origins of modern humans. Edinburgh: Edinburgh University Press.

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Morgan, T. J. H., Uomini, N. T., Rendell, L. E., Chouinard-Thuly, L., Street, S. E., Lewis, H. M., et al. (2015). Experimental evidence for the co-evolution of hominin tool-making teaching and language. Nature Communications, 6 , 1–8. Nevins, A., Pesetsky, D., & Rodrigues, C. (2009). Pirahã exceptionality: A reassessment. Language, 85, 355–404. Pinker, S. (2003). Language as an adaptation to the cognitive niche. Studies in the Evolution of Language, 3, 16–37. Sakel, J., & Stapert, E. (2010). Pirahã—In need of recursive syntax? Recursion and human language (pp. 3–16). Berlin: De Gruyter. Skinner, M. M., Stephens, N. B., Tsegai, Z. J., Foote, A. C., Nguyen, N. H., Gross, T., ... & Kivell, T. L. (2015). Human-like hand use in Australopithecus africanus. Science, 347 (6220), 395–399. Thieme, H. (1999). Lower Palaeolithic throwing spears and other wooden implements from Schöningen, Germany. Hominid evolution: Lifestyles and survival strategies (pp. 383–395). Gelsenkirchen: Edition Archaea. Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral and Brain Sciences, 16 (3), 495–511. Toth, N., Schick, K., Savage-Rumbaugh, S., Sevcik, R., & Rumbaugh, D. (1993). Pan the tool-maker: Investigations into the stone tool-making and tool-using capabilities of a bonobo (Pan paniscus). Journal of Archaeological Science, 20, 81–91. Wadley, L. (2005). Putting ochre to the test: Replication studies of adhesives that may have been used for hafting tools in the Middle Stone Age. Journal of Human Evolution, 49 (5), 587–601. Wilkins, J., Schoville, B. J., Brown, K. S., & Chazan, M. (2012). Evidence for early hafted hunting technology. Science, 338(6109), 942–946. Wynn, T., & Coolidge, F. L. (2011). How to think like a Neandertal . Oxford: Oxford University Press.

6 Pointing: The Royal Road to Language?

Before you begin reading this chapter, try this: point at something, say the chapter title just above. Now, while maintaining the point, look carefully at the shape of your hand. What do you see? What do you feel? (The chapter title, by the way, is borrowed from Butterworth [2003], “Pointing is the royal road to language for babies,” which discusses the role of pointing in language acquisition. In this chapter, we’ll consider the possibility that pointing was also, if not the royal road itself, then at least an important step on the road to the evolution of language among our ancestors.) If you’re like me, and nine out of ten other readers, you point with the index finger of your right hand. In either case, it’s likely your index finger is fully extended, forming a remarkably straight line in the direction of the point, and your arm may also be slightly extended. Your three other fingers are likely curled back, with the tips resting against the middle of your palm, or perhaps your pinkie finger sticks out a bit. Your thumb rests on top of your middle finger, between the second and third joints. If you’re like me, your palm is vertical, but you could be holding it horizontally, face down, which is also common. If you’re like me, the gesture feels comfortable and natural , as if nature had designed the anatomy of © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_6

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our hands for this exact purpose. To feel the contrast, try pointing with your little finger, or the one next to it. Not quite so easy, right? In fact, it seems that indexical pointing (pointing with the index finger) may indeed be as natural and necessary in humans as our upright bipedal gait, fist-like “power grip” (used in gripping a tree branch, baseball bat, or knife) and pincer-like “precision grip” (thumb against finger, useful for plucking berries from trees, picking up coins in the street, sewing with a needle). Pointing is not the only gesture humans use to direct attention; we can point, more subtly, with our heads, chins, lips, or by swiveling our eyes. In fact, it has been suggested that the white part of the human eye, the sclera, is an adaptation in humans that makes gaze following easier (see Tomasello et al. 2007b). However, finger points are especially demonstrative.1 But there’s also something deeply unnatural about the pointing I asked you to do. We hardly ever point just for the sake of pointing, or because someone tells us to. Rather, we almost always point at something in our immediate surroundings, or in a certain direction away from our current position, in the company of someone else, for some specific communicative purpose. Further, we point for multiple purposes, and when we point we usually say something, and what we say clarifies the purpose of our pointing. Among other purposes, humans point to direct another person’s attention to some object (“Look at that!”); to name an object (“That’s an umbrella.”); to ask a question about an object (“Is that what you’re looking for?”); to share emotion about an object or event (“Wow!”); to convey information about the location of an unseen object (“There’s an antelope carcass beyond that tree.”); to indicate the direction in which an unseen agent moved (“It went that way.”); and to direct someone to go in a certain direction (“Go that way.”) or to a specific location (“Stand over there.”). In short, pointing is an eminently versatile and useful gesture. However, unless the purpose of the point can somehow be deduced from the context, pointing alone is insufficient and confusing. It is for this reason that points are typically accompanied by some other signal, or combination of signals, including facial expressions, physical gestures, and vocalizations, which serve to disambiguate (clarify) the intended communicative purpose of the point from its many possible

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purposes. Indeed, this combination of physical gestures, facial expressions, and speech sounds is characteristic of the animated, multimodal nature of human language, which typically involves conventionalized and fully integrated combinations of physical gestures—broadly interpreted to include hand gestures, changes in posture, gaze, facial expressions, and the articulatory gestures we make with our mouths, lips, and tongue to produce spoken words. Another interesting thing about pointing, or at least certain kinds of human pointing, is that points only work, as language itself only works, if participants each understand the other as a person with her own beliefs, desires, and intentions. In other words, it seems to require the ability to take the mental perspective of another person, either the one who is pointing for you, or the one you are pointing for. If you and I are standing on a street corner, and I point in the direction of some people standing in line outside a Mexican restaurant across the street, you will naturally assume that I, as a fellow human, have some specific communicative purpose in mind. You will assume that I am not just holding my finger out for your inspection, and you will be able to work out what I’m pointing at by imagining a horizontal line running from my finger across the street to the people in front of the restaurant. In other words, you will have taken my perspective to work out what I am pointing at, using a form of geometric thinking (for a discussion of this kind of thinking in dolphins and chimps, see Pack and Herman 2006; Tomasello et al. 1999). Also unless my purpose is already clear from the context (perhaps we’d been looking for this restaurant together, or looking for one of the people standing in line), you will want to ask me why I’m pointing, because you will assume I have something in mind I haven’t fully shared with you. Similarly, if I point without saying anything, I may do so because I believe, on the basis of a previous conversation, that you will understand my intention. Or, if not, then I will (or should) understand that you will need to ask me why I’m pointing, and precisely what I’m pointing at (the restaurant itself? someone in the queue?). As I explain later in the chapter, the fact that our chimpanzee cousins typically fail to understand pointing in this way, as a contextualized communicative act, may very well result from both the inherent ambiguity of pointing and

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an insufficiently developed capacity for taking another’s perspective into account—for mindreading. Yet another important aspect of communicative pointing is that it occurs in the physical here and now. The reference is necessarily to something in the interlocutors’ joint attentional frame (currently or recently present, but possibly out of sight), or to a direction relative to a current location (“You go that way.”). It’s possible to refer to objects or events or directions that are not in the here and now (“Remember the Mexican restaurant across the street from where we were standing?”), but this can’t be done with physical pointing; rather, it requires a more sophisticated and abstract kind of pointing—not physical reference, but symbolic reference, words that point to something out of sight, in our minds and shared knowledge, a feature of human language known as displacement (Hockett 1960). Physical pointing is nowhere near as powerful as symbolic pointing, but it’s a useful and quite possibly necessary first step.

Tipping Points in Dynamic Systems You’ve probably guessed where I’m going with this. As established in the preceding chapters, we have good reason to believe that the roots of human language extend far back in time, at least some 6–10 million years ago, back into the brains of the last common ancestor of humans and chimpanzees, and, strictly speaking, even farther back, into the brains of their ancestors. As discussed in the previous chapter, we also have reason to believe that a useful protolanguage could conceivably have emerged from these ancient roots at least as early as 3.3 million years ago, the point at which we have evidence that early hominins had begun manufacturing tools sufficiently complex to have benefited from intentional instruction, possibly implying the existence of some form of language (Morgan et al. 2015). However, no matter how deep in time the roots of language extend, nor how recently it burst into full bloom, we still need to think how and why the first shoots emerged and grew into something qualitatively different from any other system of communication in the animal world. Given that we’re the only species—more accurately, the only remaining

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primate species—that has managed to evolve a human-like language, it is clear that the process was not inevitable, despite the obvious competitive advantages language has turned out to convey. Something special must have happened. What was it? Before we go any farther, let me acknowledge that he or she who speculates, especially in print, about the early origins of human language, and therefore of human pedagogy, risks ending up being accused of telling a “just so story,” the standard, embarrassing, and (some might feel) tiresome characterization of accounts based on circumstantial evidence, intuition, and the kind of speculation I’m about to engage in here. As often noted, language doesn’t fossilize. Absent stronger evidence, it’s easy to dismiss speculation about language origins as premature and therefore a waste of time (recall Hauser et al. 2014). But circumstantial evidence is evidence, and the more of it, the stronger the case. And in this case, to the extent we believe that the evolution of language and human pedagogy are instances of earthbound, biological and cultural evolution—not something caused by a virus from outer space—we have plenty to work with. We can study how other biological systems have evolved and think how lessons learned from these studies apply to the case of human language. More specifically, we can look for evidence of the biological precursors of language in our closest relatives, and think how these precursors might have evolved, under whatever environmental pressures we might have evidence for, into the various components of the capacity for language in humans. And, assuming that language capacity is not just a specialized gadget that somehow got grafted onto a languageless primate brain, but is part and parcel of a radically different biological solution to the problem of existence in a dangerous world, then we can look at this solution in its entirety and think where language fits in, and how it might have begun fitting in. Now, let me also explain what I mean by indexical pointing as a possible “tipping point” for the evolution of a human protolanguage. As you likely know, the tipping point metaphor, originated by University of Chicago political scientist Morton Grodzins (who applied it to studies of “white flight” from neighborhoods in Chicago), and later appearing in the title of a popular book by journalist Malcolm Gladwell (2006),

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attempts to explain a sudden phase shift, when a period of seeming equilibrium in a system is punctuated by a relatively rapid, irreversible shift into a new state. The tipping point metaphor brings together five related ideas. First, prior to tipping into a new state, whatever system it is must have the potential for change; it cannot change unless it already has the internal capacity or predisposition. In nature, the potential for change lies in the natural variation in the genes that build precursor traits, and, less importantly, in mutations to these genes that happen to help the organism survive in whatever habitat it occupies. To use an example we’ve already discussed, early on in fetal development, the fingers and toes of most vertebrates (e.g., mammals, birds, reptiles) are fully connected by a web of tissue, which, in a process called apoptosis (programmed cell death), is removed, to varying degrees in different animals, before birth. Take a look now, and you’ll see that you have some small amount of webbing remaining at the base of your own fingers. (Same with your toes: check if you like.) Some people have a little more webbing, some a little less. Generally, less is better for humans, because the absence of webbing allows us to move our fingers and toes independently, the better for grasping branches, and handling sticks and other tools. However, some animals, notably aquatic birds such as ducks, geese, and swans, and some mammals (beavers and platypuses are examples), have retained the webbing between their toes because it helps them swim faster. Note that the potential for webbed feet in these animals was already in place; however, it was only under selection pressure from the animal’s habitat that individuals whose genes didn’t remove quite so much webbing during fetal development were slightly more likely to survive and pass these genes along. This brings us to the second important factor in tipping point dynamics, which is that there must be some pressure for change, often imposed by external forces. Even if the system has the capacity to change, it will not change unless something encourages it to. This is especially the case, as is typical is in nature, where a change is costly. For example, a larger brain may offer more computational power, but the energetic cost of fueling it may offset any additional gain in cognitive efficiency, so it remains unchanged.

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This brings us to the third factor in tipping-point dynamics. A system may have the potential for change, and may be under pressure to change, but, owing to one or more blocking factors, cannot change unless and until the blocking factors are removed. Otherwise, the system would just keep tapping into its potential for change to meet whatever pressures for change might arise. If we consider the fact that some 99% of the estimated 5 billion species that have ever lived on Earth have since gone extinct (Stearns and Stearns 2000), presumably against their will, we can conclude that evolutionary blocking factors are common in nature. Given that environmental change can occur far more rapidly than evolutionary change, this should not be surprising. It explains, for example, why squirrels have, with fatal and messy consequences, not yet evolved the inclination to wait and look both ways before scampering across the street in front of my house. Fourth, some “small thing” (in Gladwell’s phrasing), a small catalyzing event or seed, has the effect of removing or resolving the blockage. As a result, a rapid wholesale change may occur as the system’s inherent capacity for change, which had previously been blocked, rushes to meet the previously unmet need. Note that blocking factors help to resolve the debate between the “gradualist” view of biological evolution, in which evolution is understood to proceed through a series of tiny incremental steps, and the “saltationist” view, which emphasizes sudden shifts, as in Steven Gould’s notion of punctuated equilibrium (Gould and Eldredge 1977). An example of the latter is Chomsky’s idea, discussed in the preceding chapter, that modern human language emerged suddenly, some 100,000 years ago, when a single mutation, creating the capacity for recursive syntax (“Unbounded Merge”), led to a complete rewiring of the brain (Chomsky 2010). Understood as an example of the tipping point phenomenon, such an event must have been preceded by a state of existing capacity (a new feature of language could not take hold without existing potential), building environmental pressure (a need that recursion could fill), and an explanation as to why this need had not previously been met. For example, one might argue, as Chomsky does, that the mental capacity to produce and interpret recursively embedded utterances (“We’ll eat the goat the leopard killed yesterday tomorrow.”)

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resulted from a single mutation in the genetic machinery that builds human brains, a happy accident. This may be, but to explain how a capacity for linguistic recursion suddenly emerged, it’s necessary to explain the other aspects of the phenomenon. More generally, in order to explain any given instance of a tipping point, we must identify all five components: (1) the nature of precursor capacities; (2) the nature of the pressure for change; (3) the initial blocking factors; (4) what caused the system to tip into the new state; and (5) what it was about the new state that subsequently triggered massive change in the system.

Disambiguated Pointing as a Tipping Point Briefly stated, the argument for disambiguated pointing as a possible tipping point for the emergence of a simple protolanguage in our distant hominin ancestors goes like this. First, we know that nature must have something to work with. A rudimentary capacity for human-like language must have already existed in the genomes of our ancestors, including the last common ancestor of humans and chimpanzees. As we’ll see, these precursor traits likely included the ability and inclination to use a sizeable and flexible repertoire of gestures, body postures, facial expressions, and (to a lesser extent) vocalizations to intentionally control the behavior of others. The pressure for change, we can guess, lay in the growing need, as discussed in previous chapters, for an enhanced system of social communication which would support joint attentional activities associated with (a) pair bonding and cooperative breeding (parenting); (b) cooperative foraging, which at once enabled and depended on efficient extraction of high-value, difficult-to-access food sources; and (c) a growing need to pass along technical knowledge associated with these strategies from one generation to the next, that is, to teach. However, in spite of a latent capacity for language, and building selection pressure for it to emerge, something, for some unknown period of time, must have prevented even a simple form of language from emerging—likely the same factors that have so far blocked its emergence in other primates. Judging from what we know about chimpanzee

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thinking, and assuming that our common ancestor had the same limitations, we can imagine there were at least two blocking factors: (a) limited capacity for mental perspective taking (“theory of mind”; Premack and Woodruff 1978) and (b) a limited inclination to share useful information with others, for their own benefit. To put it bluntly, it seems likely our distant ancestors were too self-focused, and too selfish, for language to take hold among them—up to a point. But then something must have changed. We don’t know what it was, but, as I will argue later in the chapter, a plausible candidate was the appearance and group adoption of a single cultural innovation— in this hypothesis, the use of indexical pointing combined with other gestures and/or vocalizations that served to disambiguate the purpose of the pointing and, in this way, eventually replaced the blind spot in the animal’s perspective-taking circuitry with a sort of “third eye” A gesture that had previously been too ambiguous to be useful could now take on specific meanings; furthermore, these new signals carried a new kind of information, a glimpse into the contents of another animal’s mind. To say that a protolanguage enhanced our capacity for mindreading is of course just a metaphor. Skulls don’t have windows, and the “cognitive state” of another person’s brain, whatever that means, is largely (and fortunately) a secret. That said, language does indeed provide a means of sharing thoughts and of providing clues to what others are thinking, even if they wish to hide those thoughts from us, as in the case of transparent lies. Once the connection was made between indexical points and disambiguating signals, it would have been relatively easy to meet the need for further disambiguation by adding additional components to the signals and to acquire these new elements through social learning. Further, as the new system grew in communicative power, it would have become increasingly easy to read the minds of others, to convey the contents of one’s own mind, and thus to engage in collective thinking, joint attention, and true collaboration. Crucially, once this new tradition of communication became even partly established, individuals who were even slightly better at using the new system would have been slightly more successful participants in cooperative foraging activities and would have been better fed,

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better at attracting and retaining sexual partners, and more successful at protecting, provisioning, and teaching their own sons, daughters, nieces, and nephews. As a result, the new signaling system, an emergent protolanguage, would have begun to breed for brains that were especially competent at using it. The fateful coevolution of human brains, language, and cumulative culture had begun! Before we continue, some caveats. First, I don’t mean to imply that one fateful morning in Africa a single ancestor ape had the novel idea of pointing at a poisonous plant and making a retching sound in the presence of her infant, and that this single act automatically sent her group down the road to language, once and for all. Rather, there were likely many false starts, related communicative innovations that never got copied, and innovations that became local traditions but then for some reason died out before the mechanisms of biological evolution could begin to do their work. Such false starts could have cropped up, here and there, possibly over many thousands, if not hundreds of thousands of years, before some lucky and unknown combination of factors allowed the new system of disambiguated pointing to take hold, culturally and biologically, to the degree that it could serve as a foundation for what was to follow. Now, as you may have noticed, there’s a bit of fuzziness in my argument, which amounts to a “cold start” problem. From one side of my mouth, I’m saying that disambiguated pointing requires a certain minimum capacity for perspective taking, combined with an inclination for sharing, without which it doesn’t work. And from the other side, I’m saying that disambiguated pointing fosters perspective taking, because it provides a glimpse into another person’s mind. So, how exactly did our ancestors pull off that bit of magic and jump the gap into the world of language? While I don’t pretend to have a completely satisfying answer to that crucial question, a couple of things are worth thinking about. For one, the gap between being able to engage in true joint attention and not being able to has to be exceedingly hard to cross. Otherwise, given the advantages it offers, other primates and other animals might well have made the crossing long ago. We could be sitting around talking about language with a focus group of crows, chimps, elephants, and whales.

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Also, it’s useful to recall that at some level we’re talking about an essentially biocultural phenomenon. Disambiguated pointing and indeed all aspects of human language depend on both biologically evolved capacities and inclinations and culturally evolved traditions. Human infants are born with an innate capacity and drive to rapidly acquire whatever human language or languages are spoken by members of the culture they happen to land in, but if they’re locked away in a closet or in some other way are denied access to linguistic interaction with other members of the surrounding culture, their powerful instinct for language acquisition will have nothing to work with (as in the unfortunate case of the urban “wild child” Genie; see Curtiss 2014). Seen in this way, the gap between the brain-based capacity for perspective taking and its cultural use may not seem that large. In a sense, they’re one and the same thing. Whether the first step took the form of a cultural innovation or a biological change that made the innovation possible may be less important than the dynamics of the biocultural, coevolutionary interaction that followed. Finally, I don’t mean to imply that disambiguated pointing, once established, would have led inexorably to modern human language. Other hard problems would need to get solved first—fine-motor control of speech organs, refined neural capacity and circuitry for symbolic reference, rapid turn taking, enhanced short-term memory, brain-based rules for word and sentence formation, and, likely much later, the capacity for complex syntax with recursive embedding. That these problems evidently did get solved doesn’t mean that they inevitably would; each likely required a similar combination of gradual increments in capacity, selection pressures, constraints, and unlikely tipping point events, which might never have occurred or come too late. Remember that several other hominin species (Homo heidelbergensis, the Neanderthals, and Denisovans), each of which might well have had some early version of language, ran aground along the way. That our own Homo species alone survived, eventually enjoying access to full-blown modern human language, should not lead us to think that language is an inevitable outcome for a bipedal primate, nor that once a protolanguage got a foothold, it would naturally evolve as it has.

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Language Precursors in Primates With these caveats in mind, let’s now get back to the main components of my argument, starting with the existence of biological precursors. As evolutionary biologist François Jacob reminds us in his paper “Evolution and tinkering” (Jacob 1977), nature cannot build new systems from scratch. Rather, pre-existing systems are reorganized, repurposed, and enhanced by natural selection and other evolutionary mechanisms. Only elephants have trunks, but all vertebrates have noses, made from roughly the same parts, with the same basic functionality. If trunks are made from noses, where do the capacities and inclinations that make human language and pedagogy possible come from? Unless we believe, as William Burroughs fancifully put it, that “language is a virus from outer space” (Burroughs 1987), we need to find the biological foundations of language in pre-existing systems on Earth. An obvious place to look is at the cognitive and communicative capabilities of our closest primate relatives. As it turns out, based on what we know about the mental capacities and communication behavior of existing nonhuman primates, including monkeys, chimpanzees, and the other great apes, and assuming these capacities and behaviors (most of which we share) did not evolve independently, as a case of convergent evolution, then we must suppose that these capacities and inclinations also existed in the last common ancestor of humans and chimpanzees, and provided the basic, off-the-shelf parts for the remarkable, species-unique signaling system that evolution built for us. Given the complexity of human language, and the myriad and intricately intertwined anatomical and neural components that underlie our capacity for language, any given list of key enabling traits will likely turn out to suffer from one or more important omissions. That said, it seems the following are among the most important biological precursors for human language in primates (see Hurford [2003] for a similar list): 1. An ability to parse the events and objects of the physical world into useful cognitive categories, e.g., to distinguish between predators and prey, edible and inedible plants (Kolodny et al. 2015), kin vs. nonkin, and dominants vs. subordinates (Dunbar 2016)—with at least

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one level of embedding, e.g., dominant non-kin vs. dominant kin (Seyfarth and Cheney 2008). At least a rudimentary capacity for mindreading (“theory of mind”; Premack and Woodruff 1978), evidenced by an ability and inclination to track the gaze of other animals and so determine at any moment what others are attending to—a source of food, another animal, oneself (Call and Tomasello 2008; Schaafsma et al. 2015). The ability to influence the behavior of conspecifics (members of the same species) through a variety of signals, including gestures, facial expressions, and vocalizations—necessarily combined with an ability to interpret such signals (Sterelny 2012). An ability to copy the gestures of others, combined with an ability to innovate—to invent new gestures for specific purposes, thus making gestural “dialects” possible (Arbib et al. 2008; Pollick and De Waal 2007; but see Hobaiter and Byrne 2011). A capacity and inclination for communicative (e.g., gestural) turn taking (Fröhlich et al. 2016; Levinson 2016). The ability to combine signals in a certain sequence, for a particular purpose (Tomasello et al. 1994). A latent capacity for symbolic reference, as evidenced by symbol learning in chimpanzees and bonobos (e.g., Savage-Rumbaugh and Lewin 1994).

Such an inventory, while certainly incomplete, is important for two reasons. First, because it begins to tell us what evolution already had to work with, the existence of these biological precursors removes at least some of the mystery surrounding what might otherwise seem a non-Darwinian “discontinuity” between human language and the communication systems of other primates (see Penn et al. 2008). Second, although the sequence of tiny steps and sudden leaps forward that it must have taken to build a full-blown human language (and a creature capable of using it), remains shrouded in mystery, at least we know that nature had a decent amount of time to get the job done. The great gulf that now so clearly separates human language from other primate communication systems had some 6–10 million years to widen—some 240,000–400,000 generations. In evolutionary terms,

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that’s surprisingly fast, but it was evidently time enough. You wouldn’t be reading this if it hadn’t been. Let’s take each of these likely precursors in turn.

Primate Cognition Like all animals, our primate ancestors would have had plenty of neural circuitry devoted to the problem of how to make sense of the world around them for their own benefit: how to organize the world into categories based on perceived differences and similarities; how to generalize from particular instances; how to distinguish between parts and wholes; how to estimate future behavior on the basis of observed past behavior; and so forth. For a foraging animal, it’s important to be able to predict, for example, that a tree with a certain kind of bark is likely to bear a certain kind of fruit at certain times of the year; that a plant of a certain height and leaf structure is likely to have a certain kind of edible root buried beneath it; or that a bee with a certain way of flying is more quick to attack than another. (On the relationship between foraging skill and language, see Kolodny et al. 2015.) As social animals, our primate ancestors would also have had an advanced degree of social intelligence. They would have been especially good at placing group mates into categories (kin vs. non-kin, dominants vs. subordinates; Dunbar 2016), and they would have been able to create subcategories, e.g., dominant non-kin vs. dominant kin (Seyfarth and Cheney 2008). They would not have had words for these categories (modern humans can have 50,000 or more), but the mental categories themselves were a start.

Mindreading (“Theory of Mind”) Although the subject of considerable debate, another likely biological precursor of human language in ancestor primates is at least a rudimentary version of the cognitive capacity known among cognitive scientists as theory of mind (ToM), the ability to attribute mental states—beliefs, intentions, desires, etc.—to oneself and others, and to understand that others have beliefs, desires, intentions, and perspectives that are different

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from one’s own. Whether or not chimpanzees have a “theory of mind” at all, the question raised in a seminal paper by psychologists David Premack and Guy Woodruff (1978) is still debated. However, more recent (and it seems, more reasonable) views understand mental perspective taking as not a single “module” which an animal either has or doesn’t, but an intricately connected network of neural circuitry (Schaafsma et al. 2015), including circuits involved in gaze processing, emotion processing, understanding of causality, prediction of future behavior based on current behavior, and so forth—all of which are present in some degree in the other great apes, and therefore were also likely to have been present in a common ancestor. The relationship between pointing as a communicative act, perspective taking, and the capacity for joint attentional activity has been a topic of special interest, notably in the work of Michael Tomasello and his collaborators. A starting point is the observation that the majority of captive chimpanzees (~60–70%) learn, without training, to recruit human assistance by pointing to food they can’t reach (in the presence of humans), or to locations they want help accessing, while alternating gaze between the location they are pointing at and their communication partner (Leavens and Hopkins 1998; Tomasello et al. 2007a). That this behavior has not been observed in wild populations is significant on two counts. First, it shows that while communicative pointing is not “natural” in the way it is for humans, chimps are cognitively capable of learning this behavior from humans. Second, when captive chimpanzees do point, they usually do so “imperatively” (equivalent to “Give me that!” or “Let me go there!”), almost never altruistically, as a way of providing useful information to another (“It’s over there…”), or of establishing the object or location as a topic of interest (“Look at that!”). Similarly, chimpanzees seem not to understand pointing as an act of benevolent communication when directed at them. When a human researcher points at the one of two buckets that contain food, the chimp typically fails to interpret the hint, apparently thinking, as Tomasello and his colleagues put it, “A bucket? So what? Now where’s the food?” Tomasello et al. (2007a: 718).

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Importantly, as evidenced by findings reported by the primatologist Michael Beran and colleagues at the University of Georgia, captive chimpanzees do seem to have some capacity to understand human pointing as a communicative behavior, and even to engage in it themselves (Beran et al. 2016). In one set of experiments with a chimp named Panzee, for example, a researcher hid different kinds of objects in different locations in the woods outside the chimp’s enclosure. Later, working with a second researcher who did not know either the location or the type of food hidden, Panzee pointed in the direction of a hidden object and then pressed a symbol on a keyboard representing the object. In another experiment using the same paradigm, the second researcher pointed in the direction of a hidden object and Panzee used the keyboard to identify the object. In short, the evidence suggests that chimpanzees have some latent but rudimentary cognitive potential to employ human-like indexical pointing, which is seldom if ever exploited in the wild, but emerges in captive chimps, especially under heavy training. In stark contrast, human infants begin to understand the communicative function of pointing, and start to point altruistically themselves (as when an adult researcher appears to have “lost” a toy dropped on the floor) spontaneously, at around 12–14 months. As the researchers explain, this latter function of pointing in humans requires that the participants not only share a joint attentional frame, but have, or can assume, a joint intentional purpose, the idea that “we” are doing something together (e.g., I am helping you in your goal of finding the toy you lost; Tomasello et al. 2007a).

Primate Vocalization and Gesture Like all social animals, all primates have evolved systems for communicating with one another in ways that benefit individual and kinshipgroup fitness. Signals include vocalizations (barks, grunts, screams), facial expressions, gestures, and combinations of these. Many of these signals, especially vocalizations, are, like most bird songs, largely instinctual, conforming to inherited templates, but are also partly learned. Few are completely automatic; rather, for good reasons, animals tend to

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have some degree of control over their signals and customize them in response to circumstances—though to what extent these controls are truly “voluntary” in particular species is open to debate. As a rule, and for important reasons, monkeys are more likely than apes to vocalize (Arbib et al. 2008). Many monkey species, including vervet monkeys, Diana monkeys, and Campbell’s monkeys, use specific alarm calls for different types of predators, each triggering an escape response appropriate to the threat. On hearing the alarm cry for an airborne predator, for example, a vervet monkey in the upper branches of a tree will look up and then scramble down the tree and into bushes. The alarm cry for a snake will cause the monkey to look down at the ground, and possibly scamper up a tree, but not so far up as it would on hearing the alarm cry for a leopard, which can climb into the lower branches (Cheney and Seyfarth 1992). Given that producing an alarm cry may attract dangerous attention to the caller, it is not surprising that callers are strategic. For example, female vervet monkeys are more likely to produce alarm cries in the presence of offspring, and males in the presence of females (Cheney and Seyfarth 1985). Adult Campbell’s monkeys produce at least six different call types, which they combine into various sequences in specific contexts including group travel, the presence of neighboring groups, and specific predator classes. Eagles trigger four different sequences, and leopards three, depending on how the caller learned about their presence (Ouattara et al. 2009). It’s worth noting, by the way, that humans also produce distress cries (e.g., crying, especially but not exclusively in infants and children), and alarm cries (adult screams in the face of danger). As in the case of monkey alarms, human cries are instinctual and universal across cultures. Screams have interesting acoustic properties, a kind of chaotic “roughness” that clearly distinguishes them from human speech sounds, which are more systematic, more predictable, and thus easier to ignore (Arnal et al. 2015). As discussed in more detail below, humans use other kinds of cries and calls that are likely of ancient origin, but have become conventionalized in different ways in different modern cultures. These include utterances known as expressives (sometimes called “interjections”) such as English “Whew!”, “Oops!”, “Oh!”, “Ah!”, and “Yay!” The point is

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that human vocal behavior includes a mix of signals, including signals of ancient and modern origin. An important feature of both monkey alarm cries and human screams is that they function to convey information to all within hearing, not to specific individuals. As such, they are especially effective in environments where individuals cannot see each other, such as in the leafy, arboreal habitat that monkeys still occupy. In contrast, gestures, as communicative acts, must be visible to the intended recipient and thus tend to be directed at individuals in close proximity. Gibbons, chimpanzees, and the other great apes all have considerable repertoires of gestures (typically around 20–30 in a given group) for a range of social purposes, including agonistic threats (suggesting a willingness to engage in physical violence), appeasement, sexual invitations, invitations to play, nursing requests, food requests, and grooming requests. Repertoires of captive animals tend to be greater than those observed in the wild, probably at least partly because some gestures (such as pointing) are copied from humans, and partly due to increased pressure to communicate in small, stable groups (Arbib et al. 2008). The use of communicative gesturing in our closest relatives is especially relevant. In a study of young captive chimpanzees aged 1–8 years, Tomasello and his collaborators reported the use of 25 different gestures, including ground slaps, head bobs, hand begs, pokes, arm raises, “wrist offers,” “back offers,” and “throwing stuff ” (Tomasello et al. 1994). A majority of the gestures were used to gain the attention of another, or to direct the other’s attention to some part of the animal’s own body, as when one chimp offers its back to another to be groomed. Interestingly, the researchers found that some of the same gestures were used for different purposes, and different gestures were used to accomplish the same purpose. For example, wrist offers (as when a young chimp extends the back of its wrist toward an adult) were used as both a form of appeasement and food request. Food requests were accomplished by both ground slaps (to gain attention) and hand begs. Notably, if one gesture did not accomplish the desired result, the animal might repeat it, or try another gesture—good evidence that the gestures were controlled and intentional. Generally, the observed gestures were idiosyncratic and ad hoc. In the case where gestures became established over time, the

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researchers concluded that pairs of animals were developing a shared repertoire by gradually conventionalizing their gestures with each other rather than imitating a more widespread convention, a process Tomasello has called ontogenetic ritualization (Tomasello 2014). It’s also noteworthy that both monkeys and apes combine signals in a way that one signal modifies the other. For example, chimpanzees and orangutans have both been observed to combine a hitting gesture (e.g., raised arm) with a “play face,” presumably in order to signal that an invitation to play-fight is not misinterpreted as actual aggression (Bekoff and Allen 1998). In the sense we’re using the term here, one gesture works to disambiguate the other. Finally, we may note that many primate signals can be understood as “proto speech acts,” with clear analogues in modern human language. By this, I mean that certain signals do not just convey “thoughts,” but more specifically constitute conventionalized social actions which, when directed at another individual, intentionally alter, in some significant way, the real-time social relationship between the two. These proto speech acts include warnings, greetings, requests (e.g., food and grooming requests), threats, and something like apologies and requests for forgiveness. For example, a female baboon who has recently attacked a lower-ranking female may later approach the victim and grunt softly to her, apparently as a way of making amends—a reconciliatory behavior which has been reported in some two dozen species of nonhuman primates (Silk 2002).

Mitteilungsbedürfnis The human infant’s tendency to share information with others, such as pointing to a toy the researcher has “lost” on the floor, is an example of what Fitch suggests we call Mitteilungsbedürfnis, a German word denoting “a drive or need to share thoughts and feelings,” i.e., “helpful chattiness” (Fitch 2010: 140). The use of alarm calls in other primates, including chimpanzees (Schel et al. 2013), might be taken as evidence that the last common ancestor of humans and chimpanzees had at

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least a rudimentary version of whatever genetic programming underlies this drive, though of course not nearly so pronounced as in modern humans, who, you may have noticed, often have some trouble containing their desire to share even more information than necessary with their interlocutors, especially when it’s about themselves.

Social Learning of Vocalizations and Gestures Although monkey alarm cries are largely instinctual, and thus perhaps not good models for intentional communication, there’s evidence that primate gestural communication is to some small degree biocultural, combining genetically-based capacities and predispositions with culturebased social learning and individual innovation. Examples: rhesus monkeys bred experimentally in isolation from others adopt gestures and postures that are typical of their species (Mason 1963); two lowland gorilla infants were observed performing “chest beats” (a standard dominance gesture) even though they had reportedly not yet seen the gesture performed by adults (Redshaw and Locke 1976); and juvenile chimpanzees raised apart from older peers nevertheless developed much the same repertoire of play gestures as did those in larger groups with more natural composition (Berdecio and Nash 1981). However, instinctual gestures are also clearly open to cultural shaping and social learning— gestural repertoires vary across groups of chimpanzees, and within any given group, some gestures are used by some individuals but not others.

Turn Taking A key feature of human language is the capacity for two-way dialogue, potentially, but not always, consisting of extremely rapid, back-and-forth exchanges of utterances. We may think of a conversation as a simple exchange of words, but this is a gross simplification. Real-time conversation is astonishingly complex, and complex at several levels. In the first place, speech is a continuous, raggedly-pulsing sequence of sound waves, which speakers manage to produce and decode into meaningful units at an average rate of about 10–15 phonemes per second (Levelt

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1993). Somehow, in the course of real-time conversation, your brain recognizes and segments incoming patterns of these sounds as words, guesses at the intended meanings of individual words using various clues—including immediately preceding words, word order, intonation, nonverbal components (gestures, facial expressions), and the larger pragmatic context (e.g., the topic and purpose of conversation). In this way, your brain quickly works out your interlocutor’s communicative intent—for example, whether the incoming utterance is an assertion, question, request, instruction, warning, etc., and, more particularly, what the utterance most likely attempts to have you think or do. Meanwhile, at the same time that your brain is processing the inputs, it’s also preparing a suitable response, given your own real-time desires and beliefs, and how you think these may mesh with interlocutor’s desires and beliefs. But now the process is the reverse—words must be chosen, organized as grammatical strings in accordance with rules of syntax, then packaged and produced as articulated sounds, involving extremely rapid and coordinated movements of lungs, throat, mouth, tongue, teeth, and lips. The fact that we can do all of this so easily, with at least the appearance of automaticity—you typically start your turn about .5 seconds before your partner finishes hers (Garrod and Pickering 2004)—is remarkable. Indeed, as I argued in Chapter 4, the very complexity and efficiency of the system argue for a long evolutionary history. Nonhuman primates are clearly incapable of anything like this, and, as we’ve seen, most researchers think it’s unlikely that human ancestors had developed much capacity for articulate speech prior to, say, 500,000 years ago. However, recent evidence suggests that both bonobo and chimp mothers engage in interactive, turn-taking gesturing sequences with their young, in a form that closely resembles the cooperative turn taking of human conversation (Fröhlich et al. 2016). Unless one thinks these capacities developed separately, it seems the neural circuitry that “makes conversation easy” (Garrod and Pickering 2004) in humans was likely built partly from systems that were available, in rudimentary form, to a common ancestor at least some 6 million years ago (Levinson 2016).

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Symbolic Reference Finally, the clearest apparent “discontinuity” between human brains and those of other primates, the most difficult to explain, and the hallmark of human language, is our species-unique system of symbolic reference, whereby arbitrary combinations of physical sounds or typed words (or, as in Braille, patterns of raised dots) can conjure up memories of past events, visions of future events, and any number of ideas, thoughts, and emotions in another person’s brain. While arbitrary sounds can appear to have certain specific “meanings” in the communication behaviors of other animals, it’s much less clear (and nearly impossible to ascertain) whether such signals are meaningful in the same way they are to humans. For example, when a vervet monkey in the upper branches of a tree hears an alarm cry prompted by another monkey’s sighting of an airborne predator, does the cry produce a visual image of a generic eagle in its own mind, and is it this mental image that frightens it and causes to scamper down to safety? Or is it rather that the signal acts more directly on its behavior, automatically triggering a specific fear response, without the need to first visualize the source of the fear, and, on that basis, decide how to react? Since we can’t ask the monkey, it seems the more parsimonious explanation is the best one, and the one that makes the most sense from an evolutionary standpoint. If a juvenile vervet monkey had to learn, on the basis of personal experience, the symbolic referent of the “Eagle!” alarm cry, it might not survive its first mistake. It’s a little hard to imagine an evolutionary advantage to having an intermediate system in the monkey’s brain that links an alarm cry to a specific visual image, and then a specific behavioral response to that image based on the emotions the image might conjure up. You’d think it would be faster, and safer, to link the sound directly to the response. However, although monkey alarm cries, for these reasons, may be unlikely precursors for our human capacity for symbolic reference, there are other reasons to think that primates use mental categories to interpret the “meaning” of specific vocalizations. For example, as reported by the primatologists Robert Seyfarth and Dorothy Cheney, baboons respond differentially to threat grunts and screams of other group members

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depending on both the relative rank of the individuals involved in the social hierarchy and whether the individuals belong to the same or different families, which is taken as evidence that they classify individuals simultaneously on two dimensions: kinship and rank (Seyfarth and Cheney 2014). Seyfarth and Cheney conclude from this that baboons have mental categories that allow them to treat calls as “narratives” with the conceptual components of actor, action, and object (individual who is acted on), and in this way parse the meaning of these calls in much the same way that humans interpret the meaning of sentences. It’s also notable that apes have been trained to use symbols in laboratory experiments and even, as claimed in the case of the young bonobo Kanzi, may acquire them simply by observing others being trained (SavageRumbaugh et al. 1986). As the title of a book about Kanzi suggests, apes like Kanzi may indeed be, in some sense, “at the brink of the human mind” (Savage-Rumbaugh and Lewin 1994).

Language-Ready Brains In summary, given the communicative behaviors of the nonhuman primates, we can observe today, both captive and in the wild, we may suspect that a number of important biological precursors for a fledgling protolanguage would have been present in a common primate ancestor at least as far back as some 20 million years ago, roughly the time of the split between monkey and ape lineages (Stevens et al. 2013). True, in response to changing circumstances in their own habitats, monkeys and apes in the world today have all evolved specialized adaptations over the same period of time, and so they provide imperfect models for a common ancestor. For example, we can’t know for certain whether the presence of predator-specific alarm calls in only some monkeys reflects a relatively recent adaptation in those who have it, or a discarded trait in those who don’t. In any case, even though we know the human lineage began evolving far more rapidly (for reasons we’ll soon discuss), than the lineages of our primate relatives did, the communicative behaviors and capacities of modern primates, being comparatively less evolved, should give us at

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least a rough sense of the communicative behaviors and capacities of our shared distant ancestors. It seems a good guess, for example, that our early ape ancestors employed a repertoire of more-or-less conventionalized vocalizations (calls and cries), facial expressions, and gestures which they used to build, maintain, and regulate their social relationships. They likely had signals for warning each other of danger; expressing emotions of fear and sexual enjoyment; greeting each other; drawing attention to themselves; requesting food, grooming, and sex; producing and responding to threats of violence from other group members; and later smoothing things over with conciliatory sounds and gestures. In these ways, they were able to build advantageous affiliations with other individuals in the group, understand the social dynamics of the group as a whole and their place within it, and in these ways contribute socially to group fitness. This was, and remains, our common primate inheritance. Grooming requests aside, it’s still a big part of who we are and how and why we communicate with each other. However, in spite of these useful capacities for social intelligence and communication, it seems safe to think that our early ancestors would have been limited in ways similar to those our existing great ape cousins are.2 They would likely have been capable of constructing mental categories such as kin vs. non-kin and dominant vs. subordinate, but they did not have names for these categories. They could easily distinguish between their mothers and aunts, but they couldn’t tell someone else which was which. Through gaze following, they would have been expert at judging what another individual was attending to, and were capable of drawing attention to themselves for their own purposes, but they did not yet have the inclination to intentionally draw the attention of another group member to some third thing for a communicative purpose. They could signal something like “Scratch my back…” or “Look at me…” but not “Scratch her back” or “Look over there.” They were probably capable of dyadic (one-on-one) communication, and something like turn taking, but they couldn’t engage in anything like the kind of civil discourse which modern humans use to exchange information and ideas, agree on goals and ways of accomplishing them, and construct new knowledge for themselves and others. They couldn’t use language to teach. Exactly what happened? How did we get from there to here, to this book you’re reading?

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Building Selection Pressure As we’ve already discussed, biological organisms do not inevitably evolve into new forms. We’re surrounded by the evidence. The earthworms I found squirming under the rocks I overturned in my yard yesterday afternoon haven’t changed much in over 300 million years. The cockroach I saw scurrying for cover along my kitchen counter last night, with infuriating effectiveness, has also been around, in more or less the same disgusting form, for some 300 million years. Houseflies are relative newcomers, just some 70 million years old (Wiegmann et al. 2003). The opossum I saw late one night walking carefully along the top of the wooden fence behind my house looks and behaves much as its ancestors did some 50 million years ago (Horovitz et al. 2009). In fact, it seems likely that most of the lifeforms in our environment have been around a lot longer, in their present packaging, than we have. And there’s a reason why it’s so hard to step on a cockroach or swat a fly, and, more generally, why pests remain pests. They’re magnificently good at what they do already, so there’s no pressure to change. But we know that at some point, no more than about 6 million years ago, our ancestors, now bipedal but still small-brained apes, began to evolve in a new direction, taking the first steps down the long and treacherous road that eventually led, against tremendous odds, to us. And along the way, pressure began to build, perhaps fairly quickly, for an enhanced system of social communication. The gestures, vocalizations, and underlying cognitive capacities that served and continue to serve our primate relatives so well had begun to impose limits on what our ancestors could accomplish in the new niche they’d begun to inhabit and shape for themselves. As we discussed in Chapter 3, the new habitat and corresponding adaptive suite involved a set of radical, interrelated changes in sexual arrangements, diet, foraging strategies, morphology, and life history. It seems that these adaptations allowed, and were prompted by, a risky shift from the relative protection of a largely arboreal life in the rainforest, with relatively easy access to a diet of fruits, insects, and green leaves, to a more dangerous but potentially more resource-rich life in the woodlands, expanding grasslands, and waterside regions of the African Rift

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Valley. This change in scenery apparently set up what was to become a fateful cascade of changes in the hominin adaptive suite, especially in the related traits of cognitive capacity, sociality, brain size, and diet. Simply put, you are what you’re smart enough to eat, and, in a social animal, you are what your group is collectively smart enough to eat. As we’ve also discussed, bipedalism increased the animal’s foraging range, giving access to a much wider and potentially richer variety of food sources, but the new food sources were patchier, harder to access, and more heavily defended than the fruits, young leaves, and insects the remaining forest-dwelling primates depend on. In such an environment, a group that had the collective cognitive capacity to fully exploit the new resources would have had a distinct advantage over groups that were limited by the cognitive capacities of individuals. In a relatively small and stable foraging territory, everyone knows where patches of food and other resources are—or, if any don’t, they can just follow along after those who do. If methods of extraction are simple enough to learn by observation and emulation alone, then individuals can just watch what others do and follow their example. But as foraging territories expand, as they would for an increasingly bipedal ape, there comes a point where it makes more sense for smaller groups to scout different parts of the range, then, on finding a new food source, to recruit others in the group to exploit the new bounty. Also, as the variety of food sources increases, as it would in an expanded range, methods of extraction become more various and complex, partly in proportion to the value of the food. As an example, honey, which is highly nutritious, is heavily defended by the bees who make it, and thus more difficult to extract and process (see Crittenden 2011 regarding the role of honey consumption in human evolution). Under these circumstances, it becomes increasingly important for individual innovations and hard-won technical knowledge and skill (such as how to avoid getting stung when collecting honey) to get shared with others, including members of upcoming generations. Crucially, these collaborative behaviors would have depended in some part on a cognitive capacity that nonhuman primates appear to lack—the ability and inclination to direct another animal’s attention to some third thing for a communicative purpose and to compose and produce a signal

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that could clarify that purpose. Groups of ancestors who could manage to do this would have been, at first, just slightly better at exploiting the new resources by (a) conveying relevant real-time information to each other (“Large animal over there…”); (b) coordinating group hunting and foraging activities (“You go this way, I go that way…”); (c) warning of danger in a particular direction (“Snake in that bush…”); (d) instructing others, such as in the manufacture and use of specialized foraging tools (“Strike there…”); and, just as crucially (e) in doing all of these things, demonstrating their special fitness as mates and parents. On the flip side, the absence of these capacities would have limited such a group’s ability to exploit the new resources, primarily because they would have been incapable of joint attentional activity. Like baboons, they could have successfully occupied a niche on the forest’s edge, banding together in large groups, leading a largely terrestrial life (though not straying far from the safety of trees), subsisting on an opportunistic, omnivorous diet of plants, small animals, fish, and other sources of protein. Like other primates, they would have been able to call out the location of a food source or dangerous predator from that location, but they would not be able to point to it, or tell someone how to get there. Like other primates, they would have been able to learn by watching others, but they would not have had any particular inclination to go out of their way to help another learn.

The Tipping Point Now, we know that at some unknown point deep in our history, something changed, and our ancestors somehow managed to escape what Bickerton (2014) has called the “prison of animal communication” and emerge into the world of human-like language. And although we’ll never know for sure, we may strongly suspect that a cultural tradition of indexical pointing as a communicative act was the key that unlocked the door. At the very least, there is good reason to think that this distinctive human gesture was an integral component of our unique system of communication from an early stage in human language evolution, if not from the very beginning.

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I’ve already mentioned a key piece of evidence. Human infants, alone among primates, begin using their fingers to point communicatively, and to interpret indexical pointing by others as communicative acts, very early on in their development—at around 12 months, just as they are beginning to speak, and just as they are becoming capable of joint attention (Tomasello et al. 2007a). Further, as Tomasello and his colleagues report, infants at this stage exploit the Swiss-Army-knife utility of this gesture by pointing for a variety of purposes. They point to make requests (e.g., pointing to a window the child wants opened, a cup he wants filled); to indicate a direction he wants to go in; to identify an object (pointing to a coat rack that had fallen and frightened him); to indicate a location in response to an adult’s question; to share emotion about a particular object; and so on. In some cases, but not always, the infant disambiguates the signal by pointing and speaking at the same time. For example, she points to the door and says “Papa” at about the time that her father is expected home. After his mother has told him not to touch her hot teacup, a child points to it and says “No,” looking to his mom for confirmation. After watching his father trim the Christmas tree, his grandfather enters the room and the child points to the tree and says “Oh!” (Tomasello et al. 2007a). Indexical pointing, in other words, gives the child on the brink of language an especially useful, arguably essential, means of engaging with others in the here and now for multiple purposes—for meeting her own needs, for sharing and testing her emerging understanding of the world around her, and for cooperating communicatively with others. Further, it seems she often and naturally feels the need to combine her points with bits of spoken language to unambiguously convey her meanings and intentions. She still has far to go, but an infant who has started to point is surely on “the royal road to language” (Butterworth 2003). Indeed, it’s a cause for concern if a child doesn’t point, or points only for limited purposes, such as to make requests but not to convey information, a characteristic of children on the autism spectrum (Baron-Cohen 1989). In cognitively typical children, on the other hand, pointing is almost certainly a cultural universal (Liszkowski et al. 2012), serving as what the psycholinguist Sotaro Kita has called a “foundational building block of human communication” (Kita 2003: 1). Language, after all, is

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essentially referential , a system in which different combinations of sounds refer (point) to things in the world, or more specifically, to things in the interlocutors’ shared mental representations of the world—the “common ground” (Clark et al. 1983). Whether the “discovery” of pointing in an early hominin group was what started our ancestors down the same road that human infants follow, it seems clear that pointing, combined with other disambiguating signals, would have supplied an important missing piece in an early protolanguage, propelling language forward in a fundamentally new direction. In fact, it’s hard to think of any other “small thing” that could have had such profound repercussions. And here’s the really important thing: Once a group of early human ancestors, for whatever reason, became accustomed to combining pointing with other signals— either vocal or gestural, or both—they would have been vastly better at engaging in joint attentional activities. And because joint attentional activities (crucially, cooperative foraging for difficult-to-acquire food sources) were becoming increasingly important to the survival of the group, individuals who were genetically even just a tiny bit better at using the new system would have been more successful at provisioning their young, more successful at attracting mates, and therefore more successful at propagating their own genes, and those of their kin. In this way, an emerging system of disambiguated pointing, an early protolanguage could have begun to shape the brains of its users for its own purposes. Initially a cultural tradition, disambiguated pointing as a new way of signaling would have become a new force in the world, driving biological evolution in the same way that other manufactured changes in a plant or animal’s habitat, such as beaver dams, have. And because of the complexity of nature, and particularly those parts of the natural world that would have attracted the interest of hungry bipedal primates—e.g., the different parts of plants, edible and inedible, the tracks of different kinds of animals, the fracture properties of different kinds of rock, the strength and flexibility of different kinds of wood—the need for increasingly precise means of disambiguation would have created pressure for increasingly complex forms of the emerging protolanguage and brains capable of using them. Over millions of years, the subsequent coevolution of brains, technology, and language has led to

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the astonishing complexity of modern human language, and the astonishing ability of human children to master one or more of these crazy languages, starting in the womb, and achieving near fluency by the age of about five.

A Spark Falls on a Patch of Dry Grass So, here’s where we are. We’ve established that key biological precursors for a human-like language were likely present in the common ancestor of monkeys and apes some 20 million years ago, and that the last common ancestor of chimpanzees and humans some 6–10 million years ago thus had a “language-ready” brain. We also know that the suite of adaptive traits that came to distinguish humans so clearly from other primates—including our bipedalism, pair bonding, cooperative parenting, group provisioning, expanding, energy-hungry brains, and corresponding reliance on high-quality, difficult-to-acquire food sources—would have generated growing selection pressure for an enhanced signaling system that could in some way support joint attentional activity, pooling of knowledge, and transfer of knowledge and skill. However, it seems clear that some exceedingly stubborn obstacle blocked the path toward a human-like protolanguage, likely the same obstacle, or set of obstacles, that continues to block the path for our languageless primate relatives. At the same time, we know that a strategy of disambiguated pointing emerges, apparently instinctually, in modern human infants, just as they cross the threshold of the door to language, for which they also have an instinct. Disambiguated pointing is therefore a plausible candidate for the “small thing” that our ancestors found themselves using to bridge the gap that no other animal before or since has managed to cross. But how exactly could this have happened? It seems there are three possibilities. One is that natural variation in the “theory of mind” circuitry in a population of individuals, combined with selection pressure that favored individuals with slightly more advanced mindreading circuitry, began to reduce the size of the “blind spot” in these animals’ social vision, making them just a bit better at reading the minds of others

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in their group. This tiny increment in social intelligence would have given these individuals a rudimentary “third eye,” an immediate advantage over their less perceptive group mates, the same advantage their offspring would enjoy, assuming the genes got inherited—as, under this scenario, was increasingly likely. As the genes for the modified circuitry spread through the gene pool, new generations of mind readers could have used this newly enhanced capacity for understanding others as autonomous mental agents and taken the next momentous step. Building on existing capacities for social communication, including existing repertoires of gestures and vocalizations, these animals now sought to alter the contents of the minds they were now better at reading. Among other behaviors, they could have begun to point at objects not just for their own benefit (the kind of imperative pointing that many captive, humanenculturated chimpanzees engage in), but to convey useful information to others, and, more generally, to help organize cooperative behavior (“You go that way. I’ll stay here.”). We can call this a “biology first” scenario. A second possibility is that disambiguated pointing began not as a genetic change, but as a cultural innovation in a group of languageready primates. We already know that certain gestures and vocalizations are open to cultural shaping in groups of present-day primates, and that these “dialects” can get passed down through generations. We also know that chimpanzees and other apes work hard to communicate their desires to others, sometimes by combining signals, such as by stamping on the ground to get another’s attention, then making one or another food-begging gesture. Disambiguated pointing to objects in the shared attentional frame could have arisen as an innovative behavior in a small subset of group members, and at some point started getting copied by others, to the extent that it became added to the group’s repertoire of signals. Over time, and owing to selection pressure for a means of supporting joint attentional activity in the new niche, individuals who, as part of their genetic inheritance, were better at using this new system of disambiguated pointing held a fitness advantage over those who didn’t, and in this way, over time, culture began to shape biology, the blind spot began to clear, and all that followed, followed.

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As you have likely realized, a third possibility is that, against all odds, both sequences of events took place more or less simultaneously, such that there was no single first cause. The fire didn’t start because a certain patch of grass was dry, nor because a spark fell on the dry patch, but because the grass was dry and the spark fell just there. The chances of this happening at all were remote, depending on an unlikely combination of necessary conditions and accidents. The emergence of language in our ancestors was, in this account, a highly unlikely biocultural accident that might never have happened, but did.

Runaway Change? Recall that the final factor we need to explain in any given tipping point scenario is how the changes triggered by the small tipping point event lead to massive changes in the larger system. Of course, part of the answer is that the existing pressure for change is already present—the tipping point event in some way allows the existing pressure for change to exert itself. In this case, pressure for an enhanced system of communication capable of supporting joint attentional activity would strongly favor the evolution of such a system once it took hold. But, at least in this case, that’s only part of the answer. It would not have been like an avalanche which, once triggered, gets carried along by gravity and its own mass. It would have been more like a snowball that begins to roll down the hill, getting bigger and heavier as it goes, if the snow is sticky enough. For the analogy to hold, we need to explain the “stickiness” of disambiguated pointing, how it was that this particular method of signaling, if this was indeed the triggering factor, proved to be such a runaway success, at least at first, as a result of its own nature. It seems that at least part of the answer has to do, curiously, with the ambiguity of pointing. As we’ve discussed, pointing alone has little value because it can mean so many different things, for example: 1. Give me that. 2. Pick that up. 3. Look at that.

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What’s that? Food there. We go there. You go there. I go there. Danger there. etc.

Generally, a single gesture has little value unless it always means pretty much the same thing (as a raised middle finger does in some Western cultures), or it has more than one meaning and there is some way of disambiguating these possible meanings, either by accompanying the gesture with additional gestures and/or vocalizations, by having a way of requesting this information, or both. The first solution doesn’t lead to much of anything, which may be part of the reason why other primates don’t use pointing communicatively in the wild. But the second solution, if it can be implemented, turns the ambiguous nature of the gesture into a virtue. The gesture itself becomes a kind of communicative frame. The meaning is provided partly by the thing that is pointed to (or the specific direction of pointing), and partly by the disambiguating component of the bundled signal. Individually, these components have no particular value; in combination, because there are so many possible objects of potential interest in the joint attentional frame, and so many different directions, and so many possible reasons to point, this simple communicative mechanism—disambiguated pointing—opens up a nearly infinite number of possible meanings (see Tomasello et al. 2007a: 705). That’s what makes it sticky.

A Return to Equilibrium The last thing we need to explain about disambiguated pointing as a tipping point for language evolution is why, as useful as it was, it wasn’t followed by unbridled, runaway change. As we’ve already discussed, there’s good reason to believe that a human-like protolanguage had begun to emerge quite early on in hominin evolution, possibly as early as 3.5

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million years ago, at around the time that the first evidence of stone tool manufacture begins to appear in the archaeological record. Whenever it emerged, this new capacity for social communication, built from existing biological precursors, would have given its users a tremendous advantage in their struggle for survival in the dangerous but potentially bountiful new niche they’d stumbled into. But, as we’ve also discussed, it seems unlikely that a full-blown modern human language—in which a finite set of sounds, combined in accordance with a finite but bafflingly complex set of rules, can be used to convey a theoretically infinite number of meanings and accomplish an infinite number of purposes—began to emerge much before about 500,000 years ago (around the time of H. heidelbergensis, and a bit more likely around 200,000–300,000, coincident with the emergence of our own species. As we’ve seen, this is consistent with the archaeological record, which suggests that human groups pursued roughly the same hunter-gatherer lifestyle throughout the Pleistocene, with only small and gradual changes in anatomy and tool manufacture. Why then, given the obvious advantages of the new method of communication and associated cognitive package, and given also the potentially powerful pressures that would seem to have been unleashed by the coevolution of brains, language, and culture—why did it still take another three million years or so for chainsaws to appear? If language is so sticky, why didn’t the snowball just keep rolling, getting bigger and bigger as it rolled down the hill? It seems there are at least two explanations for this. One is that archaic protolanguages, by facilitating group cooperation and the ability to pass along hard-earned cultural knowledge and skill from experts to novices, were indeed sufficiently powerful to support a new way of being in the world—an intentionally cooperative social structure organized around exploitation of high-value, difficult-to-extract food resources through group collaboration, and transfer of expertise from one generation to the next. But owing to internal cognitive limitations, quite likely because they lacked complex syntax and the full set of phonemes found in modern languages, these protolanguages were strictly limited in their representational power, placing a ceiling on what could be accomplished with the new cognitive tools. In other words, the cultural transmission

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mechanism could now help prevent backward slippage of expertise from one generation to the next, but was not sufficiently powerful to fuel large-scale innovations among experts within generations, as has been the case during the last, say, 100,000 or 10,000, or (most dramatically) 100 years. Perhaps more importantly, once the breakthrough into the new niche had occurred, language didn’t need to get much more sophisticated than it already was. Potentially useful mutations and other genetic changes would have continued to emerge at the usual rate in hominin populations, leading to very gradual changes over time, including larger brain size, better control over vocalization, and other supports. However, whatever radical upgrades might have occurred in the cognitive architecture of individual brains (e.g., such as might support recursive syntax), these might not yet have conferred any particular advantage and were thus ignored and lost. It wasn’t until after another fortuitous confluence of genetic and environmental events toward the end of the Pleistocene that the hockey stick again thrust upwards, in response to some other tipping point events.

Chapter Summary In summary, here’s the basic argument for disambiguated pointing as a possible tipping point for the evolution of language, and teaching through language. 1. There’s good evidence that the last common ancestor of humans and chimpanzees had most, if not all, the basic prerequisites for a humanlike protolanguage, including a capacity and inclination to influence the behavior of other members of the group using a combination of physical gestures and vocalizations—and a concomitant capacity to “read” the desires and intentions of others through these displays. 2. As discussed in Chapters 2 and 3, the hominin adaptive suite that began to evolve after the split between the human and chimpanzee lineages some 6–10 million years ago—eventually characterized by a unique combination of bipedalism, pair bonding, cooperative

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breeding, cooperative foraging and provisioning, enhanced capacity for perspective taking, and risky reliance on high-quality, difficult-toacquire food sources—would have created selection pressure for an enhanced system of communication to support these joint attentional activities. Modern chimpanzees and bonobos almost have the ability to use indexical pointing as a means of communication, but a couple of things seem to be missing. For one thing, admittedly a small thing, it’s anatomically difficult for chimpanzees to extend their index finger; when they do point, they do so with flat hand and extending their whole arm (Povinelli et al. 2003). More importantly, although captive chimpanzees point “imperatively” at food items they want to be given (i.e., as a form of begging, presumably having learned the gesture from humans), they seem not to point “declaratively,” to convey information to others, or at least not without extensive training. Wild chimpanzees seldom if ever point, for any reason. Similarly, chimpanzees do not naturally interpret human pointing gestures as communicative acts. For example, given two boxes, only one containing food, chimpanzees do not interpret a human’s pointing at one of the boxes as a clue to the location of the food— unless trained to do so. In contrast, human infants typically begin pointing communicatively at around the age of 9–12 months, often (at least half the time) in combination with vocalizations. Finally, communicative pointing is almost certainly a human cultural universal. It’s true that pointing can take on different forms in different cultures; for example, in some cultures, people also learn to point with their noses, eyes, or lips. However, indexical pointing (with the index finger) is common across cultures, including traditional small-scale cultures.

Putting these pieces of evidence together, it’s not unreasonable to think that disambiguated pointing played a central role in the evolution of human language (initially, protolanguage) and the use of language for teaching.

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Food for Thought 1. What are the critical components of a tipping-point episode? 2. Which of the following, in your opinion, qualify as tipping point episodes: a. b. c. d. e.

the sudden emergence of the modern internet a mass extinction brought about by a comet strike the formation of a hurricane the sudden popularity of the Beatles the invention of the printing press?

3. What is the relationship between mindreading and altruism? 4. What does pointing in human children tell us, if anything, about the emergence of language in humans? 5. Which do you imagine came first: the biological capacity for language, or the culture use of language?

Notes 1. But maybe not the most demonstrative means of directing attention. Operating a motorboat on the coast of Maine one summer not long ago, my 4-year-old grandson sitting in my lap, I found him directing my attention to a navigational buoy off the stern by pushing at my chin with his little hand, thus forcing me to look in that direction. 2. To be clear, in saying that other primates are “limited,” I don’t mean to imply a deficiency. All organisms are limited by the extent of their adaptations to their chosen habitats. In this sense, a “limit” is an important flip side to an “adaptation.”

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Suggested Reading Arbib, M. A., Liebal, K., & Pika, S. (2008). Primate vocalization, gesture, and the evolution of human language. Current Anthropology, 49 (6), 1053–1076. A useful review of precursor traits for language in primates. Laland, K. N. (2017). The origins of language in teaching. Psychonomic Bulletin & Review, 24 (1), 225–231. An important paper arguing, as the title suggests, that language originated specifically under selection pressure for teaching—a component of my argument that language arose under pressure for an enhanced signaling system in support of collaborative foraging, socialization (notably pair bonding), and teaching. The paper also employs several of the “sniff tests” for language origin theories I discuss near the end of the present chapter. For a more complete discussion, see Laland, K. N. (2018). Darwin’s unfinished symphony: How culture made the human mind . Princeton University Press. Morrison, D. M. (in press). Disambiguated indexical pointing as a tipping point for the explosive emergence of language among human ancestors. Biological Theory. A paper of mine, hopefully available by the time you read this, that provides a more detailed (and technical) version of the argument I make in this chapter. Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences, 1(4), 515–526. The paper that first introduced the “theory of mind” concept. Note that the question in the title implies that you either have one or don’t have. See below. Schaafsma, S. M., Pfaff, D. W., Spunt, R. P., & Adolphs, R. (2015). Deconstructing and reconstructing theory of mind. Trends in Cognitive Sciences, 19 (2), 65–72. Makes an argument that “theory of mind” should be understood as supported by several different sets of neural circuitry, which may be developed to different degrees in different species. Tomasello, M., Carpenter, M., & Liszkowski, U. (2007). A new look at infant pointing. Child Development, 78(3), 705–722.

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An important discussion regarding the development of indexical pointing in human infants.

References Arbib, M. A., Liebal, K., Pika, S., Corballis, M. C., Knight, C., Leavens, D. A., et al. (2008). Primate vocalization, gesture, and the evolution of human language. Current Anthropology, 49 (6), 1053–1076. Arnal, L. H., Flinker, A., Kleinschmidt, A., Giraud, A. L., & Poeppel, D. (2015). Human screams occupy a privileged niche in the communication soundscape. Current Biology, 25 (15), 2051–2056. Baron-Cohen, S. (1989). Perceptual role taking and protodeclarative pointing in autism. British Journal of Developmental Psychology, 7 (2), 113–127. Bekoff, M., & Allen, C. (1998). Intentional communication and social play: How and why animals negotiate and agree to play. In M. Bekoff & J. A. Byers (Eds.), Animal play: Evolutionary, comparative, and ecological perspectives (pp. 97–114). Cambridge: Cambridge University Press. Beran, M. J., Menzel, C. R., Parrish, A. E., Perdue, B. M., Sayers, K., Smith, J. D., & Washburn, D. A. (2016). Primate cognition: Attention, episodic memory, prospective memory, self-control, and metacognition as examples of cognitive control in nonhuman primates. Wiley Interdisciplinary Reviews: Cognitive Science, 7 (5), 294–316. Berdecio, S., & Nash, L. T. (1981). Chimpanzee visual communication: Facial, gestural, and postural expressive movement in young, captive chimpanzees (Pan troglodytes) (No. 26). Arizona State University. Bickerton, D. (2009). Adam’s tongue: How humans made language, how language made humans. New York: Macmillan. Bickerton, D. (2014). More than nature needs. Harvard University Press. Burroughs, W. S. (1987). The ticket that exploded. 1962. New York: Grove Weidenfeld. Butterworth, G. (2003). Pointing is the royal road to language for babies. In S. Kita (Ed.), Pointing: Where language, culture, and cognition meet (pp. 9–33). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Call, J., & Tomasello, M. (2008). Does the chimpanzee have a theory of mind? 30 years later. Trends in Cognitive Sciences, 12(5), 187–192. Cheney, D. L., & Seyfarth, R. M. (1985). Vervet monkey alarm calls: Manipulation through shared information? Behaviour, 94 (1), 150–166.

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Cheney, D. L., & Seyfarth, R. M. (1992). How monkeys see the world: Inside the mind of another species. Chicago: University of Chicago Press. Chomsky, N. (2010). Some simple evo devo theses: How true might they be for language. In Richard K. Larson, Viviane Déprez, & Hiroko Yamakido (Eds.), The evolution of human language (pp. 58–59). Cambridge: Cambridge University Press. Clark, H. H., Schreuder, R., & Buttrick, S. (1983). Common ground at the understanding of demonstrative reference. Journal of Verbal Learning and Verbal Behavior, 22(2), 245–258. Crittenden, A. N. (2011). The importance of honey consumption in human evolution. Food and Foodways, 19 (4), 257–273. Curtiss, S. (2014). Genie: A psycholinguistic study of a modern-day wild child . New York: Academic Press. Dunbar, R. (2016). Human evolution. Oxford: Oxford University Press. Fitch, W. T. (2010). The evolution of language. Cambridge University Press. Fröhlich, M., Kuchenbuch, P., Müller, G., Fruth, B., Furuichi, T., Wittig, R. M., & Pika, S. (2016). Unpeeling the layers of language: Bonobos and chimpanzees engage in cooperative turn-taking sequences. Scientific Reports, 6, 25887. Garrod, S., & Pickering, M. J. (2004). Why is conversation so easy? Trends in Cognitive Sciences, 8(1), 8–11. Gladwell, M. (2006). The tipping point: How little things can make a big difference. Little, Brown: New York. Gould, S. J., & Eldredge, N. (1977). Punctuated equilibria: The tempo and mode of evolution reconsidered. Paleobiology, 3(2), 115–151. Hauser, M. D., Yang, C., Berwick, R. C., Tattersall, I., Ryan, M. J., Watumull, J., ... & Lewontin, R. C. (2014). The mystery of language evolution. Frontiers in Psychology, 5 (401), 1. Hobaiter, C., & Byrne, R. W. (2011). The gestural repertoire of the wild chimpanzee. Animal Cognition, 14 (5), 745–767. Hockett, C. F. (1960). The origin of speech. Scientific American, 203, 88–111. Horovitz, I., Martin, T., Bloch, J., Ladevèze, S., Kurz, C., & Sánchez-Villagra, M. R. (2009). Cranial anatomy of the earliest marsupials and the origin of opossums. PLoS One, 4 (12), e8278. Hurford, J. R. (2003). The language mosaic and its evolution. Studies in the Evolution of Language, 3, 38–57. Jacob, F. (1977). Evolution and tinkering. Science, 196 (4295), 1161–1166. Kita, S. (Ed.). (2003). Pointing: Where language, culture, and cognition meet. New York and London: Psychology Press.

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Kolodny, O., Edelman, S., & Lotem, A. (2015, July). Evolution of protolinguistic abilities as a by-product of learning to forage in structured environments. Proceedings of the Royal Society B, 282(1811), 20150353. Leavens, D. A., & Hopkins, W. D. (1998). Intentional communication by chimpanzees: A cross-sectional study of the use of referential gestures. Developmental Psychology, 34 (5), 813. Levelt, W. J. (1993). Speaking: From intention to articulation (Vol. 1). Boston: MIT press. Levinson, S. C. (2016). Turn-taking in human communication—Origins and implications for language processing. Trends in Cognitive Sciences, 20 (1), 6– 14. Liszkowski, U., Brown, P., Callaghan, T., Takada, A., & De Vos, C. (2012). A prelinguistic gestural universal of human communication. Cognitive Science, 36 (4), 698–713. Mason, W. A. (1963). Social development of rhesus monkeys with restricted social experience. Perceptual and Motor Skills, 16 (1), 263–270. Morgan, T. J. H., Uomini, N. T., Rendell, L. E., Chouinard-Thuly, L., Street, S. E., Lewis, H. M., et al. (2015). Experimental evidence for the co-evolution of hominin tool-making teaching and language. Nature Communications, 6 . https://doi.org/10.1038/ncomms7029. Ouattara, K., Lemasson, A., & Zuberbühler, K. (2009). Campbell’s monkeys concatenate vocalizations into context-specific call sequences. Proceedings of the National Academy of Sciences, 106 (51), 22026–22031. Pack, A. A., & Herman, L. M. (2006). Dolphin social cognition and joint attention: Our current understanding. Aquatic Mammals, 32(4), 443. Penn, D. C., Holyoak, K. J., & Povinelli, D. J. (2008). Darwin’s mistake: Explaining the discontinuity between human and nonhuman minds. Behavioral and Brain Sciences, 31(2), 109–130. Pollick, A. S., & De Waal, F. B. (2007). Ape gestures and language evolution. Proceedings of the National Academy of Sciences, 104 (19), 8184–8189. Povinelli, D. J., Bering, J. M., & Giambrone, S. (2003). Chimpanzee ‘pointing’: Another error of the argument by analogy. In S. Kita (Ed.), Pointing: Where language, culture, and cognition meet (pp. 35–68). Hillsdale, NJ: Erlbaum Associates Publishers. Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences, 1(4), 515–526. Redshaw, M., & Locke, K. (1976). The development of play and social behaviour in two lowland gorilla infants. Dodo Journal of Jersey Wildlife Preservation Trust, 13, 71–86.

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Savage-Rumbaugh, E. S., & Lewin, R. (1994). Kanzi: The ape at the brink of the human mind . New York: Wiley. Savage-Rumbaugh, S., McDonald, K., Sevcik, R. A., Hopkins, W. D., & Rubert, E. (1986). Spontaneous symbol acquisition and communicative use by pygmy chimpanzees (Pan paniscus). Journal of Experimental Psychology: General, 115 (3), 211. Schaafsma, S. M., Pfaff, D. W., Spunt, R. P., & Adolphs, R. (2015). Deconstructing and reconstructing theory of mind. Trends in Cognitive Sciences, 19 (2), 65–72. Schel, A. M., Townsend, S. W., Machanda, Z., Zuberbühler, K., & Slocombe, K. E. (2013). Chimpanzee alarm call production meets key criteria for intentionality. PLoS One, 8(10), e76674. Seyfarth, R. M., & Cheney, D. L. (2008). Primate social knowledge and the origins of language. Mind & Society, 7 (1), 129–142. Seyfarth, R. M., & Cheney, D. L. (2014). The evolution of language from social cognition. Current Opinion in Neurobiology, 28, 5–9. Silk, J. B. (2002). The form and function of reconciliation in primates. Annual Review of Anthropology, 31(1), 21–44. Stearns, B. P., & Stearns, S. C. (2000). Watching, from the edge of extinction. New Haven: Yale University Press. Sterelny, K. (2012). Language, gesture, skill: The co-evolutionary foundations of language. Philosophical Transactions of the Royal Society B, 367 (1599), 2141–2151. Stevens, N. J., Seiffert, E. R., O’Connor, P. M., Roberts, E. M., Schmitz, M. D., Krause, C., et al. (2013). Palaeontological evidence for an Oligocene divergence between Old World monkeys and apes. Nature, 497 (7451), 611–614. Tomasello, M. (2014). A natural history of human thinking. Cambridge: Harvard University Press. Tomasello, M., Call, J., Nagell, K., Olguin, R., & Carpenter, M. (1994). The learning and use of gestural signals by young chimpanzees: A transgenerational study. Primates, 35 (2), 137–154. Tomasello, M., Hare, B., & Agnetta, B. (1999). Chimpanzees, Pan troglodytes, follow gaze direction geometrically. Animal Behaviour, 58, 769–777. Tomasello, M., Carpenter, M., & Liszkowski, U. (2007a). A new look at infant pointing. Child Development, 78(3), 705–722. Tomasello, M., Hare, B., Lehmann, H., & Call, J. (2007b). Reliance on head versus eyes in the gaze following of great apes and human infants: The cooperative eye hypothesis. Journal of Human Evolution, 52(3), 314–320.

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Wiegmann, B. M., Yeates, D. K., Thorne, J. L., & Kishino, H. (2003). Time flies, a new molecular time-scale for brachyceran fly evolution without a clock. Systematic Biology, 52(6), 745–756.

7 Teaching from Childhood to Adulthood

Let’s review. We understand that teaching—when defined broadly as an aid to social learning, in which one individual (the “expert”) goes out of its way to help another (the “novice”) acquire some important skill or bit of knowledge—is widespread in the animal world. Ants, bees, chickens, raptors, whales, cheetahs, meerkats, dolphins, domestic cats, and nonhuman primates, among other animals, all seem to demonstrate, in their own ways, an instinct for something like teaching. Tandemrunning ants slow their run toward a known food source so naive nest mates can keep up and don’t have to waste effort finding their own way. Honey bees perform an intricate “waggle dance” as a means of communicating the direction and distance to a flower patch for potential recruits willing to pay attention. In the presence of their chicks, mother hens peck and cluck more vigorously than usual at high-quality morsels of food. Passing up opportunities for meals of their own, domestic cats, cheetahs, and other felines present their offspring with injured prey, so the youngsters can more easily practice their developing hunting and killing skills. Nonhuman primate mothers tolerate their offspring’s first clumsy attempts at foraging, even if it interferes with their own efforts. © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_7

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But why do only some animals engage in something like teaching? And why don’t those that do teach devote as much effort to helping young ones learn as we do? Why can’t a chimp be more like us? From the perspective of evolutionary biology, if an instinct for teaching emerges at all in an organism, it does so only when the survival benefits to the teacher, or the teacher’s kinship group (in social insects, the hive), outweigh the teacher’s own cost in time and effort. It follows that teaching strategies will be no more sophisticated than necessary. If it’s enough for a chimpanzee mother to simply tolerate her daughter’s awkward early experiments with the mechanics of termite fishing, then more active, time-consuming acts of intervention are unnecessary. If, hypothetically, the daughter’s survival happened to depend in sufficient degree on her termite fishing skills, and if termite fishing and other such foraging strategies happened to be too difficult for offspring to learn through simple observation and trial-and-error experimentation, then nature would presumably have tried to give chimps at least a slightly more sophisticated capacity for teaching—perhaps the ability and inclination to point at a crooked stick and make an ugly face. At any rate, without the ability to teach critical survival skills, these hypothetical chimpanzees would not have survived as a species. But as we’ve learned, real young chimpanzees are perfectly capable of learning termite fishing, nut cracking, and other such skills on their own, simply by observing their mothers at work, and perhaps with a little help from an older sibling. And even if they don’t acquire these culturallytransmitted foraging techniques (and not all do, nor to the same level of proficiency), sufficient nourishment is available in the form of fruit, leaves, and more accessible insects such as grubs. For at least these two reasons, chimpanzees, like most nonhuman animals, haven’t needed to evolve the capacity for intentional instruction. We have. As discussed in earlier chapters, it’s clear that at some unknown point in our history human ancestors started coming under pressure to evolve the capacity and inclination to put a little more effort into helping their offspring become proficient foragers and, more generally, to evolve a more sophisticated signaling system. A reasonable guess is that the pressure began to build early on, when our human ancestors, newly bipedal, had begun to venture out (or been pushed out) of

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the retreating rainforests into the expanding African woodlands, waterside areas, and open savanna—a habitat full of new opportunities and dangers. River systems and lakes provided access to a rich diet of fish, turtles, and crocodiles (Braun et al. 2010). Grasslands were strewn, here and there, with the fresh carcasses of antelope and other herd animals. Through cooperative efforts, the carcasses could be snatched from the original killers and other scavengers (so-called power scavenging) and dragged back to camp—a risky but lucrative business (DomínguezRodrigo and Pickering 2003). The sun-drenched grasslands also offered numerous plants with storage tubers, which could be used as fallback foods in times of scarcity (Laden and Wrangham 2005). As a result of early, marginal successes in this new habitat, it seems these ancestors gradually became habituated to the new lifestyle, leading to additional adaptations. The diet of high-quality food sources allowed cognitivelytaxing new social arrangements (e.g., pair bonding, cooperative breeding) and required clever new extraction techniques (such as the manufacture of primitive stone knives) which became increasingly difficult to learn by observation alone. As a result of these new demands, and in concert with other adaptations, it seems our ancestors were pressured into evolving (and, owing to a combination of unlikely genetic accidents and opportunities, were successful in evolving) what was to become by far the most sophisticated system for teaching and learning in the animal kingdom—human language. Although the timing is a matter of conjecture, we’ve considered reasons to believe that a rudimentary form of language, a protolanguage, may have emerged as early as 2–3 million years ago, possibly earlier. Importantly, the new system of communication was not, as is sometimes suggested, primarily a way of transmitting information and sharing ideas and thoughts (Pinker 2003). Rather, just like its predecessor primate communication systems, and indeed like all other animal communication, the protolanguage would have been fundamentally a way of influencing the behavior of others—and of getting things done. The chief difference, a huge difference, was that instead of vocalizing and gesturing for purely selfish reasons (such as begging for food, sex, or grooming), or expressing emotions (as in the alarm cries of birds and

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other primates) the new system had become a support for joint activity, a way of getting things done with others, for the common good. As we’ve discussed, the new system probably didn’t emerge “for” any single social purpose (not just “for” collaborative hunting and gathering, or teaching, or courtship, or bartering). Rather, it could be applied in the context of these and any other activity in which it was useful to direct the attention of others, through gesturing, to particular things (people, plants, animals, etc.) or directions in the physical here and now—and, having directed another person’s attention in this way, clarifying the purpose of the gesture with some other signal or combination of signals (“[finger point] You go there.”). Because the new signaling system was inherently multifunctional and open-ended, it was open to expansion through creative cultural innovation. Over time, this feature of human language—its inherent ability to encode nearly limitless meanings—apparently led to a process of nearly runaway complexification. The catalyst was nature itself. Over billions of years, life on Earth had become (and remains) nearly limitless in its complexity, a bewildering mix of flora and fauna, treasures and dangers, parts and wholes, causes and effects. The ability to begin naming these many different things, and to direct the activities of others with increasing accuracy and specificity, gave these human ancestors a new and unique power. As a result, once established, the new system came under pressure to further complexify, disambiguate, and expand— though within efficiency constraints such as a limited capacity for vocal learning, mind reading, short- and long-term memory, and available methods of disambiguation. Over millions of years, forced to ensure that its users could handle the growing complexity of the new system, language found ways, in fits and starts, to ease these constraints, or work around them, with the result that our brains and other crucial aspects of our capacity for language became, through an unplanned, jerky process of awkward tinkering and jury rigging, perfectly suited to its use. From early on, this new adaptation, the instinct and capacity for language, must have become an integral part of the full suite of traits which, in dynamic combination, led, over the next two or three million years, to the widening gap between the human adaptive suite and the solutions devised by other primates. The newfound ability to engage

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in joint attentional activity, with support from the protolanguage, gave these hominin ancestors what was to become a tremendous advantage, particularly in respect to the basics of animal life on earth—foraging for food, avoidance of predators, mate selection, and child-rearing. By directing each other’s attention to objects of mutual interest (a monkey in a tree, a trail of bubbles emerging from the mud at the bottom of a pond, a set of fresh animal tracks and scat, the location of a nearby antelope carcass or snake, a distant patch of plants with underground storage organs), hungry foragers would have been able to share information with each other, and direct each other’s actions, for the mutual benefit and safety of the group, in a way that had not previously been possible. Further, by intentionally demonstrating difficult-to-learn techniques for tool manufacture and resource extraction, and by directing attention to critical steps and components of these processes, experts could now pass along crucial, hard-earned knowledge and skill to novices, thereby constructing a reliable pipeline of expertise from one generation to the next. And by demonstrating their superior ability to communicate in these ways, males and females alike were demonstrating their fitness as potential providers and parents. As the psychologist Geoffrey Miller has put it, “Language puts minds on public display, where sexual choice could see them clearly for the first time in evolutionary history” (Miller 2011: 356–357). As the protolanguage emerged and developed, ramifications must have cascaded throughout our ancestor’s adaptive suite in dynamic, multi-causal fashion. The cognitive demands associated with life in the new niche, including the demands of acquiring and deploying the protolanguage itself, would have created selection pressure for increasingly large, increasingly energy-hungry brains. Access to nutrient-rich resources, made possible by cooperative foraging and cultural transmission of know-how associated with increasingly sophisticated extractive technologies, helped feed these energy-hungry brains. The size of the birth canal, already narrowed by changes in the pelvic girdle associated with the animal’s bipedal gait, placed a hard limit on brain size at birth, but this limit was overcome by a prolonged period of postnatal brain growth, leading to a lengthening childhood and period of parental

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dependency. This was in turn made possible by the increased lifespan, cooperative parenting, family provisioning, and food sharing.

Teaching: Biology or Culture? As you may recall from our opening discussion in Chapter 2, and subsequent discussion at the end of Chapter 4, an important question is whether teaching in humans is best understood as a primarily biological behavior, a kind of animal instinct similar, say, to the instinct that drives mother cats to provide their young with disabled prey—or a chiefly cultural practice, something that is learned through participation in the life of the group, through copying, instruction, and other forms of social learning. Although (as we’ve discussed) there’s an obvious answer, the question is worth revisiting, not least because it calls attention to our own individual capacities and responsibilities as teachers. If teaching is primarily cultural, then we might expect to find that teaching practices vary widely from one culture to another, and from one individual to another, without much evidence of commonalities. Teaching might be something like playing a musical instrument. All human cultures have some form of music, suggesting a genetic propensity and capacity. But in any given cultural setting, some people play an instrument (sometimes just their own voice), with dramatically varying individual skill, and some don’t play at all. Further, forms of music—for example, what constitutes a pleasing sequence of notes—vary widely across cultures (for a useful discussion, see Levitin 2006). If teaching is like making music, then it’s a sort of personal cultural practice, something we can all do, but only if we put our minds to it. Or, teaching might be more like language, a tremendously complex signaling system, far more complicated than any kind of music, which all cognitively normal children in every culture largely master, entirely on their own initiative (granted, with much practice and social support), by the age of about five. And if teaching is primarily a natural, geneticallybased instinct and capacity, then we might expect to find evidence of teaching in every culture, with universal tactics and strategies, and as little variation in skill from one individual to the next as one finds in, say, the

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tricky business of walking upright on two legs. Teaching, in this account, is something we all do, an instinctual and unshirkable responsibility, one that is fully bound up with our other ways of being a human on Earth. Clearly, the answer lies somewhere in between these two poles. Human pedagogy is, and has likely almost always been, a biocultural behavior, rooted in the natural capacities and inclinations that began to emerge at the very beginning of our evolutionary history, and then became shaped, over time, and up to the present day, by local cultures. Language, and teaching through language, may have begun as largely cultural behavior—a small set of conventionalized gestures and vocalizations used in founding groups of early hominins. But, given what we now recognize as the human instinct for language and teaching, teaching must have also become increasingly biological as the emerging protolanguage and capacity for “natural pedagogy” (Csibra and Gergely 2011) began to select for brains that were better equipped and inclined to use these tools effectively, and increasing numbers of children began to be born with genetic programs specifically adapted to growing these language-ready, pedagogically-ready brains. Now here’s the important thing, and focus of this chapter—this proposition is testable. If language, and teaching through language, is at once biological and cultural, based on natural capacities and instincts shaped by cultural experiences, then we ought to find that the growth of teaching and learning in individuals follows a trajectory determined in part by our biological development, from embryo to senescence, and in part, as we live out our lives, by the changing circumstances and influences of our social and cultural environments. As we’ll see, this is indeed the case. The development of the capacity for teaching and learning through language follows a more-or-less predictable pathway—one that begins as a universal instinct and then becomes less predictable with age and growing cultural influence. It starts as a set of behaviors found in all cognitively typical mothers and babies, presumably in every culture throughout the world. These include, on the caregiver’s part, a disposition to treat the infant as a social being with a mind of its own—a person worth communicating with even before it communicates much on its own. On the infant’s part, this includes a disposition to treat the vocalizations and gestures of mothers and other

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caregivers as communicative acts intended to impart generically useful information (an essential pragmatic assumption for learning through language, see Csibra and Gergely 2011), and, just a few years later, a disposition to share information and correct the false beliefs of others. Then, over the course of continuing development, from childhood through adulthood, it seems that small individual differences in our natural capacity and inclination for teaching get amplified and augmented (or, lamentably, stifled) by our different cultural experiences and influences—most obviously by our own parents, caretakers, peers, and other teachers—with the result that we all end up with broadly varying repertoires of teaching skills, and varying roles and responsibilities. All of us teach and learn from our parents and other teachers. Some of us eventually become expert hunters, honey-collectors, navigators, artisans, electricians, computer programmers, and college professors. Some become more effective teachers than others. All modern cultures pay people to teach other people’s children. Some cultures have come to value professional teachers more than other cultures do and pay them more. In short, growth in the capacity for teaching, viewed from the perspective of both phylogeny (the evolution of teaching as a species-specific set of adaptive traits) and ontogeny (the development of the capacity and practice of teaching in individuals), takes the shape of a tree. It has many roots, representing a broad, tangled, but solid foundation in the form of the multiple neural and anatomical mechanisms—perspective-taking circuitry, circuitry for speech processing and production, prosociality, etc.—that have come to make language and teaching through language possible. Teaching emerges in children as a solid, uniform “trunk,” representing commonalities across all individuals and cultures, and then branches out again as biocultural forces start to create complexity and variation (Fig. 7.1). I’ll have more to say about cultural variation later. First, let’s look at the evolutionary roots of “natural” pedagogy, viewed from the perspective of human life history and growing brains.

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Variation in adult teaching practices shaped by cultural experiences, personal interests, etc.

Predictable commonalities in teaching across cultures.

Biological roots of the teaching instinct. Fig. 7.1 A biocultural model of teaching (Tree artwork created by www.freepik. com)

The Supersized Brain Problem As we’ve seen, a two-way, coevolutionary relationship links our speciesunique capacity for language and the size of our brains relative to the size of our bodies. These two components of the human adaptive suite are, like all our other traits, intimately and necessarily connected. The signaling system we call “language” can be understood, at least in part, as a solution to the problem of providing a large, energy-hungry brain with a steady and sufficient supply of nutrients through the affordances of cooperative, technology-supported foraging as a joint attentional activity. And our supersized brains can be understood as a solution to the problem of meeting the computational demands of this increasingly complex system of communication. But our enlarged brains present another problem we haven’t considered yet. Unlike the ready-to-go musculature and sensory-motor capacity of baby antelope, deer, giraffes, and other hoofed animals, which allow the young of these species to struggle up on their spindly legs and begin ambling along with their mothers and the rest of the herd just hours after birth, the brains of human offspring require as many as 20 years or more to reach full maturity.1 This places a hard limit on the rate at which our

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capacity for teaching and learning can grow, and partly explains why we probably won’t find any 5-year-old college professors no matter where we search. But another obvious reason why our capacity for teaching and learning grows so slowly is that it requires cultural inputs, and these become available only through engagement in a broad range of joint attentional activities with others. Further, the scope of these learning opportunities is in turn limited by our genetically-programmed developmental schedule. Just as most of us must crawl before we can walk, so can we understand only as much language as our developing brains will allow. And we can only fully participate in the life of our host group, and begin contributing to its work, when we get big enough to walk long distances, climb tall trees, carry heavy loads, and, in some societies, drive cars. We must also have a solid grasp of the group’s language: not just the syntax—most of which comes early on—but also an adult lexicon and sensitivity to the subtleties of practical, idiomatic use of the lexicon, which take years to acquire. In the meantime, we need the rest of the group to compensate for our inadequacies. That’s the problem. A large part of the solution, it now seems, lies in our unique life history pattern, including our lengthened lifespan (as discussed earlier, we live, on average, a good twenty years longer than our chimpanzee cousins), and an increased period of juvenile dependency, including two distinct new developmental phases of that evolution, has cleverly “inserted” into the standard primate template. The first, an early juvenile period between infancy and later childhood, results from relatively early weaning, which is made possible in part by collective child-rearing (“alloparenting;” see Hrdy 2007) and in part by earlier emergence of adult teeth. The second is adolescence, a new period between childhood and adulthood in which young people come to inhabit adult bodies before their brains have become fully adult. As we’ll see, these changes have major consequences for the development of language, and teaching and learning through language.

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Life in Small-Scale Hunter-Gatherer Societies Let’s start with the issue of context. Although ancient archeological sites—with their piles of broken animal bones, stone tools, and, from about 500,000 years ago, hearths—provide important clues, we’ll never know exactly what daily life was like for our Pleistocene ancestors. We do know that throughout all but the most recent 10,000 years of human evolution, our ancestors were hunter-gatherers, depending entirely on a diet of fruits and vegetables (mainly roots), meat from other animals, shellfish, fish, and other waterside resources. We also know that, like all primates, mothers nursed their babies, that children grew up slowly, and that adults lived relatively long lives compared to other primates. And we strongly suspect that, for millions of years, males and females had been pairing off, forming extended family groups, and sharing some degree of responsibility with other group members for the protection and provisioning of their offspring. Although the evidence is circumstantial, we have reason to believe that by 3 million years ago, adults had begun scavenging, hunting, and gathering cooperatively, and that at some point, possibly around that time, they had begun to evolve a unique new form of communication in support of these activities. But, because behavior doesn’t fossilize, and because we can’t travel back in time to observe these behaviors, the details of daily life are forever lost. We do, however, have rich ethnographic accounts of life in remaining small-scale hunter-gatherer societies that have only recently begun to come under the influence of the modern world. Although these societies have evolved, and selectively borrowed, considerably more complex technologies than would have been available in the Pleistocene, there’s reason to think that the basics of life in these societies can give us some sense of what life would have been like for our distant ancestors. In the following sections, I’ll try to summarize what is known about recent hunter-gatherer lifestyles, with an emphasis on features that cast light on the relationship between human life history, age structure, and the transmission of cultural knowledge and skill from one generation to the next through language. Much of this information is drawn from a useful paper by Harvard University anthropologist Frank Marlowe (2005), which summarizes trends from 478 different societies, all which

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depend on foraged plants and animals for more than 90% of the diet. Here are some of Marlowe’s more relevant findings: 1. Although the proportions vary, most hunter-gatherer groups rely on a mixed diet of plants (median = 35%), meat (30%), and fish (25%).2 With few exceptions, hunter-gatherers exploit many species within each of these categories, each of which has different properties, locations, and complex behavioral characteristics (methods of defense), which must be learned (or taught) separately. 2. Both males and females contribute foraged food, though males contribute slightly more (60%), likely owing to reduced childcare responsibilities.3 3. Hunter-gatherers tend to live in small residential groups of approximately 30 and are loosely associated with larger “ethnolinguistic” groups (median = 895). During the day, adults and older children split into small foraging parties, leaving others in camp, and return at the end of the day. 4. Nursing mothers tend to take nursing infants with them on foraging expeditions, but leave younger children, who are too big to carry but too small to walk long distances, behind in base camp, under the care of older children and adult caretakers. 5. Because of the need to maintain a sufficiently large gene pool (and resulting incest taboos), males and females tend to marry outside their residential group, meaning that most groups represent different kinship lines. Also, most foraging families are “multilocal,” meaning families spend parts of the year in camps with the wife’s kin, and parts with the husband’s. 6. Except for groups which have access to a regular supply of food in a single location (such as indigenous tribal peoples in the Pacific Northwest who rely primarily on salmon runs), most hunter-gatherers move their base camps several times a year, following seasonal fluctuations in the location of food sources. 7. Foraging ranges vary in size, depending largely on diet. Groups that rely more heavily on hunting, or live in drier climates (with more widely scattered food patches), have larger home ranges.

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8. Cooperative foraging is accompanied by food sharing among families in the residential group. Foraging bands that have a “bad day” can count on contributions from those that have a better one.

Age Structure of Hunter-Gather Residential Groups Given the important relationship between human life history phases, the emergence of the capacity for teaching and learning through language, and the role of teaching and learning in the transmission of critical cultural knowledge and skill, it would be nice to know something about the social structure of the groups our deep ancestors grew up in. For example, what was the likely ratio of children to adults? Of younger children to older children? We can’t know for sure, of course, but the typical age structure of remaining hunter-gatherer societies seems a reasonable place to start. As noted above, these groups typically consist of around 30 members at any one time. Table 7.1 gives an estimate of the number of males and females by age group, and the likelihood that individuals of different ages will participate in foraging activities or remain at the base camp (based on data Demps et al. 2012).4 To be clear, these numbers are rough guesstimates. The age structure and daytime locations will clearly vary from one group to another; the number of people in the residential group will vary (in Marlowe’s Table 7.1 Age structure of a typical hunter-gatherer residential group Age group

Daytime location

N

Elderly (50+) Post-reproductive females (36–40) Older adult males (26–50) Younger adult males (21–25) Young mothers (21–35) Adolescents (11–20) Older children (3–10) Infants/younger children (0–2)

Base camp/foraging Foraging Foraging Foraging Base camp/foraging Base camp/foraging Base camp/foraging Base camp/foraging (with mother) Total

3 2 5 1 5 6 6 2 30

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data, from 13 to 275); and the age at which children begin joining in foraging activities will also vary by group, as determined by factors such as the distance to food sources, the difficulty of travel, and the level of danger involved. For example, among the Aka, who engage in net hunting of small mammals in the Congo rainforest—a relatively safe and undemanding activity—only the youngest children (aged 3–5) remain in camp, typically watched over by a single elderly member of the group while the rest of the group is out on a hunt.5 However, in other cultures, where hunting expeditions are more arduous, young people tend not to participate in hunting expeditions before early adolescence. For example, as shown in Table 7.2, in a survey of 21 different groups of hunters, Katherine MacDonald (2007) found that young people do not typically join adult hunting expeditions until late childhood or early adolescence. Another complication is that depending on the success of prior foraging expeditions, even adults may spend some days in camp, or may venture out only in the mornings, returning in the afternoon. Nevertheless, a general pattern is clear. In a typical hunter-gatherer group, nearly half will be juveniles. Although the older ones are likely to spend at least some of their time foraging alongside adults for at least part of the day, the younger ones, especially those between the ages of 3–5, spend most of their time in camp. Here, children are looked after partly by a small number of older men and women (who would not be up to the rigors of a foraging expedition)—but they also look after themselves, meaning the older children help take care of the younger ones. As we’ll see, a mixedage peer group of children turns out to be an important, cost-efficient Table 7.2 Age of first participation in adult hunting expeditions for 21 different groups Age group

N

%

Early childhood (5–8) Late childhood (9–10) Early adolescence (11–14) Adolescence (11–18) Late adolescence (15–19) Adulthood (20+) Total

4 2 6 5 3 1 21

19 10 29 24 14 5 100

Summary table compiled from MacDonald (2007)

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mechanism for the transmission of critical cultural knowledge and skill without the need for direct adult involvement.

Hunter-Gatherer Childhoods Let’s now take a closer look at the nature of childhood in remaining hunter-gatherer cultures. A review by Emory University anthropologist Melvin Konner (2005) looked at evidence from studies of six hunter-gatherer cultures: the !Kung (Kalahari desert, southern Africa); the Hadza (Rift Valley, Tanzania); the Efe (Ituri rainforest, Congo); the Aka (Central African Republic and Congo); the Aché (Paraguay); and the Agta (or “Aeta,” Philippines). Konner identifies eleven distinctive features of infancy and childhood that are more or less present in these cultures: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Frequent nursing in infancy Weaning at 3–4 years (1–2 years earlier than in chimpanzees) Sleeping with the mother Close physical contact with the mother in infancy Adult “indulgence” of children (close attention, lack of physical punishment) Nonmaternal care (care provided by other females in the group) Father involvement Maternal primacy (the mother as primary caregiver) Multiage peer culture “Carefree” childhood (made possible by adult provisioning). Adult tolerance of premarital sex in adolescence.

Table 7.3, reproduced from Konner’s paper, summarizes the most relevant results across the six cultural groups. (The number of + signs in each cell indicates the salience of the feature in each group.) Notice that despite the variation across the groups, all the identified features are universal to these cultures. Some of these features, including overall indulgence of juveniles, are also found in most nonhuman primates. However, others—notably paternal involvement (associated with pair bonding), the importance of multiage peer groups (with, as

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Table 7.3 Hunter-gatherer childhood in six traditional cultures

!Kung Hadza Efe Aka Ache Agta

Overall indulgence

Other female care

Paternal care

Multiage peer group

Carefree childhood

+++ + +++ +++ ++ +++

++ ++ +++ +++ + ++

++ ++ + +++ ++ +

+++ +++ ++ +++ +++ +++

+++ +++ ++ +++ +++ +++

we’ll see, a distinct childhood peer culture), and an extended, relatively “carefree” juvenile period—may be distinctly human. Taken together, and viewed in combination with other features of the human adaptive suite we’ve already discussed, we can see that the hunter-gatherer childhoods are organized in a way that is uniquely conducive to the development of the capacity for language, and for teaching and learning through language, in brains and bodies that take at least twenty years to reach full adult maturity. Briefly stated: 1. The obligatory period of intense mother-child interaction in infancy, the period when the baby’s brain is growing most rapidly, provides an initial, cost-efficient launching pad for the lifelong process of acquiring language, and using language to teach and learn. 2. The extended juvenile period, from weaning through early adolescence, provides multiple opportunities for acquiring the group’s language(s), cultural knowledge, and skill in the context of mixed-age peer groups, thus reducing the need for direct adult involvement. 3. In adolescence, a kind of maturational limbo between childhood and adulthood, young people are given time to grow into their adult bodies and brains, often participating in adult activities, but without the full responsibilities of adulthood. 4. Finally, in their extended adulthood, now with fully developed brains and bodies, men and women have time to continue accumulating knowledge, skill, and wisdom in the context of adult work, and, through various forms of apprenticeship, to pass along what they’ve learned to youngsters, adolescents, and younger adults.

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Although few of us grow up in hunter-gather groups these days, and although the advent of formal public education has dramatically changed how most of us spend much of our childhood, our brains, bodies, and minds still develop in accordance with the schedule that evolution designed for us far back in the Pleistocene.

The Ontogeny of Human Teaching and Learning To repeat, if teaching and learning through language is in some part instinctual, then we ought to see evidence for the growth of these capacities in all humans. Also, given the complexity of teaching and learning through language—which involves, among other things, the accumulation of our own personal knowledge; the ability to detect invisible gaps in knowledge, misunderstandings, and false beliefs in others; and the ability and inclination to form and articulate linguistic utterances designed to narrow these gaps—we might expect that the capacity for pedagogy does not emerge all at once. Rather, it ought to develop over time, following a schedule that is at least partly genetically-determined, and therefore at least partly predictable. In fact, a significant body of evidence supporting a biocultural account of human pedagogy does indeed suggest a cross-cultural “normative developmental trajectory” of pedagogical expertise that begins in infancy, emerges full-blown in late childhood, and continues into adulthood (Strauss and Ziv 2012). Further, given the importance of language, and the inseparable, intricate relationship between language, cultural learning, and teaching, it’s unsurprising that the developmental trajectory for pedagogy largely mirrors the trajectory for language acquisition. This trajectory, however, is far from smooth and linear. Like the trajectory of human evolution itself, the development of the capacity for teaching in individuals unfolds as a series of small, even daily steps—punctuated by surges coincident with major life history milestones, such as the transition from infancy to childhood (weaning), from childhood to adolescence (often marked by rites of passage), and from adolescence into adulthood. The climb from instinctual learner to expert teacher is uneven

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and, in places, steep. We all get to different places and in different ways. Nevertheless, certain apparently deep-seated patterns of development are evident across cultures, from small-scale societies to highly industrialized, “WEIRD” ones (Henrich et al. 2010).

Infancy: Fruitful Dependency As in other primates, the first years of human life are characterized by a relatively long period of nearly complete dependence on the nursing mother and other caretakers—up to the point of weaning, which, in traditional societies, ranges widely between 12 and 54 months (median = 2.5 years) on average, about two years earlier than in chimpanzees and bonobos (Marlowe 2005). During this period, our brains, which are already larger at birth than those of other primate infants, grow especially rapidly, fueled by our mother’s milk (a particularly rich source of nutrients for a developing brain and nervous system) and consuming nearly 90% of metabolism (Leonard and Robertson 1992; Uauy and Peirano 1999). It is during this period that our brains continue laying down and organizing the increasingly complex circuitry that will eventually make teaching and learning through language possible—including circuitry for perspective taking, conversational alignment, symbolic representation, recursive syntax, prosociality, and other critical components of human pedagogy. This is also the period during which our natural gift for language acquisition comes on line with a vengeance—when, so long as someone bothers to speak with us, or at least in our vicinity, we get right to work figuring out the sound system, syntax, and vocabulary of whatever language or languages are spoken in our company.6 Just a few days after birth, we can distinguish between the sound of utterances in our native language and those in some other languages (Mehler et al. 1988), the result of a process that seems to begin in utero (Moon et al. 2013). Also, as discussed in Chapter 6, not too long after birth we begin paying special attention to our mother’s “ostensive” signals (e.g., her pointing and directed eye gaze), which we interpret as having a communicative intent well before we’re able to learn much from these communications (Csibra and Gergely 2009). Mothers and infants also engage in

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pre-linguistic turn-taking dialogue, and mothers are especially responsive to infant vocalizations that approximate native-language speech sounds (Gros-Louis et al. 2006). In speaking to us, many use, quite likely instinctively, a special form of language (child -directed speech, also known as “motherese”) which is syntactically and phonetically simpler than the full adult version.7 By 9 months, as evidence of our developing perspective-taking circuitry, we begin to understand others as intentional agents, “like us” with the result that we begin to engage with others in joint attentional activity, the notion that “we” are doing something together, such as playing peek-a-boo or catch (see Bruner 1972; Meltzoff 2007; Tomasello and Todd 1983). Then, as noted in Chapter 6, at about one year we begin to combine our own indexical pointing with verbalizations (disambiguated pointing) for a range of communicative purposes (Liszkowski et al. 2008). We point to identify the location of a toy we want someone to retrieve or notice someone else is looking for. We point at objects we want someone to name for us, and places we want to be carried to. And whereas other primate babies can cling fast to their mother’s body hair, leaving the mother’s hands and arms free for digging, climbing, grasping, and other foraging activities, we need to be carried (possibly with a sling or some other forms of baby-carrier), or handed over to another caretaker, such as an older child or other family members. Among other consequences, this kind of cooperative parenting (“alloparenting”) may result in more intense and complex linguistic interaction and social simulation (Liszkowski et al. 2008). In any case, by about 18 months, our ability to engage in aligned turntaking dialogue using syntactic, symbolic language is becoming pretty well established, and with it the ability to understand others not just as intentional agents, but as cognitive agents, having their own separate beliefs and desires. By this time also, we’ve begun to request acts of pedagogy, that is, to ask questions of others (Bloom 2000). As we interact with our cultural environment, and observe how others interact with it and each other, we gradually come to acquire those aspects of cultural knowledge and expertise found in all human cultures, quite possibly because our brains are already constrained to learn in these ways. These built-in “cognitive templates” may include, for example,

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universal patterns of categorization and reasoning about the biological world—that plants and animals have generic names, that animals can be identified by the sounds they make, that animals are dangerous or not, foods are edible or not, and so forth (Atran and Medin 2008). The templates may also include universal features of the human social world, e.g., that the same person can be called by different names, depending on social relationships, that one is expected to treat people differently depending on one’s relationship to them, and so forth. In short, as we transition from infancy into childhood, we’re already off to a strong start on the road to a lifetime of teaching and learning. We’re up and walking, literally—and figuratively in the sense that we have the basic cognitive tools we need to start acquiring our group’s culture in earnest, and to begin sharing, altruistically, what we’ve learned from others, and in this way start paying off on the investment our group, especially our mothers and other adult caregivers, have been making in us. Note that to this point the investment is highly leveraged. At the same time that mothers and other caretakers have been nourishing us physically, they’ve also been nourishing us linguistically, at very little additional cost in time and effort. We still have a long way to go, but evolution has made sure that we have plenty of time and opportunity in the next stage: childhood.

Teaching and Learning in Early Childhood Human childhood, which may be understood broadly as the juvenile period between infancy and early adolescence, is usefully understood as consisting of two distinct phases, which we can call “early” and “later” childhood.8 Fossil evidence suggests that early childhood, a development phase considered to be unique to humans, began to get inserted into human life history beginning some 2 million years ago, around the time of Homo habilis (Bogin 1990). Technically, this new phase is the period between weaning, at around the age of 3, and the first eruption of adult teeth, which begins at about 6 years. This is also the period during which the very rapid physical growth of infancy, including brain growth, begins to level off.

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During evolution, early weaning relative to the emergence of adult teeth had the crucial effect of decreasing the birth interval, with the result that mothers could produce twice as many offspring in the same amount of time that it takes chimpanzees and orangutans to produce one (Locke and Bogin 2006). Other factors put aside, this alone had the potential (which, owing to various environmental constraints, is not always realized) to substantially increase the size of the group and, in particular, the number of young children in the group. However, owing to limited jaw strength and lack of adult dentition, children in this new early childhood phase (3–5 years) still have difficulty eating adult food and so remain dependent on other members of the group to provide them with easily digestible foods such as honey, bone marrow, and mashed ground roots. Here again the unique human life history profile seems to have come to the rescue—in the form of grandmothers. Unlike female chimpanzees and other primates, who continue reproducing up to senescence, human females can live for decades after their reproductive years. According to the so-called grandmother hypothesis, this is either a specific evolutionary adaptation or a natural consequence of a lengthened lifespan (Hawkes et al. 1998). In any event, the result is that once they’ve stopped reproducing and their own children are grown, mothers can begin helping their sons and daughters raise grandchildren, and may also be available to look after other children in the group. In short, although youngsters do not yet have the strength or stamina to take on adult work, childhood is a period in which we’re fully mobile and quite capable of interacting socially with others. Older children can assume caretaking responsibilities for younger siblings, and, depending on the size of their extended family group, children at any age are likely to be more or less surrounded by younger and older children. Indeed, the language and cognitive development of children reflect a growing reliance on, and contribution to, the social life of the group. Although we continue working out rules of syntax, semantics, and pragmatics into adolescence, by the age of 3 we’re fully capable of using our growing language capacity to express our desires, to begin understanding the desires of others, and to negotiate the difference. At around 3.5–4 years, we begin demonstrating a recognition of shared cultural norms, i.e., that “we” as a group (not just a dyad) do things in certain

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predictable and approved ways (Tomasello 2014). This is accompanied by “emergent teaching” (e.g., simple correction) and subsequently by an inclination not just to correct, but to explain (Strauss and Ziv 2012). By the age of 7, we’re detecting and correcting false beliefs in others, and beginning to alter our peer teaching strategies based on an assessment of what others already know—what Strauss et al. (2002) call “contingent teaching.” At about this time, those of us with access to formal, schoolbased education are also beginning to adopt the school-based models of instruction learned from classroom teachers, such as giving instructions and explanations from a distance, as from the front of a classroom (Maynard 2002).

Teaching and Learning in Later Childhood The later phase of the human juvenile period is considered to begin with the eruption of permanent teeth at around age 6 and end at the onset of puberty and associated growth spurts, which begin at around 10 in girls and 12 in boys. This second phase of childhood is, and likely long has been, a period during which children around the world spend increasing amounts of time in the company of other children, in mixed-age groups, free from direct adult supervision for much of the day.9 This way of being in the world often revolves in large part around a separate peer culture, a childhood subculture characterized by play with physical toys (often child-sized versions of adult-world artifacts, see below); various kinds of physical games, such as foot races, ball games, and tag; singing and rhyming games; and games such as hopscotch, which combine and exercise both physical and verbal skills. These more or less formalized play activities are passed down from one generation of children to the next, often independent of adult influence.10 Partial evidence that participation in childhood peer culture is natural in humans, and a universal aspect of the serious business of growing up, may be found in modern hunter-gatherer cultures, where children spend much of their time playing with other children in mixed-age groups (Hewlett et al. 2011). (As noted above, in a typical hunter-gatherer residential group, about half of the population is under 15 years old; Hewlett

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1991.) A study of types of play among 7- to 15-year-old hunter-gatherers in Cameroon, for example, identified 85 different games, the majority (61%) related to hunting and gathering activities, camp life (cooking and childcare), and language games, such as singing and dancing (Kamei 2005). Moreover, we have good reason to believe, and some supporting evidence, that children have been engaged in simplified versions of adult activities for hundreds of thousands, if not millions, of years. For example, the archeologist John Shea, who specializes in prehistoric technology, has found examples of very small stone flakes and cores at early Acheulean sites in Israel. Since few of the tiny artifacts bore signs of actual use, Shea suggests they might well have been produced by children practicing the art of knapping more than a million years ago (Shea 2006; see also Finlay 1997). In any case, as we rise through the ranks of peer culture, children everywhere become increasingly knowledgeable and skillful in the ways of the world, at least as a child experiences it. We’re learning how to play games with other children, to understand and explain the rules of these games, to barter possessions, to dare and respond to dares, to build and manipulate affiliations with other children, to engage in playful simulation of adult work, and so forth. All of these activities provide us with opportunities to acquire and develop skills and cultural knowledge much of which becomes indispensable, though in different form, in the adult world. As such, childhood serves—and has apparently evolved—as a kind of early apprenticeship, during which younger children serve as apprentices to older ones. As a result, both younger and older children have plenty of time and opportunity to exploit and practice their natural capacities for teaching and learning on their own.

Adolescence Adolescence, characterized biologically by a growth spurt and the onset of puberty, is a second phase (after early childhood) that evolution appears to have inserted into human life history, possibly as early as a million years ago, at the time of Homo erectus, but possibly much later, with the appearance of our own species around 300,000 years ago (see

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Bogin 1999). In modern humans, adolescence spans about eight years, from 12 to 20, and serves as an intermediate phase between late childhood and adulthood. During this phase, marked by the onset of sexual maturity, we’re coming to inhabit the bodies of adults, but our brains are still developing, and our practical experience of the adult world of work and sexual relationships is still limited by both biological and cultural constraints. As we approach adulthood, we must climb down from a dominant position in our childhood peer culture to a much lower rung on the social ladder of the adult world. Later adolescence, in other words, is a second period of apprenticeship, but now the game becomes more serious as we find ourselves interacting with, subordinate to, and beginning to compete with, more experienced adults. In traditional societies, the onset of adolescence (often marked by initiation rites) is a time when young people begin foraging for themselves and participating directly in adult hunting and gathering activities. In modern technological societies, while young people are also becoming physically capable of adult work, most (at least those fortunate enough to have access to public education) remain in school for at least some portion of their adolescence, hopefully acquiring the technical skills and knowledge needed for full participation in the work of a complex technological society. In any case, around the world, and probably far back in time, adolescence has been a period of increasing pressure to apply the cultural knowledge, techniques, and social skills we develop in the incubator of our childhood peer culture to the problem of our own independent survival as well as the survival of the group. We still have the cognitive capacity for teaching through language that we cultivated and practiced during childhood; however, the onset of puberty, the increasing prominence of gender identity and gender-specific roles, the consequent emphasis on sexual competition, and the need to begin competing directly with adults may have a dampening effect on the incidence and inclination for peer teaching. It’s not until the next phase, adulthood, that we’re ready to fulfill our destinies as our group’s top teachers.

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Adulthood Adulthood, the last stage in the human life cycle, lasts some twenty years longer than in the other great apes. As discussed earlier, it’s been argued that the relatively long lifespan of humans, which includes the extended juvenile period, is an adaptation that coevolved with the dietary shift toward high-value, hard-to-extract food resources (Kaplan et al. 2000). The idea is that success in this new niche required longer periods for learning the critical cultural knowledge and skill necessary for the successful exploitation of these resources. (For an argument that some foraging skills—such as archery, tree-climbing, and digging tubers— don’t actually require much practice, see Jones and Marlowe 2002.) In any case, it seems that the combination of cooperative foraging and sharing of nutritious food reduced mortality rates in adolescence and adulthood, thus giving more time for accumulation of expertise and technical innovation during the peak period of productive work. Adulthood is also the period in which our natural capacity for teaching begins to peak. Our adult brains are now fully formed, leaving no biological limits on our capacity for language, nor on the use of language for teaching. We’ve also had plenty of cultural support and influence by this time. As we enter adulthood, we’ve been teaching others, including our peers, and observing and experiencing the teaching of our elders, for some 15–20 years. Moreover, as full participants in our group’s culture, we’ve also begun to accumulate the kind of technical knowledge, skill, and wisdom that can only be acquired in the context of authentic work alongside, and with assistance from, more accomplished adult experts. Not everyone fully masters the group’s technologies, particularly in cultures (as in most present-day cultures) where technological advances have led to massive specialization. And even smaller subsets become master teachers in their specialties, and probably even fewer contribute important innovations that get adopted by the larger group. But evolution has arranged the course of our biocultural development in such a way that a significant portion of the surviving adults in a given group has by now had sufficient time to develop the cognitive capacity, the complex repertoire of cultural knowledge and skill, and the ability to pass along this crucial inheritance to upcoming generations. In this way,

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the crank-and-ratchet mechanism of cultural transmission (Tomasello et al. 1993) can push forward another notch.

Learning to Hunt in Hunter-Gatherer Cultures The development of skill in hunting other animals, an important component of the hunter-gatherer lifestyle humans have practiced throughout most of our history, illustrates the relationship between stages in physical development, life history, and the punctuated development of technical expertise. Given that our ancestors were hunter-gatherers throughout the course of human evolution, up until the development of agriculture just some 10,000 years ago, studies of the development of hunting proficiency in remaining small-scale cultures give us some sense of the relationship between human life history, teaching, and cultural evolution in the distant past, and how it may developed over time (e.g., see Bock 2005; Gurven et al. 2006; MacDonald 2007). Beginning as early as the toddler stage, when children are too big to be carried but physically unable to walk the long distances required for a hunting trip—and are otherwise especially vulnerable to its dangers— children in hunter-gatherer cultures typically remain in camp, developing skills such as archery or use of blowguns using miniature weapons, often provided by adults, and shooting, for example, at targets representing different animals, sometimes pulled along the ground by other children (MacDonald 2007). Some of these activities involve relatively simple motor skills that can be acquired through imitation, practice, and perhaps a little feedback from peers and adults, in conjunction with serious play. Other skills, notably animal tracking and stalking, must be acquired in the context of actual hunting expeditions, and so must wait until children have the required strength and stamina. Note again the crucial role of games and childhood peer culture in this process. As we’ve discussed, a combination of expanded group size and reduced birth intervals leads to pooling of a sufficiently large group of physically-active, language-using children, of mixed age, who can get along with relatively little adult supervision—teaching and learning

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from each other in a small, child-friendly replica of the adult world, minus its dangers and distractions. In later childhood, once they are able to walk longer distances, children in these meat-hunting cultures begin to accompany adults on hunts. However, they are still limited physically, and adults may try to simplify the scope of the hunt to compensate, such as by hunting smaller prey within easy walking distance. Later, with the growth spurt of adolescence, young people become physically strong enough to participate fully in hunting expeditions, at which point they can begin to accumulate knowledge and skill in the tracking, pursuit, and capture of animals, in some cases with active instruction from adult experts. Finally, as they enter adulthood, at around the age of 20, with nearly fully developed brains, young people are ready to start taking on the cognitive challenges involved in learning the behaviors of large numbers of animals, such as how to read their tracks, how to stalk them without being noticed, and so forth. This learning process can continue for an additional 10–20 years—into the late 30s or early 40s—reportedly the time it takes to develop full hunting mastery (Gurven et al. 2006). And as adult hunters continue to gain expertise through experience, they become increasingly valuable to the group not only as practitioners, but as teachers—assuming they’re willing to play the role.

From “Natural” to “Culturally-Biased” Pedagogy We can now see that the myriad acts of teaching and learning we engage in ourselves, and see and hear around us every day, reflect a highly complex, truly ancient system of developing biological capacities and instincts, and intricately-layered cultural influences, which, because they are so common, are very hard not to take for granted. But every single act of human pedagogy—a local citizen giving a stranger directions to the nearest subway station, a child asking his mother why subway cars screech, his mother trying to explain the relationship between friction and sound, a doorman teaching a visitor how to use an elevator—all of these seemingly ordinary acts rest on, and would be impossible without,

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the full biological and cultural evolution of our species. It seems we all carry around hidden within us not only the history of our personal development as teachers and learners, but also the extraordinary history of our evolution as Homo docens, the teaching species (Gärdenfors and Högberg 2017). As an example of the underlying complexity of human pedagogy, including both its ancient roots and modern manifestations, consider the results of a video study conducted by University of Hawaii psychologist Ashley Maynard of the different teaching “styles” employed by older children while instructing younger siblings in their homes in Nabenchauk, a small Mayan village located in the highlands of Chiapas, Mexico (Maynard 2002). The older children, the “teachers,” ranged in age from 3 to 11 years and the younger ones, the “students,” from 20 to 36 months. Seventeen of the older children had some schooling, ranging from 1 to 2 years, while the remaining 36 had not been to school at all. Altogether, Maynard managed to videotape a set of more than 150 distinct “teaching episodes” in which the older children could be seen helping, in various ways, their younger siblings perform tasks such as washing, cooking, taking care of baby dolls, and making tortillas. To analyze these episodes, Maynard devised a coding scheme consisting of a set of eight “discourse measures” including verbal commands (“Put it there!”), feedback (“Good.)”, criticism (“Dummy!”), physical actions, such as guiding the body (guiding a younger sibling’s hand), combinations of physical and verbal moves (talk with demonstration), and what Maynard calls the “initiation” of a teaching episode. Several of Maynard’s findings are relevant to our discussion here. First, consistent with studies we’ve already reviewed of teaching in children (e.g., Strauss and Ziv 2012), she found a clear developmental pathway. As early as the age of 4, children were taking responsibility for helping their younger siblings learn how to carry out simple domestic tasks. By the age of 8, they had become accomplished teachers, drawing on a repertoire of verbal and gestural signals. Maynard also found an interaction between an “indigenous model” of instruction and one borrowed from Mexican school culture. The indigenous model involved features such as close physical proximity (the older child standing close to the younger one in the task setting),

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task-embedded verbal instructions, physical demonstrations, directives, scaffolding (defined as breaking a task down into manageable steps, based on the younger child’s ability), and corrective feedback. Features of the school model included giving verbal instructions from a distance (as from the front of a classroom) and providing “decontextualized” verbal explanations. Importantly, there were no differences in the amount of time the schooled and unschooled children devoted to teaching their younger siblings. The unschooled children spent just as much time teaching their siblings as those who had experienced formal classroom instruction. The children who had been to school used more verbal discourse in their interactions, provided more explanations, and gave more verbal instructions at a distance. However, they drew from both models of instruction, falling back on the indigenous model when the school model failed to work. For example, when the younger children didn’t respond to verbal instructions given at a distance (a teaching strategy typically used by classroom teachers), the older children drew closer and provided direct, nonverbal assistance. Maynard’s results are interesting for a number of reasons. First, note that her data are consistent with, and support, a biocultural account of human pedagogy. The fact that the unschooled children devoted as much time to tutoring their younger siblings as those who had been exposed to a modern school culture is consistent with the notion of teaching as a human cultural universal and affirms the importance of the mixed-age childhood culture in the natural ontogeny of human pedagogy. For the same reason, the tactics and strategies that the younger, unschooled children employed—including verbal commands, feedback, and praise—are good candidates for pedagogical practices of arguably ancient origin. In contrast, the strategies learned in school, those not associated with the traditional model, may be understood as representing a layer of modern cultural influence which, in a sense, sits on top of the more ancient one. Finally, the fact that the schooled children switched from the school model to the traditional model when the school model proved ineffective may be understood as representing individual metastrategies, uniquely available to them as result of their different cultural experiences combined with their species-unique instinct for teaching.11

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Does the Ontogeny of Teaching “Recapitulate” Phylogeny? Given what we’ve discussed so far, it’s natural to wonder whether the ontogeny of human pedagogy—the development of the inclination and capacity for teaching in individuals, from infancy to adulthood—might reflect (recapitulate) in some important ways, the emergence and evolution of teaching in our species. For one thing, it’s hard to ignore the correspondence between (a) an infant’s early and apparently instinctual understanding and use of pointing as an act of communication and (b) the possible gestural origins of a protolanguage, especially, as discussed in Chapter 6, in “disambiguated pointing.” Given that joint attention and shared reference are foundational for both linguistic communication and teaching, it makes sense that this capacity would emerge early on in human development, perhaps mirroring its early origin in our species. Another, more subtle correspondence is between the length of time it takes individuals to develop a full capacity for teaching—at least some twenty years—and the time it’s taken our species to get to the point where we are now, arguably some two million years or more. Although the timescales are obviously different by orders of magnitude, if we ask how long it takes a person to become a master teacher, or how long it’s taken our species to become capable of producing such master teachers, the qualitative answer is the same—“a pretty long time.” And in both cases, the limiting factors are also the same—a combination of biology and culture. It takes at least twenty years for our brains to become fully developed and wired, and at least another twenty years for us to accumulate the vast store of knowledge, wisdom, and technical skill it takes to make us maximally useful as teachers. From an evolutionary perspective, it makes sense that such a staggeringly complex and powerful biocultural system could not have been built overnight. Indeed, we know it’s taken well over two million years of evolution for our brains to reach their present size, about 1300 cc, three times the size of a chimpanzee brain. In other words, we have a slowly growing, slowly filling brain in both ontogenetic and phylogenetic timescales. Finally, as discussed in the next chapter, it’s reasonable to suppose that the growth in the repertoire of tactics, strategies, and metastrategies that we see in teaching from early childhood into adulthood reflects,

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in some important way, the evolution of teaching expertise in evolutionary time, and also in historical time, when cultural evolution began to outpace biological evolution. The simple nonverbal teaching methods Maynard observed in the unschooled Mayan children, part of what she calls the “indigenous model,” might well be similar to the methods our distant ancestors used at the very beginning, when selection pressures first gave rise to the teaching instinct. And the more complex repertoire of strategies employed by the older children in her study (those with experience of formal classroom instruction) might equally be understood as reflecting the current state of the biocultural evolution of teaching in our species.

Individual Differences in Natural Pedagogy If the capacity and inclination to teach are at least partly coded in our DNA, then, as a consequence of natural variation, we should expect to see individual differences. However, because individual teaching practices are also shaped by personal experiences and other cultural influences (as in the case of the schooled Mayan children in Maynard’s study), it’s difficult to know in any given case to what extent an individual’s skill and interest in teaching results from some innate talent or challenge, to what extent from personal experience, or, as will most likely be the case, from some interaction between genes and culture, natural capacity and personal interest. A good example comes from a study conducted by anthropologists Adam Boyette and Barry Hewlett of teaching behaviors among children in two small-scale societies in the Central African Republic (Boyette and Hewlett 2017). As the researchers videotaped a group of children engaged in a leaf-gathering activity on the perimeter of the camp, one of the children, an 11-year-old Aka boy, emerged as a particularly active teacher. He was observed praising two of the other children for their work climbing trees, tying up bundles of leaves, and dropping them to the ground. Altogether, he was responsible for five different teaching episodes during this particular activity and was one of five children who engaged two other children in teaching episodes simultaneously, which

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he did a total of eight times, far more than any other child. A natural teacher. As we see everywhere in nature, the past shows up in the present, so the present provides glimpses into the past. Like all children, this 11-year-old Aka boy clearly had the natural capacity and inclination to teach through language—just more of it than his peers. As such, we can guess he would have been an especially valuable member of his group. By going out of his way to help other children acquire important foraging skills, he was doing more than his part to help prepare the upcoming generation to improve the group’s likelihood of survival in the future. One likes to think that his talents as a communicator would also attract a suitable mate with similar disposition, and the two would go on to teach their own children well, and the children would in turn benefit from the special attention they’d receive from their parents and perhaps become especially effective teachers themselves. In fact, looking back in time, it seems that evolution must have selected for just such children as these, whose above-average capacities and inclination for teaching through language would have increased their own fitness and that of their kinship group. It’s hard not to think that it was in exactly this way that a natural capacity for teaching spread through our ancestral gene pool and came to be one of the most precious assets of our own inheritance.

Food for Thought 1. What exactly does it mean to say that teaching is a biocultural activity? 2. What is the relationship between human life history (as distinct from the life history of other primates) and teaching in humans? 3. Why is the typical age structure of hunter-gather groups important? 4. As you think back on the evidence of your own childhood, in what ways were your experiences consistent with arguments made in this chapter? In what ways, if any, inconsistent? 5. Do you consider yourself a “natural teacher?”

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Notes 1. As it turns out, young humans begin walking at about the same stage in brain development as giraffes; it’s just that our brains take longer to develop to that point. See Garwicz, M., Christensson, M., & Psouni, E. (2009). A unifying model for timing of walking onset in humans and other mammals. Proceedings of the National Academy of Sciences, 106 (51), 21889–21893. 2. The remaining 10% come from other sources, such as cultivated foods. 3. Although it is commonly assumed that only males hunt (as in the case of male chimpanzees) and women only gather, this is not strictly true. For example, among indigenous Australian groups, women hunt small animals, and among the Aka (Western Congo) men and women hunt together. 4. These numbers are interpolated from age group data for a set of five tribal Jenu Kuruba villages in South India, provided in Demps, K., ZorondoRodríguez, F., García, C., & Reyes-García, V. (2012). Social learning across the life cycle: Cultural knowledge acquisition for honey collection among the Jenu Kuruba, India. Evolution and Human Behavior, 33(5), 460–470. Note that about half the members of this hypothetical group (14) are juveniles, which is consistent with the average (44.5%) for hunter-gatherer groups given in Hewlett, B. S. (1991). Demography and childcare in preindustrial societies. Journal of anthropological research, 47 (1), 1–37. The cultural anthropologist Barry Hewlett, who studies small-scale societies in the Congo, considers these age group estimates “reasonable” (personal communication). 5. Barry Hewlett, personal communication. 6. Note the plural. I write this thinking of my own grandson, who has been simultaneously acquiring, to varying degrees, English (from his parents and others), Cantonese (from his grandmother), and Putonghua (“Mandarin,” from teachers at his full-immersion daycare). That a child’s brain is perfectly capable of acquiring not just one, but multiple languages at the same time, suggests that multilingualism may well have been a feature of the ancestral environment from an early point. For example, some combination of early incest taboos, bride kidnapping, and natural contacts across linguistically separated would have resulted in children being raised by caretakers speaking different dialects and languages (the distinction between the two is arbitrary).

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7. There is some disagreement in the literature about the role of childdirected speech (CDS) in language acquisition. Some claim that it is cultural universal (e.g. see Bryant, G. A., & Barrett, H. C. (2007). Recognizing intentions in infant-directed speech: Evidence for universals. Psychological Science, 18(8), 746–751). On the other hand, the anthropologist David Lancy contends that CDS is rare in traditional, small-scale cultures, to the extent that even nursing mothers in these cultures rarely speak with their infants at all! Lancy, D. F. (2007). Accounting for variability in mother–child play. American Anthropologist, 109 (2), 273–284. It seems likely that CDS is indeed a cultural universal (all interlocutors, not just mothers and infants naturally align their speech to some degree), but that the particular features of CDS, and the degree to which these features differ from adult language, vary across cultures. 8. Some researchers distinguish between “early childhood” (3–5), “middle childhood” (6–8), and “late childhood” (9–12). For our purposes here, it seems necessary to distinguish only between “early childhood” (3–5) and “late childhood” (6–12). 9. Clearly, this is changing, especially in modern societies where children have relatively little unsupervised time. In my own carefree childhood, growing up in a small town in New England, when not in school we freely roamed the town and surrounding fields and woods with other boys (and less frequently girls), playing games and engaging in somewhat dangerous pursuits such as climbing trees, hunting with rifles, and skating on ponds. 10. I recall, from my own childhood, nightly games of “45 Scatter” (a version of Kick the Can) in a neighbor’s backyard, played in accordance with a set of rules that would have been explained and arbitrated by older children. Other evidence for a separate childhood peer culture, at least in the English-speaking culture I was raised in, comes in the form of certain speech acts, such as “I double dare you to eat a worm.” and “I hosey [or have dibs on] the back seat” (by which a child lays claim to something that would otherwise be group property) which are typically employed only by children interacting with other children. 11. On the relationship between pedagogical tactics, strategies, and metastrategies, see Morrison, D. M., & Rus, V. Moves, tactics, strategies, and metastrategies: Defining the nature of human pedagogical interaction. Design recommendations for intelligent tutoring systems, 217.

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Suggested Reading Boyette, A. H., & Hewlett, B. S. (2017). Autonomy, equality, and teaching among Aka foragers and Ngandu farmers of the Congo Basin. Human Nature, 28, 289–322. The source of the anecdote about the Aka boy teacher at the end of this chapter. Csibra, G., & Gergely, G. (2011). Natural pedagogy as evolutionary adaptation. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 366 (1567), 1149–1157. An argument that teaching is an instinct. Also see Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in cognitive sciences, 13(4), 148–153—one of a handful of papers that inspired me to write this book. Kaplan, H., Hill, K., Lancaster, J., & Hurtado, A. M. (2000). A theory of human life history evolution: Diet, intelligence, and longevity. Evolutionary Anthropology: Issues, News, and Reviews: Issues, News, and Reviews, 9 (4), 156–185. An important paper regarding the coevolution of human life history and other important traits. Maynard, A. E. (2002). Cultural teaching: The development of teaching skills in Maya sibling interactions. Child development, 73(3), 969–982. Important evidence that children are natural teachers.

References Atran, S., & Medin, D. L. (2008). The native mind and the cultural construction of nature. Cambridge: MIT Press. Bloom, P. (2000). How children learn the meaning of words. Cambridge, MA: MIT Press. Bock, J. (2005). What makes a competent adult forager? In B. S. Hewlett & M. E. Lamb (Eds.), Hunter-gatherer childhoods: Evolutionary, developmental and cultural perspectives (pp. 109–128). Piscataway, NJ: Transaction Publishers. Bogin, B. (1990). The evolution of human childhood. Bioscience, 40 (1), 16– 25.

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Bogin, B. (1999). Patterns of human growth (2nd ed.). Cambridge: Cambridge University Press. Boyette, A. H., & Hewlett, B. S. (2017). Autonomy, equality, and teaching among Aka foragers and Ngandu farmers of the Congo Basin. Human Nature, 28, 289–322. Braun, D. R., Harris, J. W., Levin, N. E., McCoy, J. T., Herries, A. I., Bamford, M. K., et al. (2010). Early hominin diet included diverse terrestrial and aquatic animals 1.95 Ma in East Turkana, Kenya. Proceedings of the National Academy of Sciences, 107 (22), 10002–10007. Bruner, J. S. (1972). Nature and uses of immaturity. American Psychologist, 27 (8), 687. Bryant, G. A., & Barrett, H. C. (2007). Recognizing intentions in infantdirected speech: Evidence for universals. Psychological Science, 18(8), 746– 751. Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13(4), 148–153. Csibra, G., & Gergely, G. (2011). Natural pedagogy as evolutionary adaptation. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 366 (1567), 1149–1157. Demps, K., Zorondo-Rodríguez, F., García, C., & Reyes-García, V. (2012). Social learning across the life cycle: Cultural knowledge acquisition for honey collection among the Jenu Kuruba. India. Evolution and Human Behavior, 33(5), 460–470. Domínguez-Rodrigo, M., & Pickering, T. R. (2003). Early hominid hunting and scavenging: A zooarcheological review. Evolutionary Anthropology: Issues, News, and Reviews, 12(6), 275–282. Finlay, N. (1997). Kid knapping: The missing children in lithic analysis. In Invisible people and processes: Writing gender and childhood into European archaeology (pp. 203–212). London: Leicester University Press. Gärdenfors, P., & Högberg, A. (2017). The archaeology of teaching and the evolution of Homo docens. Current Anthropology, 58(2), 188–208. Garwicz, M., Christensson, M., & Psouni, E. (2009). A unifying model for timing of walking onset in humans and other mammals. Proceedings of the National Academy of Sciences, 106 (51), 21889–21893. Gros-Louis, J., West, M. J., Goldstein, M. H., & King, A. P. (2006). Mothers provide differential feedback to infants’ prelinguistic sounds. International Journal of Behavioral Development, 30 (6), 509–516. Gurven, M., Kaplan, H., & Gutierrez, M. (2006). How long does it take to become a proficient hunter? Implications for the evolution of extended

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development and long life span. Journal of Human Evolution, 51(5), 454– 470. Hawkes, K., O’Connell, J. F., Jones, N. B., Alvarez, H., & Charnov, E. L. (1998). Grandmothering, menopause, and the evolution of human life histories. Proceedings of the National Academy of Sciences, 95 (3), 1336–1339. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83. Hewlett, B. S. (1991). Demography and childcare in preindustrial societies. Journal of Anthropological Research, 47 (1), 1–37. Hewlett, B. S., Fouts, H. N., Boyette, A. H., & Hewlett, B. L. (2011). Social learning among Congo Basin hunter–gatherers. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 366 (1567), 1168–1178. Hrdy, S. B. (2007). Evolutionary context of human development: The cooperative breeding model. In C. A. Salmon, & T. K. Shackelford (Eds.), Family relationships: An evolutionary perspective (pp. 39–68). Oxford: Oxford University Press. Jones, N. B., & Marlowe, F. W. (2002). Selection for delayed maturity. Human Nature, 13(2), 199–238. Kamei, N. (2005). Play among Baka children in Cameroon. In B. S. Hewlett & M. E. Lamb (Eds.), Hunter–gatherer childhoods: Evolutionary, developmental and cultural perspectives (pp. 343–362). New Brunswick, NJ: Aldine Transaction. Kaplan, H., Hill, K., Lancaster, J., & Hurtado, A. M. (2000). A theory of human life history evolution: Diet, intelligence, and longevity. Evolutionary Anthropology: Issues, News, and Reviews, 9 (4), 156–185. Konner, M. (2005). Hunter-gatherer infancy and childhood. In B. S. Hewlett & M. E. Lamb (Eds.), Hunter-gatherer childhoods: Evolutionary, developmental and cultural perspectives (pp. 19–64). Piscataway, NJ: Transaction Publishers. Laden, G., & Wrangham, R. (2005). The rise of the hominids as an adaptive shift in fallback foods: Plant underground storage organs (USOs) and australopith origins. Journal of Human Evolution, 49 (4), 482–498. Lancy, D. F. (2007). Accounting for variability in mother–child play. American Anthropologist, 109 (2), 273–284. Leonard, W. R., & Robertson, M. L. (1992). Nutritional requirements and human evolution: A bioenergetics model. American Journal of Human Biology, 4 (2), 179–195. Levitin, D. J. (2006). This is your brain on music: The science of a human obsession. New York: Penguin Books.

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Liszkowski, U., Carpenter, M., & Tomasello, M. (2008). Twelve-month-olds communicate helpfully and appropriately for knowledgeable and ignorant partners. Cognition, 108(3), 732–739. Locke, J. L., & Bogin, B. (2006). Language and life history: A new perspective on the development and evolution of human language. Behavioral and Brain Sciences, 29 (3), 259–280. MacDonald, K. (2007). Cross-cultural comparison of learning in human hunting. Human Nature, 18(4), 386–402. Marlowe, F. W. (2005). Hunter-gatherers and human evolution. Evolutionary Anthropology: Issues, News, and Reviews, 14 (2), 54–67. Maynard, A. E. (2002). Cultural teaching: The development of teaching skills in Maya sibling interactions. Child Development, 73(3), 969–982. Mehler, J., Jusczyk, P., Lambertz, G., Halsted, N., Bertoncini, J., & AmielTison, C. (1988). A precursor of language acquisition in young infants. Cognition, 29 (2), 143–178. Meltzoff, A. N. (2007). ‘Like me’: A foundation for social cognition. Developmental Science, 10 (1), 126–134. Miller, G. (2011). The mating mind: How sexual choice shaped the evolution of human nature. New York: Anchor. Moon, C., Lagercrantz, H., & Kuhl, P. K. (2013). Language experienced in utero affects vowel perception after birth: A two-country study. Acta Paediatrica, 102(2), 156–160. Morrison, D. M., & Rus, V. (2014). Moves, tactics, strategies, and metastrategies: Defining the nature of human pedagogical interaction. In Design recommendations for intelligent tutoring systems (p. 217). Orlando: Army Research Laboratory. Pinker, S. (2003). Language as an adaptation to the cognitive niche. Studies in the Evolution of Language, 3, 16–37. Shea, J. J. (2006). Child’s play: Reflections on the invisibility of children in the Paleolithic record. Evolutionary Anthropology: Issues, News, and Reviews, 15 (6), 212–216. Strauss, S., & Ziv, M. (2012). Teaching is a natural cognitive ability for humans. Mind, Brain, and Education, 6 (4), 186–196. Strauss, S., Ziv, M., & Stein, A. (2002). Teaching as a natural cognition and its relations to preschoolers’ developing theory of mind. Cognitive Development, 17 (3), 1473–1487. Tomasello, M. (2014). A natural history of human thinking. Cambridge: Harvard University Press.

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Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral and Brain Sciences, 16 (03), 495–511. Tomasello, M., & Todd, J. (1983). Joint attention and lexical acquisition style. First Language, 4 (12), 197–211. Uauy, R., & Peirano, P. (1999). Breast is best: Human milk is the optimal food for brain development. The American Journal of Clinical Nutrition, 70 (4), 433–434.

8 Teaching and Learning as Language in Action

As discussed in the previous chapter, just as all human children are born with an instinct and capacity for language acquisition—and reaching, grasping, walking, and all the other activities that come to us so naturally—so too are we born with a natural instinct and capacity for teaching and learning through language. Our initial gift follows a roughly predictable developmental trajectory, from infancy to adulthood. We begin simply, just paying attention. We naturally take careful mental note of what our mothers and other caretakers look at, attend diligently to the sounds and gestures they make, and experiment with our own sounds and gestures. Before too long we’re beginning to use increasingly accurate approximations of these signals to direct the attention of others to objects of interest, to probe our interlocutors’ hidden thoughts, and to correct their apparently false beliefs. In this way, we begin doing our bit, without much conscious effort or intent, to acquire the critical life-sustaining knowledge and skill that humans have accumulated over hundreds of thousands of years, and later to pass along what we’ve learned to others, possibly with some small contributions of our own. But just as we develop varying degrees of skill in language performance as we grow up in our different contexts, so too do we become more © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_8

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or less skillful teachers as adults—partly, we may suppose, as a result of natural variation in cognitive capacities (some people seem to be better, more empathetic mind readers than others, more inclined to share useful information, and so forth), and partly as a result of varied cultural experiences. Further, while teaching and learning through language is quite clearly a cultural universal (a society that had completely lost its ability to teach would soon lose its culture), methods of teaching, and social organizations of teaching, have come to vary widely across cultures and subcultures around the world. In order to fully understand what teaching is, how it works, and how it varies, we need to understand it from both perspectives: as a set of natural instincts and capacities which develop over time in accordance with a biological schedule, but also as a repertoire of practices that get shaped over the same period of time by complex cultural forces and personal interests. So exactly how have teaching practices come to vary? In what ways is teaching different in different cultures? In what specific ways does the teaching children experience in Gallup, New Mexico, New Delhi, and the New Guinea Highlands differ, and how does it differ from the teaching experienced by a group of youngsters sitting around shaping rocks in a camp somewhere in Africa, some lost morning hundreds of thousands of years ago? Unfortunately, while these are good and important questions, we’re not yet in a position to answer them—neither you nor I, nor, as I write this, the scientific community as a whole. The problem is not just the lack of research evidence, but the lack of a sufficiently rigorous analytic framework that might be used to analyze whatever evidence, however circumstantial, does exist. As a result, to this point I’ve been limited to talking about teaching as a gerund (an -ing word), or a fancy noun (“pedagogy”), but in either case as if teaching were a single, unanalyzable whole. This is unacceptably superficial. It’s as if we were talking about swimming but had no way of describing the difference between the swimming mechanics of a dolphin, Michael Phelps, and your neighbor’s dog. For this reason, we haven’t been able to talk in any interesting or useful detail about the real-life, real-time mechanics of teaching, how specific practices might vary among individuals, across stages of development in individuals, across cultures, or through time, from the first

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emergence of human teaching, whenever that might have been, to the present day. We can say that humans use language to teach, and we can guess why, but we can’t say exactly how—unless we have some way of characterizing teaching as a set of communicative acts. In this chapter, I’d like to begin solving this problem for us by sketching out a reasonably precise framework for the analysis of teaching and learning through language. I’ll then show how we can use this framework to generate some specific hypotheses about how human teaching might have evolved, and how teaching practices have come to vary, in our modern world, across cultures and subcultures. In doing so, I’ll be drawing on much of what we’ve discussed in earlier chapters. (The argument gets a little technical: please bear with me.)

The Anatomy of a Teaching Episode We can start with the observation that teaching is episodic: not a continuous process, but, like all animal behaviors, something that occurs sporadically, here and there at particular points in space and time. We can call these events teaching episodes. In each instance, we ought to be able to identify a teacher (usually a single person); one or more learners; one or more communicative acts; and an object of joint attention, which may be a physical object or observable action in the participants’ field of view (a plant, an animal’s tracks, the novice’s method of aiming an arrow, a series of musical notes, an algebraic equation written on a whiteboard), or a shared idea or set of ideas (types of government, the atmosphere on Venus, the relationship between wealth and social status, even the idea of an “object of joint attention.”) Objects of pedagogical attention may be directly observed, or represented symbolically (and quite likely differently) in the participants’ minds. Typical communicative acts include directing attention, demonstrating an action, assigning tasks with a pedagogical intent (“Try bending it there…”), giving negative and positive feedback on the performance of tasks, asking questions, giving answers, requesting and providing explanations, making suggestions, expressing understanding

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(“Aha!”), and so forth. The ability to define a reasonably comprehensive set of such acts and to accurately and consistently identify and label communicative acts in a set of data (e.g., transcripts or videotapes of teaching episodes) is essential to a scientific description of teaching, and to careful discussions of teaching episodes among teachers (as in the Japanese practice of lesson study; see, e.g., Fernandez and Yoshida 2012). We can also see that teaching episodes take place within layers of context (e.g., a discussion of rules concerning a children’s game one morning in a field next to a settlement in a rainforest not far from the Amazon River), and these layers of context must also be identified. Teaching episodes may be fleeting (a teacher praising a learner’s performance of a task, “Well done!”) or, as in the case of a formal classroom lesson or online tutoring session, an episode may consist of an extended series of communicative acts, in which case we may say that the episode has a distinct beginning, middle, and end—and itself becomes a context (i.e., the tutoring session itself ) for the communicative acts that unfold within it. Borrowing from the philosopher Ludwig Wittgenstein’s theory of language as a kind of game (and given the turn-taking nature of language), we can think of the communicative acts in a teaching episode as a series of moves. Further, it’s safe and necessary to assume that the visible moves that teachers and learners make are non-random, produced more or less intentionally, by brains and minds, which are of course hidden from view. We must say “more or less intentionally” because, for example, certain communicative acts, which we can call expressives (“Aha!,” “Oops!,” “Yay!”) are, like animal cries, expressions of feeling, and thus not exactly intentional, though of course, also like animal cries, are open to suppression or exaggeration. Although you might think there’s nothing “pedagogical” in the act of expressing emotion about a mistake (“Oops!”), I’d say any act of communication in the context of a teaching episode must be treated as relevant, even if its irrelevance is what makes it interesting. Of course, this not a problem with expressions of emotion in response to sudden understanding, mistakes, or celebrations of success at assigned tasks—all of which are clearly interesting and pedagogically relevant.

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Another sense in which the communicative acts we observe in a teaching episode may be considered non-random is the way they get constrained by layers of internal context. As in a board game like chess, after the first move, each subsequent move is constrained by the preceding move, and in some way constrains the next one, to the point of checkmate—when no more possible moves remain. For example, a question constrains a cooperative participant (cooperation, at a deep level, being the name of the game) to provide an answer. Also as in a game of chess, each move in a teaching episode can be understood as reflecting a tactic , which in turn reflects a strategy. For example, shoving a pawn out two spaces in front of your queen as an opening move in chess may be taken as a tactic associated with the hidden strategy “Seize control of the middle.” In a teaching episode, asking a learner to demonstrate a skill (“Show me how you do it…”) is a tactic (single move), associated with a strategy which might be called assessment (“First assess what the learner already knows.”) A strategy may in turn be understood as reflecting a hidden metastrategy: a set of rules about which strategies to deploy, under what conditions, and in what order. In chess, part of a metastrategy might be something like “Seize control of the middle, castle, then attack from a position of strength.” A metastrategy in teaching might be something like “Assess, scaffold, assess, scaffold… until the learner has mastered the skill.” Also as in chess, it is understood that teachers and learners alike have access to different personal repertoires of tactics, strategies, and metastrategies (Morrison and Rus 2014). A critical difference, of course, is that unlike chess players, in a successful teaching episode teachers and learners both win, presumably because they are able to cooperatively align their strategies. In evolutionary time, if teachers and learners had not been able to align their strategies, I would not be writing this sentence, so of course you would not be reading it. Finally, whether successful or not, teaching episodes occur within cultural contexts, which we can call activity settings (Gallimore et al. 1993). The participants in these settings will have certain defined roles; some are fundamentally biological (as in mother–infant interactions), but most, especially in later life, are sociocultural (student–teacher, master–apprentice). For example, in a traditional, small-scale foraging

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society, activity settings may include those that occur in the base camp (children’s games, camp chores, storytelling episodes) and those that take place in the context of various foraging activities (tuber digging, honey collecting, fishing, hunting small mammals with nets or poisoned arrows). Activity settings in complex modern societies include playgrounds, classrooms, dining rooms, workplaces, and books like this one. Finally, we can say that the sum of all the activity settings in which teaching episodes can occur constitutes a learning ecosystem (also called a “learning ecology”; see Barron 2006).

Sample Hypotheses Generated by the Framework Given this framework, we can propose the following hypotheses, all of which follow from our discussions in previous chapters and are more or less testable. 1. Some teaching tactics (realized as communicative acts), are instinctual, emerge early on (in both ontogeny and phylogeny), and will be found in use by children and adults in every human culture. Examples of the former almost certainly include directing attention (“Take a look at the formula on page 10”) and giving negative and positive feedback. 2. Other tactics emerge later (in both ontogeny and phylogeny) and are specific to certain cultures and subcultures. A likely example of the latter is a pedagogical hint (“There’s something wrong with the way you’ve attached the stone to the stick…”), which we might guess is a tactic of relatively recent origin. 3. A person’s repertoire of teaching and learning moves (i.e., tactics reflecting strategies and metastrategies) will accumulate over a lifetime, as a result of different cultural experiences and increasing cognitive and linguistic capacity. 4. In any given episode, the preferred tactics, strategies, and metastrategies of teachers and learners will be more or less aligned in any given instance. Learning is more likely to occur when they fully aligned. (If

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I want you, as a teacher, to demonstrate a skill, and you want me, as a student, to first show what I can do without demonstration, we could get stuck.) 5. The total human repertoire of pedagogical tactics, strategies, and metastrategies has evolved over time, and across cultures. In any culture, teaching episodes will include some tactics, strategies, and metastrategies that are cultural universals, and some that are specific to certain cultures and subcultures. 6. Technical complexity correlates with pedagogical complexity. Successful transmission of complex technologies (e.g., pediatric neurosurgery) requires a richer set of tactics, strategies, and metastrategies, and a more complex learning ecosystem, than simpler technologies. 7. A more complex learning ecosystem spurs and enables individual innovation and cooperative development of increasingly sophisticated technologies. Throughout the chapter, I’ll be reviewing evidence in support of some (but not all) of these hypotheses, viewed through the lens of the framework as we discuss it, from the ground up. To repeat, much of the evidence draws on evidence and ideas we’ve already discussed.

Language as Action in a Complex World Let’s first go back and consider the role of individual acts of pedagogy, enacted through language, in the context of the larger narrative we’ve been building. According to this account, human language arose, in still-mysterious ways, as a central component of our species’ solution to the “hungry brain” problem. It seems that a group of human ancestors, driven by unknown environmental pressures (likely having to do with climate change), had stumbled into a new niche, a new way of getting along in the world, which eventually came to depend on a novel and unique combination and reconfiguration of traits and inclinations that must already have had a basis in the DNA and deep evolutionary history of their own ancestors. Habitual terrestrial bipedalism, unique

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among primates, at once freed hands to evolve new mechanics, including more precise grips and gestures. At the same time, increasingly efficient bipedalism provided a new way of getting around, away from the safety of trees, in the woodlands and expanding grasslands of Africa. The resulting opportunities and dangers in the new habitat catalyzed the formation of what eventually became the full package of biological and behavioral traits we modern humans have inherited. Over millions of years (a relatively small slice of evolutionary time), the emerging adaptive suite of traits came to include pair bonding and cooperative breeding, a lengthened juvenile period, a lengthened lifespan, a supersized brain (three times as large as expected for a primate of our size), and, to feed this hungry brain, a diet consisting of high-value, difficult-to-extract food sources. Early on, individual foragers would have been able to extract nutrients using technologies available to other primates, such as stones to crack open bones, nuts, and shells, and sticks to dig up roots and tubers—technologies that could be acquired (though not without some difficulty) by upcoming generations in the usual ways, through observation, individual experimentation, and just a little parental nudging. However, at some point groups of hominins must have begun to make the crucial transition to something resembling the lifestyle practiced by remaining hunter-gatherers in the world today. The transition came in the form of expanded ranges; increasingly sophisticated knowledge about the location of food sources within these ranges; accumulated knowledge about seasonal changes in the availability of these food sources; equally sophisticated knowledge about the behaviors of large numbers of predator and prey species; and the development of more complex tool sets and associated skills. Together this accumulating treasure of knowledge and skill would have become just a bit too complex for individuals to master solely through their own personal observation and experimentation, and yet too important not to be mastered. Remember the example of the mother chimpanzee not actively helping her daughter learn the art of termite fishing because she didn’t need to? These groups of hominins must have been getting to the point where they no longer had that luxury, could no

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longer completely depend on youngsters learning on their own. Something had to give. If not, further advances would be constrained by a limited inclination and ability to teach, and whatever cultural knowledge and expertise the group had managed to accumulate would not long survive the death of individual experts and innovators. It seems the breakthrough came not just through an increase in the cognitive capacity of enlarging brains, but, more specifically, through an enhanced capacity for mindreading (built from existing “theory of mind” [ToM] circuitry), accompanied by a prosocial inclination to share the contents of one’s own mind with others, Fitch’s Mitteilungsbedürfnis (“helpful chattiness;” Fitch 2010: 140). Together these and other new capacities and inclinations, growing from small but far-reaching changes in off-the-shelf primate neural circuitry, enabled, and were enabled by, a new system of communication, what was to become human language. Importantly, the emergent system, in the form of a protolanguage, was not yet a general-purpose tool for sharing abstract thought, explaining how things work, traveling back and forth in time, or debating the efficacy of one hunting strategy over another. Far from it. The ability to say something like “If you had not set fire to these grasslands our neighbors might still be at peace with us” was still in the distant future. That said, the system must have been useful for specific communicative purposes— otherwise it could not have taken root. Further, if the account we’ve been discussing is correct, some of the communicative acts the new system allowed would have been acts of pedagogy. Table 8.1 provides a list of possibilities. Here are some things to notice and think about as you look at the table. (Notice that in typing this sentence I am directly your attention to the table—that is, performing the first communicative act on the list— and notice that in typing this sentence I am drawing your attention to my having done so.) First, physical pointing (and later, symbolic pointing, as I am doing here, by making the phrase “physical pointing” my subject) is fundamental to teaching because it establishes, and refers to, an object of joint attention, without which teaching cannot take place. At the dawn of human teaching, objects of joint attention would have been visible actions and objects in the shared visual field, as they are in ontogeny,

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Table 8.1 An early teaching tool kit? Communicative act

Sample signal

Assertion Directive Question

Finger point + noun (e.g., “Leopard.”) Finger point + verb (e.g., “Dig.”) [Finger point] + question signal (e.g., puzzled look) [Finger point] + signal of negative affect (e.g., angry face) [Finger point] + signal of positive affect (e.g., smiling face) [Finger point] + alarm cry “Oops!” “Sorry!” “Okay” “Yikes!”

Negative feedback Positive feedback Expressive: Expressive: Expressive: Expressive: Expressive:

Warning Mistake Apology Reassurance Surprise

in the first prelinguistic interactions between infants and their caretakers (see Bakeman and Adamson 1984; Bruner 1974). Later, in both phylogeny and ontogeny, “objects” of joint attention have become anything that the magic of symbolic reference through language can conjure in a meeting of two minds, including, as I am now doing with you, the idea of an object of joint attention as a category. Notice how I’ve given each of the signals in Table 8.1 a label, e.g., directive, expressive, assertion, and so forth. As I’ll explain in the next section, the labels are associated with what linguists and language philosophers call speech acts (Searle 1969). For now, notice that some of these fit the “information-transfer” model of language, and teaching through language, better than others. For example, consider the act of pointing to a certain location (the first component of the signal), then making either a gesture or sound corresponding to the English word leopard (the second component). In speech act theory, this is called an assertion or, sometimes a “proposition.” Such a signal (the same signal) has at least two possible contextual interpretations, depending partly on whether the animal is visible, and partly on the recipient’s prior knowledge. 1. [pointing] Leopard. (The leopard is not visible.) 2. [pointing] Leopard. (The leopard is visible.)

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The first is clearly a way of conveying important information. The implication is that the signaler knows something the recipient doesn’t know, but could benefit from knowing. As a proposition, the assertion could be either true or false, but in either case it’s testable. If she dared, the recipient could take a walk over in the direction indicated and see for herself. The second signal has two possible interpretations. If the recipient has experience with leopards, and can be trusted to know one when she sees one, then we can fairly assume that the sender is drawing attention to the leopard for the benefit of the recipient, who might not have noticed it. Because the recipient already knows about leopards, this is teaching only in the very general sense of “informing.” But if the recipient doesn’t know about leopards, then two other interpretations are possible. The recipient (in this interpretation the learner) has heard about leopards but never seen one, or neither heard about nor seen one. In either case, we can assume that the purpose is pedagogical. In all these cases, the information-transfer model works reasonably well. But the information-transfer model doesn’t work quite as well for other signals. For example, consider a warning like “Watch out!” Warnings are different from assertions in that they don’t in themselves have propositional truth value. True, they may be based on faulty assumptions, and could conceivably be Machiavellian false alarms, but as actions, the apparent intention (assuming there is an intention) is not to assert truth, but to produce a feeling of danger and urgency in another person’s mind. In this sense, they are not unlike alarm cries produced by other animals, including dogs, birds, and nonhuman primates. In fact, we can imagine that a human protolanguage version of a warning would have been a standard primate warning cry, optionally combined with a point so as to orient the recipient to the physical location of the threat, and acoustically modulated (e.g., a more or less loud, high-pitched, or ragged cry) depending on how immediate and threatening the danger. As in the case of the assertions we’ve just discussed, warnings can have a pedagogical intent or not, again depending on the recipient’s prior knowledge. For example, if someone points at a snake and exclaims “Careful! Snake!” and the recipient already knows that snakes are dangerous, and the sender knows that the recipient knows, then the

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sender is simply calling attention to the danger. But if the recipient doesn’t yet know about snakes, and the sender knows that the receiver doesn’t know, it’s not just a warning, but an act of pedagogy. Pointing at a particular location on a rock, and combining this with a signal (either a gesture or vocalization) that signifies a striking action, becomes a directive (command), meaning something like “Hit there.” While a signal like this might be taken as transmitting the “information” that the signaler wants the receiver to strike the rock at a particular point, or that hitting the rock at this point would be a good idea, the real impact of the message is not just to convey information, but, from a position of authority, to get the recipient to perform an action. (Compare with an assertion like “If you strike the rock at that point, it will fracture and leave a sharp edge.”) Here again, the intent could be pedagogical or not. If the person hitting the rock is performing a service for the person providing direction, who could not do it herself, or as well, without this assistance, then we’d be inclined not to call this an act of teaching. But if the person providing direction is perfectly capable of performing the action herself, and the person she’s directing is an obvious novice, then we would be inclined to call it teaching. Note that in either case, the person receiving direction might learn something, regardless of the intent of the signal. Finally, as in the case of warnings, utterances like “Oops!” seem also related to animal cries, in the sense that they express the sender’s emotion, in this case a sense of mild shame at having made a mistake. When communicative acts such as these occur in the context of teaching episodes, they tell us something about the social relationship between teacher and learner, and the socioemotional nature of their interactions. Putting some of these things together, consider the following (admittedly fanciful) series of utterances. Imagine that L is practicing stone knapping, and T is providing a little assistance. T : Hit there. T : Right! T : Hit there. L: Oops! T : It’s okay. T : Hit there.

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Right. Careful! Right. Right.

You may recognize this as an example of what educational researchers call scaffolding (Wood et al. 1976), a teaching strategy in which the teacher provides just as much assistance, and only as much, as the learner requires in order to perform at a certain level. The idea that scaffolding plays an important role in teaching is strongly associated with the thinking of Russian psychologist Lev Vygotsky (1896–1934) and his notion of a Zone of Proximal Development (ZPD; Vygotsky 1978). (The ZPD is a sort of “teaching sweet spot” where the baseline is the level of performance a novice can achieve without expert assistance, and the ceiling the maximum level she can achieve even with assistance.) As we’ve already discussed, some animals seem to practice something like scaffolding, as when a meerkat mothers provides her youngster with a scorpion that she’s disabled to a degree that is roughly commensurate with the youngster’s developing ability to tackle the poisonous insect on its own (Thornton and McAuliffe 2006). Although human teachers are obviously doing something quite a lot more sophisticated when they scaffold a learner’s performance with the assistance of language, intuition suggests there’s something fundamental about the strategy. In the course of acquiring a skill, a novice may perform a task more successfully with the right sort of expert assistance and support, but only up to a limit imposed by the novice’s own developing ability. The ZPD is therefore an optimally effective target zone. Providing less or more than the necessary assistance is ineffectual, and therefore a waste of time. Given the slight energy requirements and much more substantial opportunity costs of teaching, it is reasonable to suppose that the ZPD has an evolutionary basis, and that scaffolding was an early teaching strategy. At any rate, as this example demonstrates, even a very simple protolanguage, consisting of a small set of conventionalized signals—without the need for complex syntax, phonology, or symbolic reference—could have enabled an early form of teaching. A toolkit such as that suggested by the list in Table 8.1 could plausibly have emerged, with a little

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tinkering, from the methods of communication, and underlying cognitive systems, that would have been available to the last common ancestor of chimpanzees and humans. Taken together, a system of conventionalized signals such as these would have given its users a way of directing each other’s attention to matters of interest in the surrounding world. It would have allowed them to establish mental links between things (objects and actions) and signals (vocal and/or gestural) representing these things. The system might have allowed them to ask questions and give answers. In any case, cultural knowledge and skill could begin to be shared, communicated, and transmitted from experts to novices, and from one generation to the next. Importantly, as we’ll see, the hypothetical toolkit in Table 8.1 includes many of the same communicative acts observed by anthropologists studying teaching and learning in remaining small-scale hunter-gatherer groups (e.g., see Hewlett and Roulette 2016). The same tactics have been observed in use by infants (Tomasello et al. 2007), and by children teaching other children in Western societies (Strauss and Ziv 2012). Moreover, as our own research shows (Morrison et al. 2015) all of these moves (including the expressives) occur with high frequency in transcripts of twenty-first-century online tutoring sessions, interspersed with many other moves (such as hints) which are almost certainly culturespecific, and of more recent origin. Such findings are clearly consistent with the hypothesis that some pedagogical moves are cultural universals whose early emergence in ontogeny, and lasting utility, seems to reflect a deep evolutionary history.

Evolution of Teaching Tactics, Strategies, and Metastrategies At the beginning, of course, say 2–3 million years ago, the original language of teaching would have been only as complex as necessary to begin helping learners acquire the crucial foraging skills and technologies that had grown just a bit too difficult to acquire through observation of expert practice and personal trial-and-error learning alone. A simple set of communicative acts that could be used for practical purposes—such as

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directing attention, providing negative and positive feedback, and giving names to people and things—might well have been sufficient. The lack of capacity for full symbolic reference would have limited teaching to the here and now, without reference to past or future events, or to causal or conditional relationships. In other words, it is fairly easy to imagine a point in the evolution of language, and teaching through language, when it would have been possible to direct attention to a swimming fish, possible to give the fish a name, but not possible to explain how to catch it without a physical demonstration, or tell a story about catching a fish too big to land. A time when it would have been possible, through negative and positive feedback, to communicate whether a stick was the right size and length for termite fishing, but not why. A time when it would have been possible to switch back and forth between scaffolding (letting the novice to do the work and giving only as much help as necessary), and demonstrating how to do the work yourself—but not possible to tell a story about how you had learned the same skills from your own mother so many years ago. But, although the system would have been limited to indexical reference to objects and actions in the here and now, it would have been openended. Any important physical action—digging, hammering, reaching, throwing, carrying—could be mimed, or given a conventional name, and, as such, could become a directive. Any tool or plant or animal, or other members of the group could also be given a name (possibly as a conventional gesture), and through pointing, attention could be directed to these things, and so now the names of actions and things could be taught and learned in context. In other words, as limited as an early protolanguage would have been, at some point many of the basic structure and building blocks of teaching through language, the same ones we use today, would have fallen into place. Certain conventionalized combinations of signals—including gestures, facial expressions, changes in posture, and vocalizations—could now serve as communicative acts with specific functions, and therefore would have provided a small but useful teaching repertoire. Teachers and learners could now play the basic game, selecting from among a small set of possible moves (tactics) associated with a limited set of strategies (e.g., physical demonstration and scaffolding), and they could

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switch back and forth between strategies, in accordance with a simple metastrategy. Though decidedly primitive compared to the much more sophisticated arsenal of tactics and strategies available, millions of years later, to teachers and learners in our twenty-first century, the emerging system would have made it just a bit easier, and with little invested effort, for relative experts to pass along crucial cultural knowledge and skill to relative novices, and that would probably have been sufficient. Tomasello’s “ratchet” was now in place, reducing slippage just enough to make cumulative culture possible. Although it’s tempting to call this early mechanism for cultural transmission “prototeaching,” the term is misleading in that it suggests some qualitative difference between human teaching as it was then and as it is now. In fact, arguably the more profound qualitative difference was between the types of teaching other animals are capable of (e.g., teaching by “social tolerance” or “opportunity provisioning”; Kline 2015), and this unique new capacity and inclination for teaching through communicative acts as a joint attentional activity. Furthermore, it seems clear that the same tactics, strategies, and metastrategies that would have been available to hominins in the ancestral environment, in the early dawn of teaching and learning through language, have been conserved (because they work), and are still in use today. Teachers in the twenty-first century still use their fingers to draw attention to, and name, objects of interest; they physically demonstrate skills; attend closely to a learner’s performance; give positive and negative feedback; and switch back and forth between demonstration and scaffolding. They warn of danger (“Careful! Those scissors are sharp.”) and work to build a sense of affiliation and personal rapport by communicating reassurance (“That’s okay.”) and praise (“Nice job!”). But, in addition to these basics, many twenty-first-century teachers have access to a cultural repertoire of tactics, strategies, and metastrategies that would almost certainly not have been available to our early ancestors, but have instead evolved over the millennia under pressure for the more sophisticated methods of instruction required by more complex technologies, and enabled by increasing cognitive capacity and increasingly complex forms of language.

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In Fig. 8.1, I try to give a sense of how a set of core strategies for teaching and learning through language might have evolved over time. (Please forgive the blatantly anachronistic use of modern English equivalents.) As discussed, it’s plausible that four of these strategies— rapport building, demonstrating, scaffolding, and naming/telling —would have been available from the first emergence of a protolanguage, perhaps some 2 million years ago, or even earlier. A fifth strategy, storytelling, growing out of simple telling, likely came later, possibly much later. If we think of it in the form of simple miming (“proto stories”), storytelling could conceivably have emerged along with a protolanguage. But if we think of it as an artful, polished, and dramatic accounting of the who, what, where, and when of past episodes, with colorful characters, surprising plot turns, and unforeseen conclusions, a capacity for storytelling would have had to wait for, and possibly coevolved with, the capacity for speech and symbolic reference. I put it, arbitrarily, at Explaining Why? Because… Storytelling Yesterday…. Naming/Telling That’s a leopard. What’s that? Scaffolding You try. Right. Not quite. Demonstration Watch me. Rapport building Oops! Sorry. It’s okay. Nice job! 2

1.75

1.5

1.25

1

.75

.5

.25

0

Millions of years before present

Fig. 8.1 Hypothetical evolution of six core teaching strategies over three different time periods

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1 million years ago, the halfway point between a protolanguage and language in its modern form, which, as discussed in Chapter 5, must have emerged no later than some 100,000 years ago, prior to the final migration out of Africa. Finally, explaining, which entails various forms of higher-order relational reasoning (and might well have grown out of storytelling, e.g., as a way of establishing causal linkages between events), likely came even later, perhaps not until the emergence of Homo sapiens, no earlier than 300,000 years ago. The key idea here is that a limited repertoire of conventionalized communicative acts (associated with at least the first four strategies, from bottom up) could have emerged nearly simultaneously, quite early on, building on existing primate communication and cognitive capacities, and under pressure for an enhanced system of communication in support of cooperative foraging. Once established as part of the culture, and thus becoming part of the environment (along with new predators, food sources, and other gene selectors), the protolanguage would have begun to select for individuals with brains that were just a bit better at using it, and in this way, teaching and learning through language became biocultural. The snowballing effect, however, would have been limited, constrained partly by the relatively glacial pace of evolution, but also by cost–benefit trade-offs. Simply put, the capacity and inclination for teaching and learning through language—even without speech, complex syntax, and symbolic representation—might well have grown adequate to our ancestor’s immediate needs fairly quickly. In a sense, good enough was perfect, and no further evolution would have been necessary, until some new environmental pressure emerged. This would explain, for example, the long periods of stasis in human evolution (in both cranial capacity and technology) identified with the Oldowan tool industry (from about 2.5–1.5 mya) followed by the Acheulian (from 1.5 to 0.5 mya). In these and other such cases, the equilibrium would have been upset by environmental threats to survival. In these trying times, nature had more to work with, in the existing structures and wiring of language-using brains, so surges in the capacity to teach through language became possible, if not inevitable.

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Looking Ahead As we’ve discussed so far, human teaching and learning through language, a species-unique social behavior, is the primary mechanism whereby humans have passed along hard-won, life-sustaining knowledge and skill from experts to novices, and from one generation to the next, down through the millennia. Exactly how a small population of newly bipedal but otherwise ordinary apes, eking out a precarious existence on the edge of the receding jungles and expanding woodlands and grasslands of East Africa some 6–10 million years ago, gave rise to the extraordinary species that currently inhabit, and threaten, nearly every patch of Earth’s surface remains a great mystery. We may never fully understand what happened, but it seems better to speculate and be only partly right than to ignore the problem altogether. Based on a growing body of admittedly circumstantial evidence, it’s become reasonable to suppose that under heavy (“hungry brain”) pressure for a system of communication capable of supporting cooperative foraging and transmission of increasingly complex knowledge and skills to offspring, groups of our hominin ancestors began to adopt sets of conventionalized signals that could be used to convey information and influence the behavior of other group members in specific, predictable ways. Long before the emergence of articulated speech, massive symbolic representation, and recursive syntax, these early communicative acts likely involved simple combinations of gestures (notably finger points), facial expressions, and vocalizations such as alarm cries, agonistic signals (threats of violence), and reconciliatory signals (e.g., cooing). Together these came to constitute a small, essential repertoire of signals that could be intentionally deployed to accomplish specific purposes. Importantly, these did not require or involve a specialized “teaching language.” Rather, the emerging protolanguage could be used in support of cooperative foraging (“Gazelle carcass that way.”) and acts of pedagogy (“What’s that?” “That’s a gazelle.”) Following this line of thinking, and in recognition of the need for a suitably granular analytic framework, in this chapter I’ve argued that episodes of teaching and learning through language are best understood as consisting of one more moves, instantiated as communicative acts (also

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called speech acts or dialogue acts), relevant to some object of joint attention. As in a game like chess, each move may be understood as a pedagogical tactic, selected in accordance with a strategy, which is in turn selected in accordance with a metastrategy. Participants in a teaching episode each have an understanding, which is more or less shared, of the game and how it works. They understand that certain moves make sense at different points in the game, and feel constrained (though not entirely obligated) to respond to particular moves in expected ways. For example, a question is understood to deserve an answer, a novice’s successful performance of a task invites an expert’s praise, incorrect performance invites comment, and perhaps a shift in strategy, say from scaffolding to demonstrating. Successful episodes of teaching and learning occur when the participants’ tactics, strategies, and metastrategies are at least roughly aligned. Importantly, teaching and learning through language has an evolutionary history, which may also be reflected in ontogeny. Certain core strategies—likely including rapport building, demonstrating, scaffolding, and telling—are almost certainly ancient, arguably dating back to the emergence of a protolanguage, possibly millions of years ago. These strategies are also among the first to emerge in interactions between infants and their caretakers. Others, including storytelling and explaining, likely emerged later (as they emerge later in ontogeny). Because these strategies are effective, and because human brains have been shaped by and for their use, these core strategies persist to this day, in the minds and behavior of teachers and learners in every human culture. Other strategies, such as assessment, and metacognitive support (“This next problem is challenging, so let’s be patient with each other…”) must have arisen much later and are specific to certain cultures and subcultures. Because teaching and learning practices are shaped by culture and vary in more or less predictable ways across cultural contexts, a scientific account needs a way of describing these contexts. Here, I’ve proposed two: the related notions of an activity setting, and a learning ecosystem (more precisely, and awkwardly, a “teaching and learning ecosystem”). These two constructs are defined in respect to learning episodes, and to each other, and may be applied at different levels of analysis. Briefly put,

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teaching episodes occur within activity settings, and the sum of activity settings available to learners in a given cultural context constitutes a learning ecosystem. Depending on the level of analysis, a classroom, school, town, city, country, Internet chat room, and indeed any inhabited region of the world available to human learners may be viewed as a learning ecosystem. For most of human history, teaching episodes would have occurred in the context of daily social interactions: interactions between infants and their caretakers, in the context of children’s games and chores, shared meals, tool manufacture, hunting and gathering expeditions, and (I suppose much later on), in the context of storytelling, and other communal activities and rituals. In more recent times, activity settings have come to include these ancient ones, but also school and university classrooms (both physical and virtual), afterschool programs, chat rooms museums, labs, factories, hospitals, movie theaters, and any number of other such settings. As a result, learning ecosystems have become significantly more complex, reflecting the dramatically increased complexity and variety of cultural knowledge, technology, and technical expertise that sustains our still-fragile existence on the planet. In this regard, two developments have been especially consequential. First, the emergence of literacy some 5000 years ago, followed by the invention of printing just a little fewer than 600 years ago, has created a powerful new kind of activity setting in which a writer like me, playing something like the role of a teacher, can carry on something like a conversation (though a lamentably one-sided one) with a reader like you. Second, the emergence of digital technologies, in particular the global Internet, has spawned a vast new learning ecosystem, giving teachers and learners everywhere in the world access to potentially powerful and effective new ways of teaching and learning from each other. In the final three chapters of the book, I want to talk about each of these developments in turn. In Chapter 9, the next chapter, we’ll discuss a special use of language I’ll be calling civil discourse, with a particular focus on what it means to “know” something is true (epistemic understanding ), and certain ways of structuring thought, and sharing thoughts with others, which I’ll be calling epistemic forms. This will finally get us away from thinking about teaching and learning, through language, as an

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activity in which interlocutors play the role of either teacher or learner; instead, we’ll be thinking about intelligent civil discourse as an activity in which participants learn from each other, and from the discourse they build together cooperatively, in search of deeper understanding of the world, how it works, and how it might be made better. In Chapter 10, a continuation of Chapter 9, I describe how the capacity for engagement in intelligent civil discourse develops in individuals (ontogeny), and how it may also have emerged in our species (phylogeny), culminating some 65,000 years ago with the crossing of the Timor Sea, likely by boat, and the subsequent colonization of Australia. Finally, in Chapter 11, I ask us to consider how our species-unique capacity and inclination for teaching and learning through language is, ironically, what has gotten us into our present predicament. We’re the only species on Earth that has managed, through our advanced technologies—petroleum-based fertilizers, internal combustion engines, nuclear reactors, bombs, other weapons of mass destruction, and the like—to put not only our own existence at great risk, but also that of so many other of our fellow creatures. And just as teaching and learning, and intelligent civil conversation, have gotten us into this predicament, so, I argue, they remain the only way out. If we are ever to solve the complex problems that face us, our institutions of public education and other mechanisms for cultural transmission will need to change, but can only change from within, through the efforts of individual teachers like you, me, and other workers in our human family business.

Food for Thought 1. 2. 3. 4. 5.

What does it mean to say that teaching is “language in action?” Why is pointing important in teaching? Is it possible to teach without an “object of joint attention?” Can you think of anything to add to Table 8.1? What is the chief argument for the presence of cultural universals in teaching (universal tactics, strategies, and metastrategies)?

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Suggested Reading Austin, J. L. (1965). How to do things with words. New York: Oxford University Press. Along with John Searle’s Speech acts: An essay in the philosophy of language (Searle 1969), this is basis for the idea that language is a way of getting things done with others—not just a way of transmitting information. Morrison, D. M., & Rus, V. (2014). Defining the nature of human pedagogical interaction. Design Recommendations for Intelligent Tutoring Systems: Volume 2-Instructional Management, 2, 217. The paper in which Vasile Rus and I first laid out the taxonomy (tactics, strategies, metastrategies) presented in this chapter. Strauss, S., & Ziv, M. (2012). Teaching is a natural cognitive ability for humans. Mind, Brain, and Education, 6 (4), 186–196. An important paper, and a major influence on the thinking behind both this chapter and the previous one. The paper is especially useful for what amounts to a meta-analysis of teaching strategies (I might prefer to call some “tactics”) used by children, as reported in more than twenty different research papers. If you only read one of the papers on this short list, I suggest you make it this one.

References Austin, J. L. (1965). How to do things with words. New York: Oxford University Press. Bakeman, R., & Adamson, L. B. (1984). Coordinating attention to people and objects in mother-infant and peer-infant interaction. Child Development, 55, 1278–1289. Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development, 49 (4), 193–224. Bruner, J. S. (1974). From communication to language—A psychological perspective. Cognition, 3(3), 255–287.

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Fernandez, C., & Yoshida, M. (2012). Lesson study: A Japanese approach to improving mathematics teaching and learning. New York: Routledge. Fitch, W. T. (2010). The evolution of language. Cambridge: Cambridge University Press. Gallimore, R., Goldenberg, C., & Weisner, T. S. (1993). The social construction and subjective reality of activity settings: Implications for community psychology. American Journal of Community Psychology, 21(4), 537–559. Hewlett, B. S., & Roulette, C. J. (2016). Teaching in hunter–gatherer infancy. Royal Society Open Science, 3(1), 150403. Kline, M. A. (2015). How to learn about teaching: An evolutionary framework for the study of teaching behavior in humans and other animals. Behavioral and Brain Sciences, 38, 1–71. Morrison, D. M., Nye, B., Rus, V., Snyder, S., Boller, J., & Miller, K. (2015). Tutorial dialogue modes in a large corpus of online tutoring transcripts. In Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), pp. 722–725. Springer International Publishing. Morrison, D. M., & Rus, V. (2014). Defining the nature of human pedagogical interaction. Design Recommendations for Intelligent Tutoring Systems: Volume 2-Instructional Management, 2, 217. Searle, J. R. (1969). Speech acts: An essay in the philosophy of language. Cambridge: Cambridge University Press. Strauss, S., & Ziv, M. (2012). Teaching is a natural cognitive ability for humans. Mind, Brain, and Education, 6 (4), 186–196. Thornton, A., & McAuliffe, K. (2006). Teaching in wild meerkats. Science, 313, 227–229. Tomasello, M., Carpenter, M., & Liszkowski, U. (2007). A new look at infant pointing. Child Development, 78(3), 705–722. Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17 (2), 89–100.

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To this point, I’ve been encouraging us to think about teaching and learning as a uniquely human activity in which a relative expert goes out of her way to help a novice acquire some new component of useful knowledge or skill, largely through the medium of language. “That’s right. Hit it there. Good. Now what? Right. Hit it there.” This is not to say that we do not also learn just as other animals do: by observing and copying the expert behavior of others, and by interacting with the world around us on our own, keeping note of our successes and failures for future reference. As we’ve seen, that’s how young chimpanzees learn to use sticks to fish termites out of termite mounds. They watch and copy, possibly with a little nonverbal assistance from a big sister. But, as technologies become more complex—not just sticks and stones, but sharpened stones attached to sticks, and woven nets, and outrigger canoes with sails—there comes a point when the “watch and copy” strategy doesn’t work so well. Take the case of tying shoelaces. Yes, you might be able to work out all twelve steps or so on your own by watching someone else, especially if someone is willing to demonstrate slowly. But it helps if your teacher combines the demonstration with some verbal instructions: “Make a little bunny ear, like this. Good. Now © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_9

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make a hole. Right. Now make another bunny ear…yup…now push that bunny ear through the hole and pull it tight. You did it!” Language, as I have argued, likely arose at least partly under selection pressure for an enhanced signaling system that could be used in just this way, to pass along important knowledge and technical skill from experts to novices, and from one generation to the next. This is classic teaching: Experts using language to help novices acquire new knowledge and skill more easily and efficiently than they could if left to their own devices. Now, it may seem a little late to admit this, but I’m afraid we haven’t paid sufficient attention to the fact that humans can learn from each other in the context of collaborative work, especially from the talk associated with that work. In other words, we learn not only from experts, in our role as novices, but also in the context of talk with our peers: family members, friends, and teammates. Teachers also learn from their students, and parents from their children’s interesting questions about the world, what things are, how they work, and how they might be made to work better. And not only do we learn about the world around us in this way, we also learn how to think. To put this formally, an account of teaching and learning through language is incomplete without some attempt to explain the relationship between collaborative cognitive work and the accumulation of cognitive capital in both individuals and groups, through both evolutionary and historical time. More generally, we can say that language is the mechanism through which humans have come not just to share information, and not just to teach, but also to take part in productive thinking with other group members, a form of taskembedded discourse. In this sense, we’re like a colony of social insects, whose intelligence derives from the collective intelligence of the colony as a whole. (As we’ll see, because their collective cognitive work is fundamentally instinctual, insects are in some sense better at this than humans, who must learn how to think together with others, and do not always do so, or at least not as well as we might.) To illustrate, here’s another story. Back in the 1990s, sitting on a subway train in Boston, somewhere between Park Street Station and Lechmere (i.e., on the Green Line), a section on which, as you may know, the wheels screech terribly as the cars get tugged around bends, I overheard two quite different conversations, both between young boys and

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their mothers. Some twenty-five years later, I recall the content clearly, if not the exact words. One conversation went very much like this: Boy A: Mommy why do the wheels squeak like that? Adult A: I think it’s from the friction. They probably need to oil the wheels.

Nearby, a fidgety boy was twirling around a stainless steel pole near the center of the car, treating it like a piece of playground equipment. Adult B: Stop that! Sit down! Boy B: Why? Adult B: Because I said so.

I wonder what you’ll be thinking as you compare the two exchanges. I remember thinking the first mother had it wrong. True, the horrific screeching must have been caused by the metal wheels of the train rubbing against the metal tracks as it rounded the bend, producing the high-pitched screeching sound so troubling to our ears. But do workers regularly oil the wheels, or tracks, or both—then forget to do that, or put it off, like leaving dirty dishes in the sink? And if oiling the wheels is a regular maintenance practice, wouldn’t that leave messy puddles of oil around the tracks, possibly creating a fire hazard?1 But I was more concerned, as you may be, about the second exchange, and differences between the two. They are indeed striking. In the first case, it’s the child who initiates the exchange. The noise is unpleasant. He must have figured out on his own that the train’s wheels were to blame, but why? Most wheels— wheels with rubber tires, for example—perform their jobs quietly. Why are these wheels so different? Surely his mother will know. As it turns out, she doesn’t know for sure, but she’s willing to guess. She offers a tentative explanation (“I think it’s from the friction…”), construes the situation as a problem, and suggests a possible solution. In this way, through the remarkable affordances of symbolic language, the child and adult demonstrate an ability to think together about an object of joint attention in the world; to treat it—the noise—as a phenomenon with an identifiable root cause; and as the mother suggests, as a problem with a possible solution.

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The second exchange is markedly different in several crucial ways. For one thing, note that it’s the adult, not the child, who initiates the exchange. Her double-barreled directive (“Stop that! Sit down!”) is unadorned with any politeness marker (compare: “Please sit down honey”). Like the first boy, the second boy requests an explanation (“Why?”), but in this case the mother’s explanation consists simply in stating her right to tell her son what to do. The whole exchange, it’s hard not to think, is an exercise in power, not intellect. Both sets of participants are clearly “doing things with words” (Austin 1962), but in this second example, the mother is deploying her power of language purely for the purpose of controlling her son’s behavior. Notice also that in both conversations, the adult’s status as an authority figure comes into play. In the first, the child seeks an opinion from a trusted authority, who then takes pains to qualify her answer— she thinks, but is not sure, that the noise is caused by friction. In the second case, the mother wields her authority without qualification or explanation. There may indeed be good reasons why the child ought to sit down, but these are not provided. Both uses of language serve legitimate purposes, but only the first aims to deepen understanding. What shall we call this first kind of talk? I suggest we call it civil discourse.

Civil Discourse Defined Although you might think of civil discourse as just a kind of “polite talk,” I’d like to suggest that we think of the “civil” part as the same civil in the word civilization, and, as such, that an ability and inclination to engage in civil discourse with others is an important part of what it means to be civilized . Civil discourse is what has made our human civilization possible. Civil discourse is what enables humans, alone among animals, to think together for the common good. (I’ll give a more technical definition in a later section.) Now, you might think it odd that I give a simple exchange between a mother and her young son about why subway wheels make screeching

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sounds as an example of civil discourse—as if it fell into the same category as talk among adults at a town meeting about whether to pass an ordinance against smoking outside public buildings. I’m going to argue that the first conversation is indeed an example of the sort of productive, problem-solving, truth-seeking discourse we may hope to hear in jury rooms, legislative chambers, hospitals, churches, family dining rooms, air traffic control towers, research labs, school rooms, and anywhere else that humans congregate to do important things with words for the collective benefit. Civil discourse, I want to argue, is a way of sharing thinking with others—the human brain’s best and most important work. Civil discourse is indeed civil in the sense of being polite, but the civility is conditioned not just by superficial social norms, but by the underlying need to build, maintain, and affirm affiliations with fellow thinkers, for the common good. The sort of civil discourse I am thinking of is an exercise in human intelligence and, as such, is rational and in a sense “scientific.” It is also “civil” in the sense of being rational discourse among civilians—not just scientists and more-or-less polite intellectuals, but all members of human groups whose collective thinking, through the millennia, has produced and maintained civil society. Civil discourse, understood as thinking aloud with others for the common good , is, and has always been, the lifeblood of human civilization, from the time that human ancestors first began to live in cooperative groups, up to the present day. As such, we may say that civil discourse is as old as language itself.

Civil Discourse and Teaching So what does this have to do with the evolution of teaching? Returning to the subway car conversations, it’s not hard to think that the first mother was doing something much like teaching, possibly even intentional teaching, but she wasn’t lecturing, or even “conveying information” in the guise of an expert. She may not even have thought of herself as teaching; it’s quite possible she was just doing her best to answer her child’s reasonable question. In contrast, it’s easy to say that the second mother was not teaching. If, in response to her son’s question, she’d

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explained she was worried he might be hurt, that he was calling too much attention to himself, distracting other passengers, causing her embarrassment, or some other explanation, we might give her some credit for teaching through civil discourse, especially if her son could then transfer the lesson to other situations. But the salient feature of this mother’s way of talking was not so much her unwillingness to teach as her unwillingness to engage in, or be drawn into, civil discourse. Yes, like alarm cries in other animals, human language is useful for controlling the behavior of others for their own benefit, and mothers certain have a right, if not an obligation, to use language for this purpose. But if the mothers of our ancestors had used language only to discipline their children or keep them from harm, you couldn’t possibly be reading this sentence, or thinking whatever thoughts this sentence inspires. Civil discourse, I’m going to argue, has been the primary mechanism through which our ancestors have built new forms of knowledge and skill, and transferred the resulting cognitive capital within groups, from one group to another, and from one generation to another, for hundreds of thousands (if not millions) of years. But, as the second mother demonstrated, civil discourse is apparently not instinctual.

Civil Discourse and Epistemology I’ve chosen to use the term civil discourse over other possibilities (e.g., “rational discourse,” “productive discourse,” “intelligent talk”) that occurred to me for two reasons.2 First, because I find myself writing at a time when civil discourse—as the term is generally used and understood—seems to be going out of fashion, too often elbowed aside by divisive political rhetoric, sermonizing, insult, and empty bluster. Second, and more importantly, because I want to emphasize the crucial role that this form of language use has played, over hundreds of thousands of years, in the emergence of human civilization. It is not just that we have the potential to engage in civil discourse because we are products of, and are fortunate to inhabit, civilized societies. Rather, we inhabit civilized societies because, millions of years ago, our distant ancestors

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somehow became capable of engaging each other in a certain kind of talk aimed at gaining a mutual understanding of how the world works, and how we can best make our way in it—overcoming its dangers and exploiting the opportunities it offers—and they exercised this capacity to the full. If they had not, we would still be sitting around in caves eating half-cooked bats.3 Now, in using the term civil discourse, understand that I don’t refer only to conversations between intelligent, “civilized” people. True, the ability to engage in civil discourse is a sign of civilized intelligence. But we can safely assume that all but the most cognitivelychallenged humans, regardless of their general intelligence (as measured, for example, by an IQ test), are fully capable of engaging in intelligent civil discourse and may do so, more or less regularly, from an early age, as so many of us have done for thousands of generations. Second, I don’t mean to imply that the content of civil discourse must be especially sophisticated, or even that it needs to make total sense. For example, here’s a snippet of a conversation between my grandson Isaac—at this point age three years, six months—and his grandmother: Grandson: [demonstrating the function of a toy truck] The smoke comes out from the ground…and it [shooting sound] flies away. Grandmother: Why does it fly away? Grandson: Because the gravity goes on the ground and it’s shooty gravity.4

As we’ll see, this exchange has all the features of civil discourse. Here’s a working definition: Civil discourse is a special kind of talk in which participants, through a series of back and forth discourse moves, work cooperatively to construct new knowledge or belief in each other’s minds, drawing on a shared epistemology—including conventionalized ways of organizing knowledge about the world, justifying beliefs, and engaging in discussions about these beliefs with others.

In the example I’ve given here, Isaac makes an assertion about how the toy truck works and how the smoke (exhaust?) comes out from the ground beneath it. Rather than simply acknowledging this assertion (“Oh…”), his grandmother asks why the smoke flies away. Rather than

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simply asserting his right to make such an assertion, or claiming it’s just the way things are (“Because it does…”), Isaac does his best to explain (“…the gravity goes on the ground and it’s shooty gravity.”). In this way, the grandmother and grandson demonstrate a shared epistemology (way of knowing), which, it seems, includes a shared assumption that phenomena in the observable world have causes which are open to explanation.5 In accordance with this shared epistemology, they cooperate—as best grandmothers and their three-and-a-half-year-old grandsons can— to construct a theory, or at least the beginning of one, about the behavior of smoke. We have several important ideas to unpack here, and several important questions to pursue. For one thing, notice that civil discourse, as I’ve defined it, is not just a matter of “conveying information” or “knowledge” accurately from signaler to receiver, from expert to novice. Rather, as in the case of most human discourse, it’s a matter of joint action, a way of getting things done with words—in this case the construction of an explanation that makes at least some sense to both parties. Second, as joint action, civil discourse depends on the cognitive cooperation of both participants, a matter not only of getting things done, but of agreeing to think together with words. Third, as a kind of cognitive work, civil discourse is jointly understood (in its ideal form) as having a constructive purpose or goal—to create new knowledge or understanding in the minds of participants for their mutual benefit. Bear with me again as I introduce some technical language. First, it’s clear that this cognitive work is accomplished with, and enabled by, a shared set of cognitive tools for constructing and organizing knowledge—what we can call an epistemic toolkit —and a shared sense of how to use these tools to do the work.6 As I’ll explain, these tools include a library of epistemic forms (Collins and Ferguson 1993; Morrison and Collins 1996), some of which (I’ll call them epistemic primitives) are shared by other thinking animals, and some of which are uniquely human, the result of our unique ability to symbolize concepts through the affordances of language. Each of these epistemic forms is associated with a set of epistemic discourse moves and shared rules governing the use of these moves. For example, when his grandmother asks Isaac why the

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smoke flies away, she’s employing an epistemic form I’ll call a cause-and effect model . The related discourse move is a request for an explanation, a standard move in civil discourse. As in a board game like chess, discourse moves are rule governed. If I ask you why metal wheels on metal tracks screech when going around a bend and you say “Because they do,” I will feel that you’re not playing fair. Close attention to the rules of epistemic discourse and a disposition to guard against and point out potential violations may be called epistemic vigilance (Sperber et al. 2010).7 Finally, we can say that epistemic fluency (Morrison and Collins 1996) is the ability to employ a large number of epistemic forms, to fluidly make and respond appropriately to many different kinds of associated moves, and, through careful attention to these moves, to exercise epistemic vigilance. So, going back to the subway conversation, when the first boy asked his mother why the wheels were screeching, he was making a standard move (“Why…”) in what we can call the “cause and effect game.” In proposing an explanation, his mother demonstrated her understanding of the game and her willingness to participate. In qualifying her response (“I think it’s from the fiction…”), she was demonstrating her sensitivity to the demands of epistemic vigilance, acknowledging the limits of her own knowledge and her understanding of her role as a co-constructor of knowledge, not its ultimate source. Her subsequent suggestion about a possible solution to the problem (“They probably need to oil the wheels…”) signals a move to another epistemic form, which we can call problem-solution. Note that her proposed solution is again qualified—the maintenance crew “probably” needs to oil the wheels. When the other boy asked his mother why she told him to sit down, we may think he was demonstrating his own sense of epistemic vigilance, in this case an understanding that a directive can be considered to deserve an explanation and might otherwise be considered unreasonable. In her response (“Because I said so…”), his mother demonstrates that, at least at this particular place and time, she does not share her son’s epistemic stance. While arguably a parental prerogative, her unwillingness to play the game and attempt an explanation shoves the exchange outside the realm of civil discourse.

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Now, this way of thinking raises a number of questions related to issues we’ve discussed in previous chapters. At what point, for example, did a capacity for civil discourse first emerge in human evolution? How, why, and for what purpose did it emerge? To what extent is civil discourse biological—that is, a cultural universal among humans—and to what extent does it vary across cultures? What were the biological precursors? How does the capacity for civil discourse develop in individuals? Why do some people seem more willing and able to engage in civil discourse than others?

Some Important Epistemic Forms It will be easier to begin developing some tentative answers to these questions if we have some sense of the contents of the epistemic toolkit humans use, through language, to construct, organize, and share knowledge of the world and how the world works—not just how it seems to be, but how it really is—to the extent we can figure that out. As I’ve said, we can think of the toolkit as (a) a set of epistemic primitives, based on precursor, brain-based cognitive structures shared by many thinking animals—including our own most distant ancestors— and (b) highly flexible ways of combining these primitives, through language, into higher-order, distinctly human ways of thinking. Over time, in other words, our capacity for civil discourse has emerged, riding the coattails of language, from simpler, prelinguistic structures found in off-the-shelf vertebrate brains, which have been conserved and enhanced in off-the-shelf primate brains. For example, chimpanzees and other nonhuman primates can easily recognize individuals, recall the behavior of individuals during past encounters, recall specific locations of these encounters, and, putting these together, can recall the “who, what, when, and where” of past episodes.8 Only humans, however, can convey this kind of information to others, through language, by telling stories, a uniquely human epistemic form. Since I’m about to walk us through a partial list of epistemic forms, it may be helpful to begin by thinking about the nature of a list , itself an epistemic form. Consider the following:

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Epistemic forms? 1. list 2. object 3. action 4. agent 5. category 6. place 7. personhood 8. map 9. timeline 10. plan 11. cause-and-effect model 12. eggplant 13. story 14. theory 15. problem-solution. As an epistemic form, a list is an ordered set, and the items in the set are assumed to belong to the same category (see below), and thus ought to have at least one feature in common.9 When written out, the items on a list are often numbered for convenience of reference, as in my example. A list may be comprehensive (e.g., the Ten Commandments, the fifty states of the United States) or representative. The items may be presented in random order, sorted alphabetically, or in ascending or descending order of importance, or in accordance with some other such scheme. Now, here’s an interesting thing. I believe, but unfortunately have no good way of testing this belief sitting here alone in front of my computer, that I can anticipate what you’ll be thinking as you look over my list. You will be wondering, I predict, whether I intend this list to be representative in any important way, and why I’ve chosen to list the items in this particular sequence. You may also be wondering what I mean by some of these terms, and might wish you could ask me to define them (“What exactly do you mean by ‘person?’” “How is a ‘person’ different from an ‘agent?’”). You might want to argue that one or another of the items doesn’t really belong on the list—that I’ve committed a category error (“‘Eggplant?’ Really?”). I’ll bet you’ve also begun thinking, as I have,

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about items I’ve not included here. For example, what about prediction? Or mechanism? Or analogy? Right or wrong, my sense that I can at least guess at your thoughts as you look over my list is remarkable, the kind of mindreading that makes civil discourse possible, and that has made it possible for me to write this book with your thoughts in mind, and for you to read, predict, and hopefully understand, my thoughts. The reason I think I can predict your thoughts in this instance, to the extent that I can, is that I assume we both know how to play the “list game,” understood as the use of standard discourse moves associated with this particular epistemic form.10 Asking why a list is ordered in a certain way, suggesting it be ordered differently, arguing that a particular item doesn’t belong on the list, that an item is missing, that certain items could be merged, or split in two—are some of the standard moves that participants in a conversation about a list can expect each other to make. Anticipating your own epistemic vigilance, I acknowledge there will be other such moves I’m not thinking of at the moment. Now, suppose you ask me to define what I mean by the term person, to explain the difference between persons and agents, and to explain why I consider a “person” an epistemic form. In this case, you’d be moving from the list game to the definition game. In doing so, you’d be demonstrating your understanding that the same word can mean different things to different people, and that civil discourse depends on our having a shared understanding of what our words mean. More generally, the ability to engage in civil discourse involves the ability to employ some large number of epistemic forms, many more than I’ve listed here, and to move fluidly from one to another. A complete description of human epistemic fluency would involve a complete list of epistemic forms available to all humans (cultural universals), those that are specific to certain languages, cultures, and subcultures (an example of the latter might be systems analysis), and a full list of the discourse moves associated with each form, whether universal or culture-specific. Given, among other problems, the possibility of new epistemic forms emerging every day in some corner or another of our intellectual world, a complete inventory is impossible, and anything approaching it would burst the seams of my argument, which, fortunately, depends only on

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your getting the general idea. To that end, in the following sections I’ll give a few more representative examples of epistemic forms and the games we language-using humans play with them. As I go along, I’ll be making a point of linking human thinking to thinking in other animals. A central idea is that human ways of thinking about the world must have emerged from a set of epistemic primitives: cognitive structures and mechanisms available to other animals, including our distant primate ancestors. In other words, just as there must have been biological precursors for the human faculty of language, so too there must have precursors for our special ways of thinking with language. For example, if a list, as a way of organizing knowledge, is a true epistemic primitive—and I confess I’m not sure it is—then we ought to find evidence of the use of lists by other animals. As it turns out, the research tells us that creatures as evolutionarily distant as pigeons, monkeys, rats, and chimpanzees can be taught to recognize sequences of arbitrary symbols and to distinguish these from other sequences— suggesting that something like lists, as cognitive structures, may well have a deep history in the evolution of vertebrate brains (e.g., see Hulse and Dorsky 1979; Ohshiba 1997; Terrace 1993). However, only humans give names to lists, debate list membership, and incorporate lists into other, higher-order epistemic structures such as lists of features, steps in a procedure, causes of a problem, or components of a system. As we’ll see, this is just one example of how the human epistemic toolkit seems to have been built from, and on top of, precursor cognitive structures available to our distant ancestors and other animals, and how we’ve come to use this toolkit, through the magic of symbolic language, to think with each other in entirely new ways, in ways that are impossible for any other thinking animal. In the following sections, I work through a representative set of epistemic forms, give some examples of discourse moves associated with each form, and discuss precursor structures in other animals, especially nonhuman primates. A subtext is our close kinship with other animal thinkers, and the way that language allows us to think with each other in ways denied even our nearest evolutionary kin.

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Object Let’s begin with the basics. Clearly, any list of epistemic forms must include, near the top, objects and actions. In a world in which individual objects (mineral, vegetable, or animal) can represent either opportunity or danger, and sometimes both at once, it’s essential that an animal be able to perceive individual objects as separate from the background—a capacity that prey species may work hard to block or disrupt—and identify the object as belonging to one category or another. Although much of the scientific work on object recognition involves visual systems, animals clearly draw from a large array of perceptual information—including sounds, smells, tastes, and touch—to recognize objects (including other animals) in their environments and distinguish them from other objects. Because the ability to recognize a variety of objects is ubiquitous in the animal world, and because human infants demonstrate the ability to distinguish, for example, cats as different from horses and giraffes well before they learn to name them (Logothetis and Sheinberg 1996), the ability to recognize and classify objects is obviously prelinguistic, based on neural mechanisms that must have deep evolutionary roots. However, as discussed in previous chapters, the ability to direct attention to particular objects for communicative purposes, to give objects names and, discuss, for example, whether a given object (e.g., Pluto) falls into one category or another, is distinctly human, an essential part of the way we humans think with others about the world around us. For this reason, language evolution gave us noun-like structures for naming things (more generally, abstract ideas such as “democracy” and “pizza delivery system”) and adjective-like structures for describing them (a form of disambiguation). Civil discourse moves related to objects include naming a thing, asking its name, listing its defining features (how it can be distinguished from similar things), asking about such features, claiming that a thing is not what it is claimed to be, claiming that a thing is not a single thing but many, and so forth.

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Action Just as clearly as objects, we also need to recognize actions—encoded in language as verbs—as primitive epistemic forms that are also available to other animals. Both predator and prey species have an obvious interest in being able to detect, track, and classify movements of objects in their environments, partly because how something moves helps to distinguish it from other objects (e.g., the difference between a stick and an insect pretending to be a stick), and partly because it matters, sometimes very much, whether another animal is moving rapidly toward, or away from, one’s own current location. As tool-using animals, we humans also take a special interest in the actions we make ourselves; the actions others make, especially with their hands and bodies; and the effects different actions—throwing stones, digging a hole, hitting one rock with another to split off a sharp flake—have on objects. Given these fundamental interests, it is unsurprising to find that animals have neural mechanisms dedicated to monitoring action. For example, using “point-light animations”—in which cartoon-like animated light displays are made to resemble the movements of different animals, so-called “biological motion”—researchers have discovered that a range of distantly related species, including pigeons, chicks, monkeys, apes, and dolphins, all attend preferentially to movements of their fellow creatures (Simion et al. 2008). Using the same technique, newborn human infants have been found to discriminate between biological and non-biological motion (ibid.). In other words, the evidence suggests that neural mechanisms for detecting and tracking biological motion are built into the vertebrate visual system and are therefore of ancient origin. Nonhuman primates, humans, and quite likely other animals also have built-in neural mechanisms for visually monitoring and controlling fine-motor movements, such as grasping. The system employs a set of mirror neurons, which fire not only when the animal itself makes a certain gesture, but also when it observes someone else make a similar one (Rizzolatti et al. 1999). Interestingly, in humans, the mirror system is found in Broca’s area, which is also associated with the production of speech sounds, which may be understood as a special kind of gesturing. For this reason, it has been suggested that tool use and hand gestures were

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in some way strongly linked in the evolution of language, the so-called mirror system hypothesis (Arbib 2005). An important function of language is the ability to convey subtle distinctions between different kinds of actions (e.g., floating vs. fluttering), to direct the actions of others (“Twist that knob…”), and to request such direction (“Clockwise?”). Verbs and adverbs, in other words, are just as important as nouns and adjectives. Indeed, the search for connections between objects and actions is fundamental to thinking in all animals.

Agent The notion of agency may be understood as linked to cognitive process (acting through a neural network) that ties together the actions (behavior) of an object (the agent) with an intended goal . Evidence that prelinguistic human infants have an innate sense of agency comes from experiments in which babies are shown an animated cartoon of a large ball following a smaller one toward a barrier with a small gap. The smaller ball passes through the gap, and the larger ball goes around the barrier and then continues to follow the smaller ball as the two move out of view. After presenting the animation several times, the experimenter shows the infants two versions of what happens after the objects pass the barrier. In one version, the larger ball catches up with the smaller one, and the two come to a halt. In the other version, the larger ball passes the smaller one and continues on its way. Consistent with the hypothesis that the infants perceive the larger ball as an agent with the goal of “chasing” the smaller one, they tend to stare at the “passing” version longer, suggesting they find it surprising (Csibra et al. 2003). Viewed from an evolutionary perspective, it seems clear that many other animals, especially prey animals, must be able to attribute agency to the movements of other animals in their view and to understand, for example, the intended outcome of chasing when they observe it. The capacity to translate more subtle but still observable behaviors into hidden intentions is especially important for social animals such as primates, who must be able, for example, to distinguish between an

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invitation to play-fight and a true threat, and in this sense may be understood to have at least a rudimentary capacity for reading an agent’s mind. However, it’s clear that only humans can represent different kinds of agency symbolically and discuss, as I am doing here, differences in the perception of agency in humans and nonhuman primates. Civil discourse moves in the “agency” game include ascribing one or another motive to an agent’s behavior, providing an alternative explanation, and arguing that a given action was unintentional. As I explain below, the concept of agency, as an epistemic primitive, seems to get incorporated into other epistemic forms including cause-and -effect models, personhood , and stories.

Category Along with objects, actions, and agents, it’s clear that categories are among the most primitive of epistemic forms, deeply rooted in the cognitive apparatus shared by all sentient beings. The ability to pick out certain important objects, actions, and events against the fuzzy background of the natural world, attend to them, place them in categories on the basis of recognized features (predator vs. prey, edible vs. inedible, dangerous vs. non-dangerous, useful vs. useless), and act accordingly, is fundamental to survival throughout the animal world. As we’ve already discussed, vervet monkeys are famously able to distinguish between different types of predator (e.g., eagles vs. leopards vs. snakes) and to produce different alarm cries for each (Seyfarth et al. 1980). A similar behavior has been observed in prairie dogs, which apparently have different alarm cries for hawks, humans, coyotes, and domestic dogs (Kiriazis and Slobodchikoff 2006). Only humans, however, give names to categories, use these names as a means of symbolic reference to objects and actions currently out of view, and debate category membership, e.g., whether or not poor little Pluto deserves to be classified as a planet. As epistemic forms in human minds, categories are defined by a set of distinguishing features.11 As I discussed in Chapter 7, whereas the act of pointing and naming was likely a function of an early protolanguage, the ability to debate category membership is almost certainly a more recent

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development, if only because it requires symbolic reference, which, as I’ve argued, must have followed, and built on, indexical reference. There’s a difference between saying “Look, leopard!” (pointing to an animal in the immediate frame of reference) and “Watch out for leopards!”—animals not currently visible that fall into a certain category. It’s another step up the ladder of symbolic representation to assert that leopards are members of the cat family and to discuss, as a topic of civil discourse, whether certain kinds of leopard ought to belong to the “endangered species” category. Typical discourse moves in the “category game” are very much like those in the list game. They include claiming that something “falls into” a certain category or does not, asking for a list of the features that define category membership, suggesting that a given feature is not a defining feature, suggesting that two different categories are in fact the same, and arguing that a category is “fuzzy” and thus better represented by a continuum of features which are more or less present. I have argued, for example, that it is better to think of human language not as a single category of communication (in the sense it was either present or not at a given point in time), but as a set of related features that must have fallen into place, in fits and starts, over 2 or 3 million years.

Definition This brings us to the issue of definition, which may be understood as a formal attempt to identify the meanings of words, and to distinguish the meanings of similar words from each other. Because definitions have to do with word meanings, and because humans alone are capable not only of using words to name things, but of treating the names themselves as objects of attention, we may be confident that, unlike the ability to form mental categories, which is arguably present in all animals with nervous systems, definitions are unique to the human epistemic toolkit. That said, definitions only work because, along with other animals, including our own hominin ancestors, we have the capacity to form mental categories and to consider, in some fundamental way, whether a given thing falls into this category or that.

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Because the natural world itself is inherently fuzzy, because the number of things that may be usefully named is nearly infinite, and because of the need to establish shared understanding of word meanings, an ability to play the “definition game” is a critical component of epistemic fluency and civil discourse. Standard moves include requesting a definition (“What do you mean by ‘teaching?’”), providing a definition, and arguing that membership in a category is dependent on the definition of category membership (“It depends on how you define ‘language’”). Epistemic vigilance, in other words, requires that participants in civil discourse about complex phenomena are working from a shared understanding of relevant terminology. Careful definitions, based on lists of distinguishing features, may serve to disambiguate meanings and avoid misunderstandings.

Place As a cognitive construct, a place is clearly another important epistemic primitive. As animals amble, burrow, swim, or fly through their habitats in search of food, mates, and shelter, it’s essential that they have a sense of place, and, in particular, that they be able to recognize and recall the features of the important places they visit and return to. In the early 1970s, the neuroscientist John O’Keefe and his student Jonathan Dostrovsky, using electrodes implanted in the brains of rats navigating a maze, discovered the existence of neural “place cells” in the hippocampus (O’Keefe and Dostrovsky 1971).12 These fire, for example, when the rat passes certain locations, such as one containing an unfamiliar object. While place cells have been studied mainly in rats and mice, and more recently in monkeys and humans, it is now thought that place cells are involved in the navigational systems of most if not all vertebrates (Jeffery and Hayman 2004). While other animals can distinguish one place from another and, as in the case of honey bees, signal the location of a particular place, only humans can talk about places. As argued in Chapter 6, the ability to point to a particular place in the shared attentional frame—and also to point in the general direction of a place beyond the horizon, or obstructed

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from view—was likely early feature of a protolanguage. Over time, with the evolution of the capacity for symbolic reference, locations outside the immediate attentional frame could be named, and referred to symbolically, without the need for physical pointing. As an epistemic form, a place is understood to have a location with respect to other places, and a set of features that serve to distinguish it from its surrounding environment and other important places, and a name: Overton Square, Walden Pond, the post office. Discourse moves in the “place game” include asking and telling where a place is, describing its distinguishing or remarkable features, and asking for such a description.

Map A map may be understood as a higher-level epistemic form that represents such constructs as the location of multiple places within an environment with respect to each other, the distances between places, and standard routes from one place to another. Discourse moves related to maps include asking for and giving directions to a place, describing the location of one place with respect to another, suggesting a shortcut between two places, and arguing that a so-called short cut is in fact longer, or more dangerous. Although we typically think of them as twodimensional, maps can also be three-dimensional, as when we describe one object as being on top of, or below another. Maps, like many other epistemic forms, can also be used metaphorically, as when we talk about Democrats and Republicans being “far apart” on the issue of immigration, or when we talk about understanding the meaning of a poem “on different levels” or “from different perspectives.” Whether nonhuman animals have access to cognitive maps—spatial memory systems that include a bird’s-eye view of the animal’s current location and orientation with respect to other locations in a physical environment—is a matter of some debate (for an argument that animals can navigate an environment without recourse to a cognitive map, see Bennett 1996). The notion of a cognitive map as a memory structure was first introduced in 1948 by the American psychologist Edward Tolman based on his work with rats navigating mazes (Tolman 1948). O’Keefe

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and Dostrovsky’s discovery of place cells in the rat’s hippocampus (1971) helped to validate Tolman’s original conjecture. More recent imaging work has identified the presence of head direction cells in many regions of the brain, and grid cells in the entorhinal cortex (Brun et al. 2002), a region near the hippocampus, both of which are located in close proximity to the neocortex—known to be the site of higher-order brain functions such as sensory perception, cognition, generation of motor commands, spatial reasoning, and language. Whether nonhuman animals have cognitive maps in Tolman’s original sense, it is clear that many animals are capable of representing and storing spatial locations in memory. In a frequently cited example, scrub jays—members, along with crows and ravens, of the corvid family— cache surplus food, including worms and nuts, in multiple locations and steal from caches they’ve observed other birds making. Importantly, the jays can recall not only the location of these caches, but also how long ago the food was cached, as evidenced by the fact that they avoid searching for perishable foods such as worms if the interval is sufficiently long for the worm to have decayed, but continue returning to buried nuts, which have a more distant expiration date. Also, if a scrub jay notices it’s been observed by another jay while caching, it later returns to the site to recache the food in a safer location, but only if it has itself stolen food! (Clayton et al. 2001). As we’ll see, this has implications for the existence of episodic memory, which is thought to store not only the “where” and “when” but also the “what” and “who” of past experiences. More generally, it seems obvious that any foraging animal—especially one that inhabits a relatively large, patchy range—must have some way of recalling the location of important landmarks within its territory, representing its own location relative to these locations, and calculating the direction and distance to each relative to its own current location. This is especially important in habitats—including those inhabited by most primates—that experience seasonal variation in the availability and quality of food sources at different locations. This leads us to another critical way of knowing about the world: the ability to track changes in the environment over time.

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Timeline A timeline is an epistemic form that serves to place a series of episodes or events in chronological order, to represent the duration of these episodes, and their spacing, i.e., the amount of time elapsed between episodes. A timeline can be a straightforward record of events with known dates attached to them, or—as in my attempt in Chapter 6 to plot the coevolution of human language, brains, and material culture—a timeline can be an important component of a theory (discussed below), in which case it can be called a scenario. Moves in the “timeline” game include claiming that an event did or did not occur at a certain time, claiming that events occurred in a certain order, claiming that an event marked a turning point, and claiming a causal relationship, e.g., that one event caused or paved the way for another. Timelines can also be projected into the future. The extent to which other animals can represent sequences of events in the past and use them to predict the timing of future events is unclear. As noted above, a scrub jay can apparently keep track of how much time has elapsed since it buried a worm in a particular location, and will not trouble to dig it up again beyond a certain expiration date. Evidence suggests that male meadow voles can keep track of the location of multiple females in their range, the reproductive state of the female when last visited, the length of time elapsed since the visit, and thus the probability that she will be in the same reproductive state at some later time (Ferkin et al. 2008). Experiments with chimpanzees and bonobos suggest that these close relatives of ours also have some sense of elapsed time (“mental time travel”; see Suddendorf and Corballis 2010) and can use it productively. For example, in an experiment designed to elicit scrub jay behaviors, captive apes were allowed to sample frozen juice (preferred but perishable, analogous to worms) and grapes (less preferred but less perishable, analogous to nuts). The two kinds of food items were then hidden in different locations while the subjects watched from behind a barrier. The apes were then released and allowed to retrieve the food after periods of five minutes (at which point the frozen juice would not yet have melted) and one hour. As predicted, the apes scampered to retrieve the frozen

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juice after the shorter interval, but, when forced to wait an hour, went off to look for the grapes instead, apparently calculating that the frozen juice would by then have melted into the ground, making any search a wasted effort (Martin-Ordas et al. 2010). Indeed, it stands to reason that an animal with a large foraging range and seasonally available food patches (notably, ripening fruit) would be better off if it had some way of keeping track of how long a given food patch would likely be productive, instead of having to keep returning, and possibly wasting energy, to check. Observations of wild chimpanzee foraging behavior suggest that this is in fact the case, and that chimps do indeed track not only the location of food patches, but also their seasonal availability (Bessa et al. 2015). Needless to say, only humans keep written logs of past events, calendars for scheduling events in the future, and discuss proposed meeting dates (as in a famous New Yorker cartoon: “How about never? Would that work for you?”).

Plans and Procedures A plan is a special kind of timeline, a mental representation of a series of distinct actions (steps), to be executed in a certain sequence, aimed at producing a future result or goal . When a plan has become standardized, we call it a procedure. As we’ve seen, our chimpanzee cousins are clearly able to mentalize and execute fairly complex plans such as those involved in termite fishing and nut cracking. If a chimp could describe termite fishing as a procedure, it might go something like this: “First, find an active termite mound. Next collect your materials. You’ll need a stout stick for poking holes in the mound, and a long slender stick stripped of any leaves. (It works best if the stick is slightly frayed at the end, which makes it easier for the termites to bite onto it. Chew it a bit, or rub it against a rock.) Third, locate a hole in the side of the mound; if you can’t find an existing hole, use your stout stick to make one. Now insert the slender stick into the hole, wait a bit, then withdraw the stick, which, with luck, will be crawling with termites. Insert the stick in your mouth, and extract the

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termites with your lips. Chew and swallow. Repeat as many times as you like. If one hole in the mound stops producing results, try another.” Although it’s impossible to read the contents of a chimpanzee’s brain, it’s reasonable to think that chimpanzees indeed follow a plan like this if only because we see them behave as if they had such a plan. Unlike humans, however, chimpanzees do not actively instruct novices in how to fish for termites (although, as we’ve seen, an older sister may help a younger one), do not represent procedures symbolically, and thus cannot discuss alternative procedures or plans with each other. In contrast, and as we’ve discussed, the capacity for collaborative planning, and the ability to share routinized plans (procedures) for the production and use of tools, must have been playing a central role in the accumulation of cognitive capital for millions of years. As toolmaking procedures grew too complex to be learned easily by simple observation and individual trial-and-error learning, pressure must have grown for ways of helping novices acquire the necessary procedural knowledge, thus giving rise to teaching through intentional demonstration (“Look here…”) and scaffolding. (“Now you try…no…not like that…like this…right.”) At some much later point in the coevolution of language, brains, and technology, humans developed the capacity to explain to others how to carry out certain procedures offline, and eventually to engage in civil discourse about procedures, as epistemic forms, and to write down instructions describing how to change a tire or make beef bourguignon or fish tacos. Discourse moves associated with plans and procedures include listing the steps in order (“First…next…finally…”), claiming that a step is unnecessary, suggesting an additional step, suggesting that steps be taken in a different order, and claiming that the same outcome can be achieved with a simpler procedure.

Personhood As an epistemic form, a person is a mental construct representing an individual agent (usually a human, but also an animal, fictional character, god, or, in the case of animism, a natural phenomenon such as a river

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or lightning) with a defining set of category memberships and personality traits, including personal appearance and characteristic ways of behaving, that serve to place the individual in a category of its own. As such, a person, as a cognitive category, incorporates and extends the notion of an intentional agent by giving the agent a name and set of defining attributes. As we’ve already discussed, our fellow primates, as a fundamental component of their own social intelligence, are fully capable of recognizing and recalling the past behavior of individuals in the group, and placing individuals in complex categories based not only on their social relationships to themselves (mother, sister, aunt; dominant vs. subordinate), but also on the basis of their relationships to each other (e.g., the dominant of two uncles on the mother’s side; see Tomasello 2000). However, only we humans give each other names and spend as much as two-thirds of our social interactions talking about non-present others (Dunbar 2004). As distinct from simple gossip, civil discourse about individuals occurs when participants in the conversation operate from a stance of epistemic vigilance, which acknowledges the fact that individuals are complex, behave as they do for a variety of reasons, often unknown, do not always behave predictably, behave differently in different settings, may change their behavior over time, and are in other ways ultimately mysterious and unpredictable. In this sense, characterizations of a given individual may be understood as theories (see below), and the observed or reported behavior of the individual may be understood as evidence in support of the theory. Moves in discourse about individuals include claiming that a person has a certain trait (e.g., is boring, humble, generous, proud); claiming that a person does not have that trait; reporting the behavior of a person as evidence that the person has this trait or that; asking for such evidence; predicting the future behavior of a person; questioning such a prediction; and so forth.

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Cause-and-Effect Model As defined by the anthropologist Marlize Lombard and cognitive scientist Peter Gärdenfors, “causal reasoning” involves three related cognitive capacities: (1) the ability to predict future outcomes based on present observations; (2) the ability to achieve certain effects (goals) by manipulating causes; and (3) the ability to “predict” (guess at) the cause of an effect, even if the cause is not immediately apparent (Lombard and Gärdenfors 2017). The extent to which nonhuman primates and other animals demonstrate these capacities, if at all, is again a matter of debate. Certainly many animals, perhaps most, learn to associate causes and effects—such as discovering the relationship between eating poisonous food and getting sick, or pressing a lever and receiving an electric shock—and thus may be understood to have at least the first of these three capacities. Further, tool-using animals such as crows, ravens, sea otters, dolphins, capuchin monkeys, and chimpanzees all clearly demonstrate the ability to cause outcomes in a planful way, such as by cracking open a nut or shellfish by hitting it with a rock, or stripping the leaves from a twig for use in digging for insects. But whether nonhuman animals can work backward, from effects to possible causes, seems doubtful. For example, although vervet monkeys are, for good reason, deathly afraid of snakes, they reportedly do not react to the sight of a track left by a snake (Cheney and Seyfarth 1992). Humans, in stark contrast, have evolved the ability to work with highly sophisticated cause-and-effect models, which allow us not only to predict future outcomes, to manipulate outcomes, and to work backward from observed effects to likely causes, but also to engage in civil discourse, with others, about these models. Moves in the “cause and effect” game include asking about the cause of a particular effect (“Mommy why do the wheels squeak like that?”), suggesting a possible cause (“I think it’s from the friction”), arguing that an effect has multiple causes, offering and discussing lists of possible causes, arguing that a supposed cause is “just a symptom,” suggesting that an action may have unintended consequences, and so forth. As we’ll see, informal causal models such as these form the basis for the higher-level structures we call theories.

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Problem A problem, we can say, is an adverse situation or set of circumstances that is potentially open to a solution. Problems are understood to have causes and adverse consequences for those affected by them. Standard moves in civil discourse about a problem include claiming that a situation is a problem; claiming that it is not, requesting and providing evidence regarding the consequences of a problem; claiming that a problem has a certain cause, or several causes; arguing that a cause is really just a symptom; suggesting a solution; arguing that a solution will address an underlying cause, or that it won’t; arguing that a solution may have unforeseen consequences; and so on. The ability to engage in civil discourse about problems and their potential solutions is closely related to the ability to work back and forth between theory and evidence. A workable solution, after all, necessarily addresses the root causes of the problem, and these are often matters of conjecture and theory building. Particularly in the case of complex problems such as pandemics, poverty, political polarization, school shootings, and climate change, the underlying causes are themselves complex and open to theoretical analysis. In these cases, epistemic vigilance requires that an acceptable solution be built on an evidence-based theory about its causes. Other animals solve difficult problems, of course. A homing pigeon released in unfamiliar territory hundreds of miles away from its home loft will find its way back using a combination of atmospheric odor gradients (the birds literally smell their way back), and as they approach their home territory, stored visual representations (Bingman 2011). Rats learn to find their way through complex mazes. New Caledonian crows manufacture hooks from twigs and use them to extract insects from cracks and crevices in rotting wood, much the way chimpanzees use sticks to fish for termites (Hunt 1996). But no other animal discusses potential solutions to problems with its fellow creatures, no other animal depends so much on its ability to do so, and no other animal regularly fails to make full use of its capacity for collective problem-solving.

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Story Stripped to its essentials as an epistemic form, a story is a narrative account built around a series of one or more episodes, which occur at a particular time and place, and in which one or more persons (humans, animals, gods, etc.), each motivated by the desire to accomplish certain goals, perform certain actions, which have consequences for themselves and other agents. As epistemic forms, stories are clearly unique to humans. However, like all such structures, our ability to produce and make sense of a story is rooted in the cognitive structures and mechanisms we’ve already discussed, which are shared by nonhuman primates and, in many cases, other animals. These include the epistemic primitives discussed above, including timelines, maps, personhood, and cause-and -effect models. The underlying neural architecture, which may be understood to integrate these different structures, is episodic memory. As originally defined by the cognitive scientist Endel Tulving (1983), episodic memory allows humans, and only humans, to relive personal experiences by “traveling back to the past in their own minds,” a cognitive activity sometimes called “time travel.” In Tulving’s definition, humans have evolved something he calls autonoetic consciousness, the ability to form a mental representation of one’s own self, and project this representation not only into the past, but also into the future, and other imagined situations. It’s what allows us to think about what it would be like to own a restaurant, play a musical instrument well, and what it will be like to die. The same capacity allows us to relive our own past experiences, and those of others when we’re told about them. Whether any other animal has a subjective sense of self in the way that humans do, it is clear that many other animals have something very much like episodic memory (“episodic-like memory”), which allows them to bear in mind the “who, what, and where” of past experiences, and to use these stored memories of the past to make decisions in the present. We’ve already seen evidence of this in species as distantly related as scrub jays, meadow voles, and other great apes (chimpanzees and bonobos), all of which have somewhat different, but equally important reasons for being able to recall the particulars of past events.

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Episodic memory must have been especially important for our hominin ancestors—who like all foraging primates had not only to keep track of the identity and past behavior of other group members, but also the location of seasonal food patches, and the location and nature of past encounters with predators and prey. As social groups became larger and more complex, as foraging ranges expanded, and as food resources became more various and difficult to extract, selection pressure for enhanced episodic memory would have increased, meaning that individuals with genetic profiles that made them even slightly better at storing and recalling the “what, who, and where” of past episodes would have been more fit: better fed, better at attracting mates, and better at raising their young to the point of sexual maturity. Eventually, and likely well after the emergence of protolanguage, a capacity for symbolic reference, possibly (as I argued in Chapter 6) rooted in the ability to point and name, would have made it possible not just to recall personal episodes, but to relate these episodes to others, i.e., to tell stories. Initially, stories were likely about recent events, such as an encounter with a prey animal or predator, which could be related using a combination of pointing and miming. In time, the obvious advantage of being to tell about events in the more distant past, as a way of instructing young people in the group’s history, and thus transmitting useful knowledge about the group’s culture, quite likely put pressure on language to become more useful for storytelling, and brains to become better at understanding stories. As a result, humans have become habitual storytellers, and the informal exchange of personal stories as a form of social communication has become a cultural universal (Boyd 2009). As epistemic forms, stories have distinct, predictable structures. When we hear a story, we expect to be told something about the participants, who they were, what they did and where, why they did it (apparently a distinctly human concern), and the consequences of their actions. We also expect that the story will have a beginning, middle, end, and purpose. As we listen to a story and don’t hear what we expect, we can interrupt and request the missing information. “Who was Ulysses?” “Where did this happen?” “Why did he do that?” Also, like a police detective, we can try to elicit a story in this way from a reluctant storyteller.

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The role of stories in civil discourse is complex. Depending on context, a story can be understood as pure entertainment, as art, as a way of passing along important cultural information to upcoming generations (as in creation myths), or as a way building social solidarity and common ground with interlocutors (Grice 1989). The latter is an especially common use of storytelling, as when your new friend tells you a funny story about a surprising encounter with a raccoon in an attic, which reminds you of the time you had a problem with squirrels in your own attic and ended up trapping six, which reminds your interlocutor of the fact that squirrels are the second leading cause of house fires in the United States, and so on. The mere exchange of stories like these is arguably not civil discourse as I’ve defined it, so long as there’s no exercise of epistemic vigilance. In other words, if stories are taken as simple accounts or interpretations of personal experiences, without a claim to represent anything like objective reality, then, while perfectly useful and important uses of language, they don’t rise to the level of civil discourse. But if stories are embedded in civil discourse, that’s different. If your story about the squirrels in your attic is part of an argument you’re trying to make that squirrels are domestic animals, and if I then ask you to define what you mean by “domestic,” and you respond that by domestic you mean an animal that lives in and around human habitations, then we’re in the realm of civil (if not very sensible) discourse. More generally, stories become objects of civil discourse only to the extent they are open to question—that is, accounts that may or not be true, and are thus, like theories, subject to the tests of validity required by epistemic vigilance.

Theory Theories are the most evolved of the epistemic forms we’ve discussed so far, and most clearly illustrate the features of civil discourse among humans. Other animals might be considered to form theories, but only in the limited sense that their predictions about causal relationships are open to revision. For example, when a raven warily approaches an unfamiliar food item, and begins pecking at it with almost comical caution

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(see Heinrich 1999), we might argue that the raven has a working “theory” that the food item is dangerous, and is willing to modify its theory only in the face of sufficient evidence to the contrary. A cognitive stance of uncertainty, when combined with a willingness to take risks, is clearly adaptive. But ravens don’t discuss their theories about the palatability and nutritional qualities of different foods with other ravens. As constructed by humans, theories can be usefully compared to stories. Like theories, stories, in the form of creation myths, are explanatory, in the sense that they can explain how things came to be, and why things are as they are. As such, stories can make use of “evidence” as a way of establishing the storyteller’s “truth.” A protagonist’s crafty intelligence, for example, is evidenced by episodes in which she manages to escape certain death at the hands of enemies by turning them into rocks, making herself invisible, or posing clever riddles. A critical difference between a story and a theory is that whereas a story may be understood to be the storyteller’s own undisputable account of the way things happened, a theory is an account not of how things were, or are, but how things might have been, or might be, and in any case the particulars are open to debate. Like stories, theories are built around epistemic primitives—including objects, actions, agents, categories, timelines, maps, and, most obviously, cause-and -effect models, and may, as noted above, incorporate stories. Theories are also constructed around a broad range of other epistemic forms I’ve elected not to discuss here, including systems (which explain how the different components of a system are dynamically related to each other) and processes (which examine phased or continuous changes in a system over time). Typical discourse moves in the “theory and evidence” game include claiming that a certain piece or collection of evidence supports a theory, asserting that it does not, and proposing an alternative theory that fits the same evidence. Also, since theories are built around many other epistemic forms, arguments about theories typically involve a broad assortment of moves related to these forms, including: asking for a definition (“How do you define language?”); arguing that given categories are not “watertight”; arguing that agents behave differently in different contexts (as in discussing differences in the behavior of wild and captive chimpanzees);

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arguing that a given outcome has multiple causes; claiming that events occurred in a certain sequence; and so on. In short, civil discourse about theories engages multiple components of epistemic fluency.

How Did Animal Thinking Become Human Thinking? Again, what happened? How did off-the-shelf primate intelligence evolve into human intelligence? How did the ability to form useful mental categories such as prey vs. predator morph into the ability to argue about whether or not Neanderthals were fully human or whether Pluto is a true planet? How did the ability to choose efficient routes among scattered food patches somehow turn into devices on our phones that tell us to turn right 800 feet ahead onto McLemore Avenue? How did episodic memory, the innate primate capacity to recall the “who, what, when, and where” of past episodes evolve into critical appraisals of the novel Anna Karenina? How did an animal with only a dim understanding of the relationship between physical causes and their effects become capable of developing theories about the evolution of language, and teaching through language, in its own species? How, and when? This is the subject of the next chapter.

Food for Thought 1. Do you agree civil discourse is an ancient form of human talk? Could we have become who we are without it? 2. What, in your mind, is the relationship between civil discourse and teaching? 3. What is the role of epistemic vigilance in civil discourse? 4. Can you think of any important epistemic forms that are not among those discussed in the chapter? 5. How exactly did animal cognition become human cognition?

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Notes 1. As it turns out, the first mother was not as far off the mark as I thought. I recently came across a story in the Boston Globe describing an experiment aimed at reducing noise on Green Line trains by spraying mists of water on the tracks. Excess water could be easily be drained away without fear of fire. 2. For example, “intelligent conversation,” “intelligent discourse,” “reasoned argument,” “accountable talk.” For a discussion of this last construct, see Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in philosophy and education, 27 (4), 283–297. 3. The reference is to a description provided by the biologist Christopher Wills of hominin life in the Zhoukoudian region of Northern China roughly 500,000 to 200,000 years ago: “…the people of the caves of Zhoukoudian crouched over their smoky fires, eating the half-cooked bats.” Wills, C. (1993). The runaway brain: The evolution of human uniqueness (p. 69). New York: Basic Books. Bickerton (1992: 46–47) uses this example in his argument that large brains alone (absent modern forms of language) do not necessarily lead to technical progress. 4. There’s some chance that Isaac was just being “silly.” At around this time he also told his uncle that gravity is “what makes people fall down” and demonstrated by falling down himself. Another explanation is that, given his understanding of gravity as something that makes people (and potentially smoke) fall down, he may be thinking that smoke rises, instead of falling down, in response to “shooty gravity,” a kind of antigravity. 5. I should mention that a few weeks after this exchange Isaac entered a new “know nothing” stage; anytime his grandmother asked him a whyquestion, he responded the same way: “I dunno.” 6. I hope you will not be too put off by my liberal use of technical jargon in this paragraph. I use the term “epistemic” here partly to connect with certain relevant research literature (see note below), but also for the sake of brevity. We could equally well be talking about a “knowledge construction toolset,” “knowledge construction fluency,” and so forth. 7. As employed by Sperber and his colleagues, epistemic vigilance refers to a “suite of cognitive mechanisms” that humans have evolved to guard against the possibility of being accidentally or intentionally misinformed. These mechanisms, they argue, help to offset the risk that language might

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be used for maladaptive, Machiavellian purposes, and have thus allowed language to evolve as a mechanism for social coordination and learning. Another way of saying this is that humans have innate bullshit detectors. As discussed later in the chapter, these begin to come on line in all children around the age of 3–4 years, but eventually, under various cultural influences, become more effective for some of us than others. Even a list of random numbers and words share the feature of list membership—being on the same list, right? See Collins and Ferguson (1993) for the first use of the notion of epistemic games as a set of mental “moves” associated with particular epistemic forms. Morrison and Collins (1996) extended this idea to the realm of discourse moves and introduced the idea of epistemic fluency. Since that time, Doug Shaffer has applied the term “epistemic game” to describe a certain kind of educational video game intended to help students acquire epistemic fluency in certain domains, such as urban planning. See Shaffer, D. W. (2005). Epistemic games. Innovate: Journal of Online Education, 1(6), 2. You may notice the circularity in my terminology here. I started by claiming that a list is a set of items belonging to the same category, and now I’m claiming that category membership can be defined by a list of features. As it turns out, it’s a characteristic of epistemic forms, as building blocks, that they can serve multiple purposes. This is especially true of lists, which form the basis for many other structures, such as a list of steps in a procedure, phases in a process, components in a system, pieces of supporting evidence in a theory, correspondences in an analogy, and so on. In 2014, O’Keefe received a Nobel Prize for this work.

Suggested Reading De Waal, F. B., & Ferrari, P. F. (2010). Towards a bottom-up perspective on animal and human cognition. Trends in cognitive sciences, 14 (5), 201– 207. The authors use a similar approach to the one used in this chapter— arguing that the “building blocks” of human cognition are present, though less developed, in other animals.

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Lombard, M., & Gärdenfors, P. (2017). Tracking the evolution of causal cognition in humans. Journal of Anthropological Sciences, 95, 1–16. The paper focuses on the evolution of “causal cognition” in humans, as applied to the task of animal tracking (in the context of hunting). As you read it, think about what other “epistemic forms” are involved in the process—e.g., agents, actions, categories, maps, timelines, and so forth. Morrison, D., & Collins, A. (1995). Epistemic fluency and constructivist learning environments. Educational Technology, 35 (5), 39–45. An early paper in which the cognitive scientist Alan Collins and I first described the relationship between epistemic forms, epistemic “games,” and epistemic fluency. Seed, A., & Tomasello, M. (2010). Primate cognition. Topics in cognitive science, 2(3), 407–419. A useful review of what is known about primate cognition, including understanding of causality, objects in space, and time.

References Arbib, M. A. (2005). From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics. Behavioral and Brain Sciences, 28(2), 105–124. Austin, J. L. (1962). How to do things with words. Cambridge: Harvard University Press. Bennett, A. T. (1996). Do animals have cognitive maps? Journal of Experimental Biology, 199 (1), 219–224. Bessa, J., Sousa, C., & Hockings, K. J. (2015). Feeding ecology of chimpanzees (Pan troglodytes verus) inhabiting a forest-mangrove-savanna-agricultural matrix at Caiquene-Cadique, Cantanhez National Park Guinea-Bissau. American Journal of Primatology, 77 (6), 651–665. Bickerton, D. (1992). Language and species. Chicago: University of Chicago Press. Bingman, V. P. (2011). Making the case for the intelligence of avian navigation. In Menzel, R., & Fischer, J. (Eds.), Animal thinking: Contemporary issues in comparative cognition (Vol. 8, p. 40). Cambridge: MIT Press.

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Boyd, B. (2009). On the origin of stories: Evolution, cognition, and fiction. Cambridge: Harvard University Press. Brun, V. H., Otnæss, M. K., Molden, S., Steffenach, H. A., Witter, M. P., Moser, M. B., et al. (2002). Place cells and place recognition maintained by direct entorhinal-hippocampal circuitry. Science, 296 (5576), 2243–2246. Cheney, D. L., & Seyfarth, R. M. (1992). Précis of How monkeys see the world. Behavioral and Brain Sciences, 15 (1), 135–147. Clayton, N. S., Griffiths, D. P., Emery, N. J., & Dickinson, A. (2001). Elements of episod-ic–like memory in animals. Philosophical Transactions of the Royal Society B: Biological Sciences, 356 (1413), 1483–1491. Collins, A., & Ferguson, W. (1993). Epistemic forms and epistemic games: Structures and strategies to guide inquiry. Educational Psychologist, 28(1), 25–42. Csibra, G., Bıró, S., Koós, O., & Gergely, G. (2003). One-year-old infants use teleological representations of actions productively. Cognitive Science, 27 (1), 111–133. Dunbar, R. I. (2004). Gossip in evolutionary perspective. Review of General Psychology, 8(2), 100. Ferkin, M. H., Combs, A., Pierce, A. A., & Franklin, S. (2008). Meadow voles, Microtus pennsylvanicus, have the capacity to recall the “what”, “where”, and “when” of a single past event. Animal Cognition, 11(1), 147–159. Grice, H. P. (1989). Studies in the way of words (Vol. 65, p. 274). Cambridge: Harvard University Press. Heinrich, B. (1999). Mind of the raven. New York: Cliff Street Books. Hulse, S. H., & Dorsky, N. P. (1979). Serial pattern learning by rats: Transfer of a formally defined stimulus relationship and the significance of nonreinforcement. Animal Learning & Behavior, 7 (2), 211–220. Hunt, G. R. (1996). Manufacture and use of hook-tools by New Caledonian crows. Nature, 379 (6562), 249. Jeffery, K. J., & Hayman, R. (2004). Plasticity of the hippocampal place cell representation. Reviews in the Neurosciences, 15 (5), 309–332. Kiriazis, J., & Slobodchikoff, C. N. (2006). Perceptual specificity in the alarm calls of Gunnison’s prairie dogs. Behavioural Processes, 73(1), 29–35. Logothetis, N. K., & Sheinberg, D. L. (1996). Visual object recognition. Annual Review of Neuroscience, 19 (1), 577–621. Lombard, M., & Gärdenfors, P. (2017). Tracking the evolution of causal cognition in humans. Journal of Anthropological Sciences, 95, 219–234.

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Martin-Ordas, G., Haun, D., Colmenares, F., & Call, J. (2010). Keeping track of time: evidence for episodic-like memory in great apes. Animal Cognition, 13(2), 331–340. Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in Philosophy and Education, 27 (4), 283–297. Morrison, D., & Collins, A. (1996). Epistemic fluency and constructivist learning environments. Constructivist learning environments (pp. 107–119). Englewood Cliffs: Educational Technology. Ohshiba, N. (1997). Memorization of serial items by Japanese monkeys, a chimpanzee, and humans. Japanese Psychological Research, 39 (3), 236–252. O’Keefe, J., & Dostrovsky, J. (1971). The hippocampus as a spatial map: Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34, 171–175. Rizzolatti, G., Fadiga, L., Fogassi, L., & Gallese, V. (1999). Resonance behaviors and mirror neurons. Archives Italiennes de Biologie, 137 (2), 85–100. Schwartz, B. L., & Evans, S. (2001). Episodic memory in primates. American Journal of Primatology, 55 (2), 71–85. Seyfarth, R. M., Cheney, D. L., & Marler, P. (1980). Vervet monkey alarm calls: semantic communication in a free-ranging primate. Animal Behaviour, 28(4), 1070–1094. Shaffer, D. W. (2005). Epistemic games. Innovate: Journal of Online Education, 1(6), 2. Simion, F., Regolin, L., & Bulf, H. (2008). A predisposition for biological motion in the newborn baby. Proceedings of the National Academy of Sciences, 105 (2), 809–813. Sperber, D., Clément, F., Heintz, C., Mascaro, O., Mercier, H., Origgi, G., & Wilson, D. (2010). Epistemic vigilance. Mind & Language, 25 (4), 359–393. Suddendorf, T., & Corballis, M. C. (2010). Behavioural evidence for mental time travel in nonhuman animals. Behavioural Brain Research, 215 (2), 292– 298. Terrace, H. S. (1993). The phylogeny and ontogeny of serial memory: List learning by pigeons and monkeys. Psychological Science, 4 (3), 162–169. Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55 (4), 189. Tomasello, M. (2000). Two hypotheses about primate cognition. In C. M. Heyes & L. Huber (Eds.), The evolution of cognition (pp. 165–183). Cambridge: MIT Press.

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Tulving, E. (1983). Elements of episodic memory (p. 1). New York: Oxford University Press. Wills, C. (1993). The runaway brain: The evolution of human uniqueness. New York: Basic Books.

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If we want to understand how an ancestral signaling system based on simple combinations of gestures, vocalizations, and facial expressions has evolved into a system capable of supporting civil discourse about the nature and origin of civil discourse, we need to think in terms of three very different timescales. First, we need to consider how the process might have unfolded in evolutionary time, beginning, say, some 6–10 million years ago, when our lineage first began to diverge from that of chimpanzees, inadvertently setting off the biological explosion that produced you and me. Second, we need to think about what has happened much more recently, in historical time, starting, say, from the emergence of the first “behaviorally modern humans” around 200,000– 300,000 years ago, to the present day—the time of the secondary cultural explosion that produced subway systems with squeaky wheels, toy trucks, and mechanical water pumps. Finally, we need to think about what happens in ontogenetic time: how a babbling infant becomes a boy who asks his mother why the train wheels squeak, and how inquisitive boys and girls grow up to become adult engineers who devise plans to spray water on subway tracks to reduce the squeaking. © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_10

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Epistemic Understanding from Infancy Through Adulthood Because the development of epistemic fluency in individuals may in some ways reflect (“recapitulate”) its development in our species, let’s start with that. As described by the cognitive scientist Deanna Kuhn and colleagues at Teachers College (Kuhn et al. 2000), building in part on work by University of Michigan researchers Barbara Hofer and Paul Pintrich (Hofer and Pintrich 1997), the development of what these researchers call epistemological understanding in individuals passes through four distinct and fairly predictable stages. A defining test of each stage is how the individual weights subjective experience (how things appear to be) against objective “truth” (how things are), and what role, if any, engagement in civil discourse plays in the individual’s capacity to seek truth in discourse with others.

Childhood “Realism” In the earliest phase, from infancy up through the age of 3–4 years, children, in Kuhn’s taxonomy, are realists. Reality, the truth of things, is directly knowable and comes from an external source. Assertions are facts. Cows say “moo” because Mommy says they do. As one can see from picture books, giraffes have long necks, leopards have spots, and firetrucks are red. Santa Claus comes down the chimney and eats the cookies we set out for him, leaving only a few crumbs as evidence of his visit. There is no need (nor capacity) to think critically about the assertions of others, and for good reason. It just wouldn’t work if parents and other adult caretakers consistently and intentionally misled children about the basic facts of the world—claiming, for example, that cows go “oink” and pigs fly. Nor would it work if infants did not naturally trust adult assertions, including well-intentioned “white lies” about Santa Claus and the Tooth Fairy. Indeed a child’s innate trust in adult assertions lies at the core of Hungarian cognitive scientists Gergely Csibra and György Gergely’s notion of “natural pedagogy” (Csibra and Gergely 2009), the child’s default assumption that when an adult points at a

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picture of a giraffe and says “giraffe,” the word applies not only to this particular, long-legged, long-necked animal with spots, but to all such animals.

The Child as “Absolutist” At some point around the age of 3–4, children naturally transition to a second stage and become epistemic absolutists. They still consider that reality is directly knowable and can be ascertained with the help of parents and other reliable third parties. However, children now begin to entertain the possibility of false beliefs in others. For example, prior to the transition from pure realism, having seen a researcher replace the contents of a box of candy with some pencils, a child will think that another child, who left the room just before the switch, will now believe the box contains pencils, because it does (Perner 1991). After the transition, the child realizes that the child who did not witness the switch will still believe the box holds candy—which may be taken as evidence of developing “theory-of-mind” (perspective-taking) circuitry, and the resulting recognition that others can have beliefs and perceptions that are different from one’s own. Critical thinking now becomes an important way of distinguishing between reality—what is actually in the candy box—and what others may believe is in it. Assertions are still about objective facts, but the facts may now be true or false. Importantly, research suggests that engagement in linguistic discourse about the distinction between subjective perceptions of reality and objective truth can help a developing child sort out the difference. In one such study, Michael Tomasello and his doctoral student, Heidemarie Lohmann, conducted a training study with 3-year-old German-speaking children who had previously failed tests of their understanding of false beliefs (Lohmann and Tomasello 2003). For example, among other tests, the children had been asked to say what they thought was in an egg box, which turned out to contain a toy car, not eggs. When asked what another child would believe was in the egg box, they had predicted the child would think it contained a toy car; in other words, they were unduly influenced by what Tomasello calls the “pull of the real.”

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Children who had failed the false-belief tests in this way were randomly assigned to four different training conditions. In the control condition, the children were simply given similar experiences with “deceptive” objects, such as a pen that looked like a flower, without any overt discussion of beliefs. In the other conditions, the experimenters engaged the children in discussions aimed at highlighting the difference between their beliefs before and after learning the true nature of the deceptive objects. In the “full” training condition, for example, the children were first shown the deceptive object and asked to say what they “thought” or “believed” it was. After the experimenter demonstrated the true nature of the object (a pen that resembled a flower), the children were asked to recall their previous beliefs and to predict what a third person (played by a puppet) would believe before and after having the true nature of the object revealed. As it turned out, only the children in the control condition, the ones who had not been engaged in epistemic discourse, failed a post-test on their understanding of false beliefs—all the others significantly improved their understanding. It is in the context of this kind of talk, the researchers concluded, that children come to understand that others have perspectives of reality, and thus beliefs, that may be different from one’s own. Knowledge, in other words, now becomes relative to one’s perspective.

The Child as Egalitarian “Multiplist” In the next transition, which typically occurs at an early point in middle childhood (6–12 years), recognition of the subjective nature of belief becomes dominant, to the point that critical thinking about objective reality may seem irrelevant. This is what Kuhn calls the multiplist , egalitarian stance toward knowing. People have different experiences, knowledge is a matter of opinion based on these experiences, and therefore people are entitled to their own opinions, which are equally valid. Because the assertions of others are no more than opinions, assertions are neither true nor false. A person’s own opinion is all that matters. Expertise has no special standing, and there is no important distinction between fact and opinion, nor between theory and evidence. Whereas

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before, in the “realist” stage, young children were influenced by the “pull of the real,” now they are influenced by the “pull of the norm.” Indeed, at a time in their lives when children spend a good deal of their waking hours interacting with other children, in mixed-age groups, many of them near peers, it makes sense that children might come under pressure to adopt the group’s beliefs, even if these might not accord with their own developing sense of reality.

The “Evaluativist” Perspective At some point children and young adults may make (but, as we’ll see, do not necessarily make) another transition, to an evaluativist perspective. At this stage in the development of epistemic understanding, it is still understood that beliefs are subjective, and that knowledge, generated by human minds, is uncertain. However, assertions now begin to be understood as judgments, which are open to evaluation and argumentation. Critical thinking is now valued as a way of attaining deeper (though never perfect) understanding of the nature of the world and how it works. People have different opinions and beliefs, and some are more popular than others, but some beliefs are more fully grounded in evidence. One’s own beliefs may be wrong, but so may the normative beliefs of the group. Productive civil discourse with other “evaluativists” is now possible.

The “Inclusivist” Perspective Although this is not part of Kuhn’s model, we can also postulate a fifth “inclusivist” stage in the development of a person’s epistemic understanding, which we can say is characterized by the recognition that different people have different ways of knowing. Understanding this, people with an inclusivist perspective potentially have a better chance of engaging in productive civil discourse with those whose ways of knowing are different from their own. Full-blown epistemic fluency, in other words, includes a rich repertoire of discourse moves associated with multiple ways of knowing. For example, compare the following:

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1. We can’t know for sure what God has planned for us. 2. It’s impossible to predict the future with perfect accuracy. Note that the two assertions express nearly equivalent ideas, but different ways of knowing. Making use of such knowledge, people with different ways of knowing can potentially engage in productive civil discourse with each other without having to abandon their own epistemic beliefs. As suggested in Fig. 10.1, these different levels of epistemic understanding do not replace each other over the course of an individual’s cognitive development. Rather, they are additive and adaptive. At the most advanced stage, individuals may draw from all five levels of understanding. For example, although older children and adults may no longer trust external authority absolutely, a certain level of trust in the expertise of others is necessary and makes good sense. Older children and adults continue to trust their personal expertise to some large degree. And although they may come to understand that some beliefs are more fully supported by empirical evidence than others, they may also retain respect for the differing beliefs and experiences of others, and the different ways that others construct knowledge. The key point: humans pass through distinct developmental stages in their understanding of the relationship between objective reality and subjective experience, and between their own beliefs and the beliefs of others. As in human development generally, the process is shaped by a combination of nature and nurture. Moreover, the nurture part— cultural influence—becomes increasingly dominant as we grow up. Whereas all “cognitively typical” children naturally come to discard their early sense that everyone has the same beliefs, only some come to understand, through social learning, that beliefs are testable, and that some beliefs turn out to be more firmly grounded in evidence, and therefore more worthy of belief.

Fig. 10.1 Stages in the development of epistemic understanding. Note Estimates for the onset of the multiplist, evaluativist, and inclusivist stages are strictly hypothetical

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Epistemic Understanding in Evolutionary Time As I suggested at the outset, it’s worth considering to what extent the development of epistemic understanding and a dedication to epistemic vigilance in children and young adults in modern times and cultures reflects its evolution in our species. As it turns out, there’s a pretty good fit. In the early going, when our hominin ancestors first set off down the road toward language and civil discourse, long before developing the capacity for symbolic reference, these ancestors would, like all nonhuman animals, and somewhat like young children, have lived their mental lives largely in the here and now, meaning that knowledge about the world must have been directly dependent on observed objective reality. In such a world, it was not a matter of “opinion” that leopards are dangerous, that a certain kind of plant with purple stems, toothed leaves and spiky fruits can kill a child who eats it, or that the figs on the trees 5000 paces to the northeast have now ripened. In the early going, presymbolic hominin communication, rooted as it was in the here and now, must have been honest, not only because Machiavellian subterfuge would have undermined its utility to the group as a whole, but also because reality checks would have been too easy. It seems that the transition from a realist to an absolutist epistemology must have followed next in phylogeny, and likely early on, just as it does in ontogeny. Recall that the ability to detect false beliefs in others, the hallmark of the absolutist stance, is dependent on an emerging perspective-taking capacity. In order for me to understand that the child who was out of the room when the researcher took the candy out of the box and replaced it with pencils will think it still contains candy, I have to understand that other children have minds of their own, with different thoughts and beliefs. Recall also that a human-like theory of mind must have coevolved with language, as a fundamental component of the human adaptive suite. If I can compare your assertions about whether a certain plant is edible against my own personal experience with the same plant, I can begin to exercise some epistemic vigilance. Note that at the beginning of this absolutist stage, we can still operate in the here-and-now. We’re not

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necessarily at the point where your assertions need to involve symbolic reference for me to identify what I consider your false belief that the fruit you’re asking me to reach for you is edible. Just the act of your pointing and making a “begging face” can tell me what you’re thinking, so long as I can imagine you might be thinking anything at all. However, for language to grow more sophisticated, more readily revealing the mind’s contents, and giving it more varied content—then mindreading, and the neural circuitry that supports it, must have become more sophisticated. The next transition, from the absolutist to the multiplist stance is critical, because it is at this point, I think, that we can see the beginning of a transition from innate, biologically determined capacities and inclinations to ones that are at least partially culturally determined. First, recall that the distinguishing characteristic of the multiplist stance is the belief not only that people have different beliefs, but that all beliefs are equally valid (“Everyone is entitled to his or her own opinion…”). In other words, this way of thinking, which honors the intellectual contributions of all group members equally, is a fundamentally social approach to the construction of knowledge. As such, a multiplist stance may be understood as deeply connected to the uniquely human combination of (a) cooperation (in hunting, foraging, and child-rearing); (b) egalitarianism (especially as it relates to food sharing); (c) mindreading; (d) language; and (e) cultural transmission of knowledge and skill through language, which, as I argued in Chapter 5, had likely begun falling into place, in the form of a protolanguage, by at least some 1.8 million years ago (see also Whiten and Erdal 2012). In this context, as a fundamental component of the emerging human adaptive suite, an egalitarian, “multiplist” approach would have conveyed a selective advantage to the groups that embraced and practiced it. Indeed, it is not hard to imagine that treating the opinions and ideas of all group members equally is a good strategy for a bipedal, nomadic group of hunter-gatherers who regularly encounter new challenges and opportunities as they move from one foraging range into another. It’s notable that egalitarianism, and particularly the idea that children should be encouraged to explore the world on their own, without overt adult instruction, is a characteristic feature of remaining small-scale, hunter-gatherer cultures (Boyette and Hewlett 2017).

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If the emergence of an egalitarian epistemological stance is at least partly biological, then we ought to see a universal developmental trajectory, similar to the emergence of teaching in children at around the age of 4 (Strauss and Ziv 2012). Indeed, as reported by Kuhn, the transition from the absolutist to the multiplist stance occurs at roughly the same time as the transition from the primarily selfish motives typical of 3–4 year olds, to an emphasis both on sharing and adherence to group norms, which is largely in place in modern human children by the age of 8 (Fehr et al. 2008). Recall again that this also the period when children around the world begin spending much of their time immersed in peer culture, surrounded by children who are not much younger or older than themselves. Honoring the rights of others to their own beliefs and opinions makes especially good sense where the distance between experts and novices is narrow. This is not to say that cultures, including youth cultures, don’t promote certain beliefs as norms; in fact, it’s arguably the very recognition that different individuals can embrace different opinions and beliefs that may inspire cultural leaders to promote one set of beliefs and practices over another, which may be necessary for true cooperation to take place. Even children’s informal games have rules, and wouldn’t be possible without them. Further, in borderline cases (“Is the arrow more in than out of the bullseye?”), it’s important to have someone with sufficient authority make a judgment. But if all are entitled to their own opinions, and if the credibility of an opinion is simply a matter of authority and power, there’s a problem. Conventional wisdom may be misguided. Traditional ways of doing things may be inefficient. Received knowledge may be wrong, perhaps fatally. Without epistemic vigilance on the part of individuals, the group may follow a charismatic leader off in a dangerous new direction and perish. This brings us to the next transition, from the egalitarian, multiplist stance to the evaluativist one, in which beliefs are considered more or less valid depending on, among other things, the preponderance of available evidence. In a sense, this is a return to realism. If I can show that mixing powdered ochre into an adhesive makes a better bond for hafting a stone spearhead to its handle, the whole group can benefit.1 The exchange of

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new ideas encouraged by an egalitarian approach is a good thing, but without competition, and a way of judging among competing ideas, potential progress can be limited. Science is impossible. So is the rule of law and evidence-based policymaking. Unfortunately, the transition from the idea that all beliefs are equally valid, to the idea that some beliefs are more fully supported by evidence than others is not so easy to make, even in our own age. This is evident, for example, in Kuhn’s finding that only about half the adults in her study had made the transition. For example, asked to explain the causes of school failure, one adult subject, Frank, stated his belief that problems at home, specifically divorce, were the primary cause of school failure. When the interviewer asked for evidence, the conversation went like this (Kuhn 2002: 127): Interviewer: How do you know that this is what causes children to fail in school? Frank: Well, it’s like mostly when the mother and father are divorced they can have psychological problems, you know, and they can’t actually function in school. Interviewer: Just to be sure I understand, can you explain exactly how this shows that problems at home are the cause? Frank: Well, the kid, like, concentrates on how he’s going to keep his mother and father together. He can’t really concentrate on schoolwork.2

In other words, Frank seems not to recognize that the interviewer’s question (“How do you know…”) is a request for evidence of a causal relationship between divorce and school failure, not just a restatement of that relationship. Admittedly the distinction is subtle. Frank’s response would have been appropriate if the interviewer had asked “Why do the children of divorced parents have difficulty in school?” but not “Why do you think…” But even when the interviewer presses for evidence (“…can you explain exactly how this shows that problems at home are the cause?”) Frank fails to provide the requested evidence, again simply restating his theory. Why the failure? Is it because Frank lacks the cognitive capacity to distinguish between theory and evidence as the result of some mental

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deficiency? Or is it because, much like a checkers player playing chess with a chess expert, Frank doesn’t recognize the interviewer’s request for evidence (“How do you know…”) as a move in the theory-and-evidence game, quite possibly because he hasn’t often had a chance to play the game, that is, to engage in true civil discourse with others who recognize its rules?

Epistemic Fluency and Cultural Transmission The chess analogy is useful here, but only to a limited extent. Like epistemic fluency, or language itself, chess expertise is not a single entity that one either has or doesn’t. True, chess beginners must learn something like twenty rules, including how each of the pieces move and capture other pieces—different for pawns, knights, bishops, rooks, queen, and king—and a few other conventions such as the special double move of the king and rook called “castling.” But chess expertise consists not only in knowing these basic rules, but in understanding how certain complex combinations of moves can lead to checkmate, and how to defend against them. This comes with experience, not a rule book. Chess expertise may also be understood as developing in stages, with labels such as “beginning,” “intermediate,” “expert,” and “master.” In the same way, epistemic understanding, and therefore the ability to engage in true civil discourse, develops in stages: from the blind trust of infancy and early childhood, through the “everyone has their own opinion” egalitarianism of middle childhood, to the potential transition, not always achieved, to the multiplist stance in later childhood and adulthood; and the even rarer transition to the understanding that different people have different “ways of knowing,” and that engaging in civil discourse with others who have different ways of knowing than your own is difficult but not impossible. Of course, the absolutist, realist, egalitarian, evaluative, and inclusivist phases are really no more than convenient labels. As with chess, you’re only an expert to the extent that you’re capable of making expert moves. Like chess expertise, advanced epistemic fluency consists of certain general dispositions and habits of mind, such as an acknowledgment

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of uncertainty, and a willingness to change one’s beliefs in the face of someone else’s compelling, evidence-based argument. But it also involves an intuitive understanding of the nature and use of the epistemic forms we discussed in Chapter 9—such as categories, definitions, maps, stories, and theories—as well as an appreciation for the inherent ambiguities and fault lines in each. Another way of saying this is that epistemic vigilance pays special attention to certain customary “suspects.” Here are some examples: 1. 2. 3. 4. 5. 6. 7. 8.

Any given list of items may be incomplete. Not everything fits neatly into one category or another. Not all maps are accurate. Some procedures get better results than others. Not all plans are equally likely to succeed. Not all stories are true. Some theories have a stronger base of evidence than others. Some solutions are more likely to solve a complex problem than others.

In other words, while epistemic forms usefully represent and organize knowledge of the world in conventional ways, making it possible to transmit knowledge, packaged as language, from one brain to another, epistemic forms remain just that: structures for organizing knowledge of the world, not the world itself. The world itself remains infinitely various, ever-changing, full of pleasant and unpleasant surprises. Just walk a few kilometers toward that mountain and a clever plant that looks almost exactly like the one you’ve been eating with pleasure for years in the immediate vicinity now makes you violently ill. Wrong category. Watering holes dry up, sea levels fall and expose new land bridges, the coastline bends farther to the south than you’d thought, so now you need to revise your mental map. Cold weather starts to set in earlier each year, migrating birds arrive later, the soil becomes drier, and so seasonal planning needs to change. Your uncle’s hunting stories grow more elaborate with every telling. Did he really kill two antelope with a single spear thrust?

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There’s another, even more basic reason why conventional wisdom and stores of knowledge must be treated with skepticism, at least by some members of the group. The crank-and-ratchet mechanism that drives technical progress and the accumulation of life-sustaining cultural knowledge and skill (Tomasello et al. 1993) has two critical components. One, the ratchet, transmits existing cognitive capital from one generation to the next through processes of social learning, including intentional instruction. This prevents slippage and cultural forgetting. But the second component, represented by the crank, feeds on innovation, and the successful integration of innovations into the group’s existing store of cognitive capital. More specifically, it requires modification of existing technologies, creation of new categories and names, revision of existing maps (initially, mental maps) with new routes and locations, revision of existing theories in the face of new evidence, invention of new approaches to problems, and so forth. Further, in a species such as ours, which depends on joint intentional activity and distributed, collective cognition, the successful adoption of innovations depends not only on the new insights and ideas of individuals, but on the ability of individuals to get their new ideas accepted, and the willingness of other members to accept them. In a language-using species such as ours, this depends on a willingness and ability to engage in productive civil discourse. Another way of saying this, building on a theme established in Chapter 5, is that evidence of significant technical accomplishments may be taken as evidence of significant civil discourse.

Civil Discourse and the Colonization of Australia With these ideas in mind, let’s go back and review the apparent course of technical innovation in our species. When do we first begin to see evidence, not just of language, but of civil discourse? As discussed in Chapter 5, the earliest evidence for the intentional manufacture of stone tools is currently dated from 3.3 million years ago at a site in Kenya (Harmand et al. 2015). At around 1.8 million years, we

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see the emergence of a significantly more sophisticated method of manufacture: the Acheulean tool industry. Findings from reverse engineering of the knapping technique employed to produce these slightly more complex tools, together with experiments based on various methods of teaching these techniques to novices, suggest the possibility that a protolanguage may have emerged at around this time (Morgan et al. 2015). As we’ve also discussed, a protolanguage is likely to have involved simple combinations of gestures (notably, finger points as a means of directing attention), facial expressions, and vocalizations. The capacity for symbolic reference, high-speed speech, and complex syntax—all arguably prerequisites for modern forms of civil discourse—would have been waiting in the distant future. Indeed, the persistence of the Acheulean industry for well over a million years suggests that language, and the culture it supported, may not have changed substantially over that time, possibly constrained by limits imposed by existing brain design and the absence of sufficient selection pressure to push through these limits. But then, beginning as long as 500,000 years ago, in Homo heidelbergensis, we begin to see evidence of significant growth in cranial capacity, more sophisticated technology (the Mousterian tool industry), leading before long to the emergence of a new species—Homo sapiens—beginning around 300,000 years ago at sites throughout Africa (Scerri et al. 2018). Modern humans may have reached the coast of southern China as early as 194,000 years ago (Michel et al. 2016). But the most compelling evidence for an emerging capacity for civil discourse appears later. Around 65,000 years ago, modern humans began to colonize Australia, which required an ocean crossing of some 70 km (Balme 2013). Within the next 40,000 years, the settlers had spread widely across the new continent, leaving archeological evidence of the same behaviors their ancestors had begun to practice back in Africa at the beginning of the exodus, including personal ornamentation, ritual burial, long-distance trade, and composite tools (Davidson 2010). …an ocean crossing of some 70 km. The magnitude of this feat is arguably the best, earliest evidence yet for the emergence in our species of distributed cognition through civil discourse. It’s not unlike the evidence

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of the moon landings of the 1970s, or the hypothetical appearance on Earth of an extraterrestrial spacecraft, both of which, in themselves, would provide strong, indirect evidence not just of intelligence, but of collective intelligence and shared cognitive effort through civil discourse. It may be that the relative complexity of the Acheulean tool industry compared to the Oldowan hints at the presence of an early form of language, which would have made it considerably easier for experts to help novices master the requisite skills. But it wouldn’t have been necessary, and probably not possible, to discuss the relative advantages of the more time-consuming Acheulean techniques over simpler, less effective Oldowan ones. Our ancestors wouldn’t have needed modern language to teach Acheulean-style stone knapping, just the capacity for some simple communicative acts such as pointing and giving positive and negative feedback. But an ocean crossing of 70 km or more, across the stormprone, reef-ridden waters of the Timor Sea, would have been another matter. As we’ll see, such an accomplishment may be interpreted as a strong signal not just for the presence of language, but for an ability to use language to plan and coordinate the movements of a human group sufficiently large and technically equipped to form a viable founding population on the mainland. That couldn’t have been easy. These people, I will argue, certainly had language, and almost certainly the ability to use language to think, and think aloud with others, in something very much like modern ways. Part of the argument is a no-brainer. We know that the indigenous peoples of Australia, the direct descendants of the original colonists, were already exhibiting modern behaviors, including, crucially, fullblown modern human language, prior to the arrival of the first European settlers more than sixty thousand years later, in the late eighteenth century (by which point an estimated 407 different languages had sprung up; Bowern 2016). The same is true, of course, of indigenous peoples throughout the world, including the descendants of those who remained behind in Africa. Any modern human child, born anywhere in the world, whether at Brigham and Women’s Hospital in Boston, Massachusetts, an Aboriginal camp in the interior of Australia, or a remote village in the Amazon, still largely protected from Western contact, will grow up speaking whatever human language is spoken by her caretakers. And if

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one of my own human children, either the one born at the Brigham or the one born at the Nethersole, in Hong Kong, had, God forbid, been snatched from us and raised by another set of parents among Makú speakers in the Amazon, she would without doubt now be fluent in Makú.3 Unless we suppose that an identical capacity for modern language arose independently in widely scattered indigenous populations throughout the world, it must be that the original Australian settlers already had modern language. (Recall from Chapter 5 that 65,000 years is well after even Chomsky’s estimate of 100,000 years for the emergence of modern language.) So, we can strongly suspect that as they stood on the shore of some island in the Indonesian archipelago, looking off over the ocean to the south, in the direction of an unseen continent—which, as they could not yet have known, was teeming with easy prey—the future colonists would have been speaking a full-blown human language, with all modern features, including high-speed speech, massive symbolic reference, and complex syntax. Their brains were our brains, and likely had been for thousands of years. The question, then, is not whether the Australian colonizers had full-blown language, but whether they were capable of using their language for distributed cognition through civil discourse. Where were they, in other words, on the road from epistemic realism to egalitarianism to full-blown epistemic fluency? Were they more like young children, willing to believe whatever they are told by their parents, or anyone else? More like older children, who believe critical thinking is unnecessary, because everyone’s opinion is equally valid? Were they more like Frank, with his apparent inability to distinguish between theory and evidence? More like the mother on the subway train who refused to be drawn into civil discourse with her son? More like the other mother, who felt obligated to respond as best she could to her son’s request for an explanation concerning the cause of the screeching of the subway train wheels? Or more like the team of engineers who came up with the plan to solve the problem by spraying water on the tracks? We can’t know for sure, of course, because, as we’ve seen, while all human beings have the potential to engage in productive civil discourse, as a biological inheritance, advanced epistemic understanding

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and fluency develops under the influence of other thinkers, and is therefore partly cultural. We know that the colonizers had the brain hardware, but what about the software: their minds? How did they think? What sort of mental effort did it take to get themselves across those 70 km of open water to the new land? And to what extent was the mental effort shared through discourse? One possibility is that it didn’t take much mental effort at all because the crossing was an accident. We can assume the colonists had previously settled on or near the shore of one of the Indonesian islands. A typhoon or tsunami could have washed a group out to sea, and some, clinging to debris, might have drifted across the Timor Sea, washed up on the shore of the strange new continent, regrouped, and survived long enough to establish the first colony.4 While a logical possibility, it seems that an accidental colonization event is unlikely. For one thing, to serve as a viable founding population, the group would have to have been sufficiently large, with the right sort of demographic structure. Specifically, and most obviously, it would have needed a combination of men and women, preferably of different ages (older ones to look after younger ones), ideally with the same age structure as a typical foraging band, and with sufficient store of technical expertise. At the same time, a family group alone wouldn’t do, because you’d need potential new mates from a different family. As discussed in Chapter 8, remaining hunter-gatherers live in endogamous (mating) communities of about 500 members. If the colonists didn’t wash up all at once in a group approaching that size, they would have needed a series of closely-spaced accidents, each one contributing some missing piece of demography. Another problem, given the lack of evidence that any other hominin species ever made it to Australia, is that we need to explain why groups of Homo erectus, which had been in Indonesia much longer—as many as two million years—didn’t have any such fortuitous accidents. Quite likely these groups had a protolanguage, and they certainly would have had no difficulty exploiting the resources (giant sloths, etc.) on the mainland if they’d made it across. But there’s no evidence so far that H. erectus ever made the crossing, whether by accident or design.

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A third problem with the accidental colonization scenario is that another scenario makes much more sense: that the colonization of Australia by modern humans some 65,000 years ago was fully intentional, largely (though perhaps haphazardly) planned, and grew out of an existing technology: deep-sea fishing with boats and nets. And, I’d like to argue, neither the technology, the planning, nor the execution of the plan would have been possible without a capacity for distributed cognition through civil discourse. The argument goes like this. First, the coastal waters and coral reefs around the islands of Southeast Asia would already have supplied rich sources of protein in the form of fish, mollusks, crustaceans, and echinoderms such as starfish and sea urchins. We know that’s what the other hominin populations in the vicinity must have been exploiting. Of these, fish, especially larger ones, would have offered the best single package of protein and other nutrients, but they would also have been the most difficult to catch. And the biggest fish, such as tuna, were farther out to sea, well beyond wading distance. However, at this point, having been island hopping across Southeast Asia for thousands of years, there is evidence that the new arrivals would likely already have developed boats or rafts of sufficient seaworthiness to make it out into the open water—after all, there is evidence that even H. erectus had some form of watercraft (Gibbons 1998). Further, skeletons of deepwater fish such as shark and tuna have been found at Jerimalai Cave on East Timor, a site inhabited by modern humans some 42,000 years ago, which, while the earliest evidence so far, was unlikely the first successful attempt at deepwater fishing (see O’Connor et al. 2011). A question is how they caught such fish. Given that fishhooks big and strong enough to land large pelagic fish don’t show up until quite a lot later, it’s more likely that the Australian colonizers were using nets made with knotted, twisted fiber. Because fiber is highly perishable, the evidence is necessarily indirect. Some of the earliest physical evidence for the use of string, and probably also knots, comes in the form of pendants, beads, and other objects with perforations suggesting the use of string (Hardy 2008). These start showing up around 300,000 years ago in Africa, so it’s almost certain that the modern humans who colonized Australia 250,000 years later would have had a fiber-based technology,

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which they could have used for both lashing together wooden rafts (or sailing canoes perhaps not unlike the ones used to this day by seafaring peoples of the western Pacific; see Gladwin 1970) and for making nets. More direct evidence that the colonists had been making nets for deepsea fishing comes from the remains of fish found at 30,000-year-old sites in New South Wales, Australia, where the fish were all the same size— consistent with the size of deepwater fish caught with modern gill nets (Balme 2013). Adding all of this up, it seems likely that the modern humans who first settled on the Australian continent probably didn’t arrive there by accident, or as a single Mayflower-style colonizing expedition. It’s far more likely they already had an established deepwater fishing culture involving the use of strong nets and seaworthy boats, which, using paddles and/or sails, they could control sufficiently to get safely out to sea and back. Eventually they would have gotten to know the waters farther and farther off the coast, the location of reefs and islands, the prevailing winds and currents, the habits of different seabirds, and the steady, predictable drift of constellations in the night sky. At some point the fishermen would have seen the shoreline of Australia in the distance. At some point they would have started making landings and setting up temporary camps. At some point the camps would have become permanent bases with resident families. At some point these people’s store of names for plants and animals, already extensive, would come to include words for a 450pound, 6-foot kangaroo, a tiger-sized marsupial lion, a flightless bird twice the size of an ostrich, and a two-and-a-half ton wombat (see Harari 2015: 65). Within a few thousand years of the arrival of humans, these animals were extinct, but we were there to stay.

The Beginnings of Epistemic Diversity So, what does all of this have to do with the case for the emergence of civil discourse? To what extent were the Australian colonizers capable of debating the pros and cons of different plans of action? Of discussing the accuracy of their mental maps? Of discriminating between theory and evidence? Of separating evidence-based policy from politics?

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If the language behavior of twenty-first-century humans is any guide, it seems the best answer is that there would have been a mix. Because their brains were not much different from the brains of our own children and grandchildren, children in the late Pleistocene likely would have passed through the same early stages of cognitive development and epistemological understanding that we do now. Very young children would believe anything they were told, but as they got a little older, thanks to developing perspective-taking circuitry and language capacity, they’d come to recognize the possibility of false beliefs in others. At some later point, under the influence of the human genetic disposition for cooperation and sharing, combined with the traditional egalitarian hunter-gatherer cultural norms that persist to this day, they would next have entered, and possibly remained in, the frame of mind that considers everyone’s opinions to be equally valuable. And, on a passage across the choppy sea in an open watercraft, it’s not hard to imagine that a mother would tell a fidgety child to sit down and stop asking questions. Why? Because she’d said so. But the very nature of a deepwater fishing culture implies a level of technical sophistication, distributed cognition, collaborative planning, and problem solving that would not have been possible if everyone’s opinion counted equally, if the power of authority was always greater than the power of a good argument, and if theories were not tested against available evidence. If the Age of Reason had not already dawned 65,000 years ago, a 450-pound kangaroo might still be hopping about its business as if nothing had happened. Okay, maybe not. But I assume you get the point. Successful deepwater fishing with woven nets strong enough to contain a thrashing tuna or shark requires collaborative planning, collective physical effort, and group thinking. Someone who knows what to look for needs to be scanning the ocean surface for signs of fish. Others need to be paddling or working the sails. Others need to handle the nets. Someone needs to navigate, keeping track of the craft’s location with respect to known waypoints, reading currents, and keeping watch for reefs and approaching storms. Once large, flopping, sharp-finned fish are brought aboard, someone needs to be ready with a club or rock.

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And at every point, decisions need to get made. Should we head over there, where those seabirds seem to be feeding, or stay right here, where we saw some tuna just a few minutes ago? Are those clouds on the horizon storm clouds? Are they coming in our direction? Should we head back now, or try our luck and stay a little longer? Do we have enough twine on board to mend the nets? Sometimes the answers to such questions might have been obvious, hardly worth discussing, but at other times they would have been very much worth discussing, partly to avoid mutiny, but also because the best answer—which might well make the difference between returning safely to harbor with at least some fish on board and not returning at all— is most likely to arise, if at all, from the shared knowledge, expertise, and ideas of the collective which, in the absence of telepathy, requires thoughtful talking and listening. When the going gets tough, and the alternatives are all imperfect, it’s not enough just to express an opinion. You have to be prepared to explain yourself. I could go on. It’s not a small thing to build a steerable boat, from available materials, say bamboo poles and lashing, capable of getting you and a sufficiently large crew safely out on the open ocean and back. It’s not a small thing to make string from twisted fiber, knot the strings together in just the right way to make a strong net with openings just the right size to snag a tuna. There’s a lot of teaching and learning in that. There are wrong ways, right ways, and, if you can figure them out, better ways. That also takes discussion. As does the decision to break camp and move to a different location, let alone a location in a new land 70 km across open ocean. Of course modern humans would later embark on even more challenging crossings, not least the three-day, 250,000-mile crossing to the surface of the moon, the work of some 400,000 scientists, engineers, and technicians, and countless hours of productive civil discourse. But by 65,000 ago, thanks to language, teaching through language, and a capacity for civil discourse, our ancestors were already navigating their way, driven by the winds of their collective intelligence, into an uncertain future.

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Food for Thought 1. Do you recall passing through stages of “epistemic understanding” in your own childhood? 2. What have you observed in your own children, or the children you’ve had the good luck to be around? 3. What about other adults in your world? 4. Why aren’t we all “inclusivists?” 5. Do you agree with my claim that the original colonizers of Australia must have been capable of engaging in civil discourse? If not, what is your counter-argument?

Notes 1. On the use of ochre as an adhesive for use in stone tool manufacture, see [Lombard 2007]. 2. Kuhn, D. (2002). Thinking as argument. In L. Smith (Ed.). Critical readings on Piaget (pp. 120–146). London, NY: Routledge. 3. The name of this language is reportedly a pejorative, meaning, ironically, “without speech.” The point I am making here is that no cognitively normal human being in any human culture is without speech. 4. You might think they could have walked across on a land bridge from Indonesia. However, evidence suggests that sea levels were never that low. See Balme (2013).

Suggested Reading Balme, J. (2013). Of boats and string: The maritime colonisation of Australia. Quaternary International, 285, 68–75. Discusses the evidence that the initial colonization of Australia would have required an ocean crossing of at least 70 km, and suggests that the use of fiber technology, in addition to complex cognition and communication, would have played a major role.

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Davidson, I. (2010). The colonization of Australia and its adjacent islands and the evolution of modern cognition. Current Anthropology, 51(S1), S177–S189. Argues that the first colonization of Australia is evidence that humans had “modern cognitive ability” by this time, likely having first emerged among African ancestors of the colonizers. Kuhn, D., Cheney, R., & Weinstock, M. (2000). The development of epistemological understanding. Cognitive development, 15( 3), 309–328. The source of the account I give in this chapter of the stages of epistemic understanding in human children.

References Balme, J. (2013). Of boats and string: The maritime colonisation of Australia. Quaternary International, 285, 68–75. Bowern, C. (2016). The Australian comparative lexical database. Language Documentation and Conservation, 9, 1–45. Boyette, A. H., & Hewlett, B. S. (2017). Autonomy, equality, and teaching among Aka foragers and Ngandu farmers of the Congo Basin. Human Nature, 28, 289–322. Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13(4), 148–153. Davidson, I. (2010). The colonization of Australia and its adjacent islands and the evolution of modern cognition. Current Anthropology, 51(S1), S177– S189. Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature, 454 (7208), 1079. Gibbons, A. (1998). Ancient island tools suggest Homo erectus was a seafarer. Science, 279 (5357), 1635–1637. Gladwin, T. (1970). East is a big bird: Navigation and logic on Puluwat. Cambridge, MA: Harvard University Press. Harari, Y. N. (2015). Sapiens: A brief history of humankind (p. 65). New York: HarperCollins. Hardy, K. (2008). Prehistoric string theory: How twisted fibres helped to shape the world. Antiquity, 82(316), 271–280.

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Harmand, S., Lewis, J. E., Feibel, C. S., Lepre, C. J., Prat, S., Lenoble, A., et al. (2015). 3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya. Nature, 521(7552), 310–315. Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67 (1), 88–140. Kuhn, D. (2002). Thinking as argument. In L. Smith (Ed.). Critical readings on Piaget (pp. 120–146). London, NY: Routledge. Kuhn, D., Cheney, R., & Weinstock, M. (2000). The development of epistemological understanding. Cognitive Development, 15 (3), 309–328. Lohmann, H., & Tomasello, M. (2003). The role of language in the development of false belief understanding: A training study. Child Development, 74 (4), 1130–1144. Lombard, M. (2007). The gripping nature of ochre: the association of ochre with Howiesons Poort adhesives and Later Stone Age mastics from South Africa. Journal of Human Evolution, 53(4), 406–419. Michel, V., Valladas, H., Shen, G., Wang, W., Zhao, J. X., Shen, C. C., et al. (2016). The earliest modern Homo sapiens in China? Journal of human evolution, 101(10), 1e104. Morgan, T. J. H., Uomini, N. T., Rendell, L. E., Chouinard-Thuly, L., Street, S. E., Lewis, H. M., et al. (2015). Experimental evidence for the co-evolution of hominin tool-making teaching and language. Nature Communications, 6, 6029. O’Connor, S., Ono, R., & Clarkson, C. (2011). Pelagic fishing at 42,000 years before the present and the maritime skills of modern humans. Science, 334 (6059), 1117–1121. Perner, J. (1991). Understanding the representational mind . Cambridge: MIT Press. Scerri, E. M., Thomas, M. G., Manica, A., Gunz, P., Stock, J. T., Stringer, C., et al. (2018). Did our species evolve in subdivided populations across Africa, and why does it matter? Trends in Ecology & Evolution, 33(8), 582–594. Strauss, S., & Ziv, M. (2012). Teaching is a natural cognitive ability for humans. Mind, Brain, and Education, 6 (4), 186–196. Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. Behavioral and Brain Sciences, 16 (03), 495–511. Whiten, A., & Erdal, D. (2012). The human socio-cognitive niche and its evolutionary origins. Philosophical Transactions of the Royal Society B: Biological Sciences, 367 (1599), 2119–2129.

11 Into the Uncertain Future

Having devoted so much typing to the question of how humans, over the course of 6–10 million years, came to be who we are now—a unique species of walking, talking, teaching, ape—it seems important for us to think at least a little in this final chapter about the present, and where certain unsettling present trends may lead us in the uncertain future. I don’t know about you, but here are some of the things I try not to worry about too much, as if one could stop: 1. Global pandemics, spread through high-speed global transportation systems. 2. Climate change and its impact on human populations. 3. Unequal distribution of wealth and educational opportunity. 4. Armed conflict, terrorism, forced migration, genocide. 5. Denigration of science, political polarization, the ascendance of politics over policy, and attacks on the integrity of democratic institutions around the world. 6. Unintended consequences of social media, such as Facebook, Twitter, and Instagram on the quality of our civil discourse. © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_11

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Ironically, it seems clear that all these present threats trace back, directly or indirectly, to our collective, species-unique capacity for teaching, learning, and civil discourse through language. Only supersized, language-shaped brains—and many, many generations of language-using teachers and learners—could have created such difficult, supersized problems. It seems the more we know about the nature and origin of teaching among humans, the greater our obligation to think about the unfortunate consequences of our ascendance as a species, and what, if anything, can be done about the perilous situations we’ve put ourselves in. Because runaway global warming is arguably the biggest long-term threat, and because it’s entangled with so many of the other issues on my worry list, let’s start with that. As of 2016, some 97% of climate scientists agreed that recent global warming trends, especially over the past 50 years, are the result of human activity (Cook et al. 2016). Primary contributors to the problem include: combustion of fossil fuels (notably oil, coal, and natural gas) as sources of energy, tropical deforestation, and industrial farming of cattle and other livestock. All of these activities raise levels of atmospheric, heat-trapping greenhouse gases—mainly carbon dioxide, methane, and nitrous oxide. Environmental impacts include rapid melting of the polar ice caps, rising sea levels, loss of biodiversity, extreme weather events (storms, extremes of heat and cold), droughts, wildfires, and reduced crop yields (Lissner and Fischer 2016). These in turn wreak havoc—and threaten to wreak even greater future havoc— on human populations, particularly those of us with the misfortune to inhabit low-lying coastal areas, flood plains, and drought-prone rural areas, especially near the equator. Harms include famine, deteriorating health, forced migration from the countryside to cities and from poorer countries to wealthier ones—all of which put pressure on local resources and support systems (schools, hospitals, systems of social welfare), and inevitably lead to harmful social unrest. As with so many of our problems, global warming and its disastrous consequences—for both humans and our fellow creatures—is almost entirely the result of human ingenuity, distributed cognition, and teaching. No other animal has the intelligence and ability to organize the construction of coal-burning power plants, cut down rainforests with chainsaws at the rate of 20,000 square miles per year, or raise massive

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herds of methane-spewing beef cattle for its dining pleasure.1 If we’d remained the ordinary, small-brained apes we were just a few million years ago, none of this would have happened. And yet no other animal has the intelligence to measure—using sophisticated scientific instruments—current and historical levels of atmospheric carbon dioxide. No other animal can coordinate international scientific measurements of global temperatures and sea levels. No other animal can build computer models to project historical climate trends into the future. No other animal can construct testable theories about the causes and consequences of weather patterns, publish the findings in peer-reviewed scientific journals, and engage in intelligent civil discourse about the results. And no other animal can propose global solutions to the problem of global warming, and cooperate with others to implement those solutions. But, despite the obvious threats to human civilization, and despite our potential ability to solve our problems through the same kind of technical ingenuity and innovation that created them, the human response to global warming has been tepid and slow, quite possibly disastrously slow. Why? One answer may be that the human brain, arguably the most complex and intelligent biological system in the known universe, is crippled by a serious, possibly fatal, design flaw. We’re smart enough to create problems that threaten our very existence, but we may not be quite smart enough, collectively, to solve them. Evolution seems to have left us, as a species, cognitively overextended, in a state of dangerously arrested intellectual development.

Science, Education, and Politics Here’s my argument. We can begin with the fact that only about half of adults in the United States agree with the scientific consensus that climate change is caused by humans, the lowest among 20 countries surveyed (Plutzer et al. 2016). What explains this discrepancy? While the reasons for the gap between science and public opinion and understanding are complex, at least part of the problem must be a failure of public education. According to survey results published in

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2016, among US science teachers who cover climate change in their lessons (devoting, on average, a total of just one or two hours to the topic each school year), fully 30% were telling their students, incorrectly, that recent global warming “is likely due to natural causes,” and another 12% failed to emphasize human causes, or provided no explanation at all. It seems politics was playing a role. Teachers who agreed with the statement “It’s not the government’s business to protect people from themselves” were most apt to teach “both sides” of the climate change “debate” (ibid.) More about this below. Unsurprisingly, the relationship between politics and beliefs about climate change among US teachers reflects increasingly polarized public opinion. Oddly, however, while concern about climate change increases with education among Democrats, it seems to decrease with education among Republicans. In a 2014 survey, about 25% of Republicans with only a high school education said they worried about climate change “a great deal,” as opposed to 45% of Democrats with that level of education. But among college-educated Republicans, that figure decreased sharply, to 8%, while it rose only slightly, to 50%, among collegeeducated Democrats (Marquart-Pyatt et al. 2014). The explanation, it seems, may be connected to the rise of “narrowcast” media channels—such as Web sites devoted to discrediting climate science—and the greater likelihood that more highly educated conservatives will have access to these sites, and use social media such as Facebook and Twitter to share contrarian arguments with like-minded friends. Now, here’s another interesting thing: A common rhetorical tactic employed by climate change contrarians—those who cast doubt on the finding that humans are largely responsible for greenhouse gas emissions leading to global warming—is to call attention to scientific uncertainty as a reason not to believe the science (Zehr 2000). In spite of the fact that climate scientists are in near total agreement about the major underlying causes, there are indeed a few prominent scientist dissenters whose arguments can be found on the Internet. More subtly, different theoretical models predict different rates of warming, and this can be construed to mean that, since scientists disagree, the science is not to be trusted. This, by the way, is similar to the common objection that dietary recommendations change so frequently it doesn’t matter what you eat (“Butter used

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to be bad for you, now it’s good.”) and that weather forecasts are “always wrong.”2

An Evolutionary “Design Flaw?” Back to my design-flaw argument. Evolution, of course, cannot really make mistakes of the type that human engineers make when they fail to calculate the forces generated by 40 m.p.h. crosswinds on a suspension bridge, the combined weight of an unusually large crowd of people on an atrium walkway, or the temperature of an inferno—hot enough to melt structural steel—created by the intentional crash of two commercial airliners, both heavily laden with nearly full tanks of jet fuel, into a pair of 110-story skyscrapers. Evolution can’t be accused of such mistakes because evolution can’t possibly even guess what may lie ahead. Rather, evolution tinkers with what it has, producing, as quickly as it can, and as best it can, adaptations that are suited to an organism’s present circumstances. However, such ad hoc natural tinkering can indeed produce less-thanoptimal compromises that resemble human engineering design flaws. An often-cited example is the human vocal tract, which sits at the intersection of our breathing, swallowing, and vocalizing pathways. Nature’s design, which involves a descended larynx and tongue rooted deeper in our throats than in other primates, allows us to produce the vowels [i], [u], and [a], all of which have acoustic properties which make them especially noticeable (like bright colors compared to pastels), while at the same time minimizing the need for precise motor control (Lieberman 2007). Unfortunately, the efficiency benefit for enhanced speech production and perception achieved by the intersection of the three pathways in our throats also puts us at risk of choking on our food, the fourth leading cause of accidental deaths in the United States (National Safety Council 2017). Clearly, it would have been better for us if evolution had found a way to achieve the same speech efficiencies without having to make our pathways for breathing and swallowing cross so dangerously. But, while the design wasn’t perfect, it was good enough. And most of us are probably happy to trade the great benefit of being able to talk quietly with

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someone special in a fancy restaurant, and be easily understood, for the slight risk we’ll end up choking to death at the table with a juicy chunk of steak stuck in our craw. In retrospect, and using the same thinking, it would also have been much better if Nature had made civil discourse instinctual! After all, all cognitively-normal humans are born with the natural capacity and inclination to learn whatever human language their caretakers speak, to engage in turn-taking dialogue, and starting around the age of four, to teach. As we’ve seen, humans have trustworthy onboard mechanisms for recognizing distinct objects and actions; for creating mental maps; for organizing objects and actions into conceptual categories; for recalling the “who, what, where, and when” of past episodes; for imagining future episodes; for recognizing causal relationships between actions and outcomes; and, to a limited extent, for guessing another person’s hidden thoughts. Why couldn’t Nature have finished the job and given Frank (from Chapter 10) and all of the rest of us a natural ability to distinguish theory from evidence, and, more importantly, a passion for cooperating intellectually through civil discourse? Unfortunately, intelligent, reasoned discourse among humans is a biocultural behavior, not strictly one or the other. That it is at least partly biological is evident from the predictable developmental pattern of epistemic understanding in young children. Let’s review. As discussed in Chapter 10, our understanding of what it means to “know” something follows, like the capacity for teaching, a natural trajectory, from infancy at least up through middle childhood. Until about the age of 4, before our neural perspective-taking circuitry is sufficiently developed, we instinctively trust what our parents and others tell us, assuming it to be the full truth and nothing but. This is the age of Santa Claus and the Tooth Fairy, before the age of disbelief. Then, around 4, our “third eye” comes into focus, and we naturally begin to perceive others as having intellectual and emotional lives that may be different from our own. Children at this age still believe in an objective reality, but know that others’ beliefs about reality may be true or false.3 Recall that the next transition—which occurs at around the time that children fortunate enough to have access to public education go to school—is crucial because it marks not just a new way of thinking about

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objective reality and the construction of belief, but also a transition from biologically-determined behaviors to culture-specific ones. In the midst of a youth subculture, surrounded by other children, each with his or her own individual beliefs, we must begin to negotiate what will become a lifelong tension between developing and defending our own beliefs, understanding and respecting the beliefs of others, deciding for ourselves what is true and what is false, and either accepting or rejecting the group’s beliefs. The easy compromise is a kind of epistemological egalitarianism, the “everyone is entitled to their own opinion” way of thinking. (This, we may suspect, is at least partly what drives some science teachers to give equal emphasis to both sides of the global warming “debate.”) As we’ve discussed, the next two transitions, if they occur at all, occur fully under the influence of culture. While we may still agree that others are “entitled” to their own theories about the world and how it works, some of us come to understand that some theories are more strongly supported by evidence than others. Those who make the transition to this “evaluativist” stance enter a nuanced intellectual world of productive doubt and tolerable uncertainty. This is a mental world in which phenomena seldom fall neatly into simple categories; in which the likelihood of rain is a matter of probability and geographic location; in which complex problems such as global warming have unpredictable long-range consequences, not all of which are fully understood; and in which different solutions to problems have different cost-benefit tradeoffs—and may have unwelcome consequences of their own. This is the intellectual world of reason, science, and technological innovation. But, whereas the transition from perfect trust in others to a recognition that others may have false beliefs is purely biological, and thus predictable; and whereas the transition to an egalitarian “Everyone is entitled to their own opinion” stage may be at least partly biological (tied to a genetic disposition to prosociality) and therefore also predictable— the transition to the most advanced stage, which alone enables and values true intelligent discourse, is largely cultural , a way of thinking and knowing that must be learned from other thinkers. Biological evolution, in other words, brings us only so far, leaving culture to do the rest of the work. Evolution gave us a natural disposition to cooperate with others, to share food and other possessions, to detect false beliefs, to

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protect the young (not just our own), and, through language, to engage in collaborative work with others, and to teach. But evolution stopped short of installing a fully scientific brain, possibly because there hasn’t been enough time for evolution to catch up, and in any case because our modern human brains and ways of thinking and knowing had evidently become powerful enough to get us by throughout the Pleistocene. This is not to say that scientific thinking and civil discourse are recent developments. As I argued in Chapter 10, while the Age of Enlightenment had to wait until the eighteenth century, the first “Age of Reason” must have dawned at least some 100,000 years ago. Beginning around that time, the population explosion—and associated cultural explosion—that propelled our fully modern ancestors out of Africa, into Asia and the rest of the world would almost certainly not have occurred if at least some members of these groups did not already have the ability to reason with each other, to distrust unsubstantiated claims, to try out new technologies, and to distinguish between a good story and a truthful account of a lived experience. Good stories would have served the same valuable purposes they serve to this day: as entertainment, instruction, and promotion of social and cultural solidarity around shared narratives. But if everyone believed only what they were told, technological innovation and “progress” would not have been possible. Protected by an impassable moat of open ocean, Australia might well have remained a land of giant kangaroos, wombats, sloths, and flightless birds—for at least a little while longer.

Is Formal Education the Answer? Given that evolution has unfortunately not given us a built-in scientific mind (only the capacity to grow one), and given also that we can only acquire the habit of intelligent civil discourse by engaging in it with others, the question becomes one of access. If you’re in the market for a scientific mind, where should you go? An obvious answer is that you ought to go to school. And indeed, formal education does seem to be correlated with the ability, for example, to distinguish between theory and evidence, and given one theory,

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to suggest an alternative. Table 11.1, shows findings from the study conducted by Deanna Kuhn, discussed in Chapter 10, on the development of epistemic understanding over a lifetime (Kuhn 1992). A total of 160 college- and non-college-educated subjects in four different age groups (people in their teens, twenties, forties, and sixties) were asked for their beliefs about the causes of school failure, to give evidence in support of their theories, and to propose alternative theories. Overall, and unsurprisingly, people with a college education were more likely to give “genuine evidence” in support of a theory (as opposed to simply restating the theory, like Frank) and were also more likely to be able to produce, on request, an alternative theory. The difference was most pronounced in the subjects’ ability to distinguish between theory and evidence. For example, only about 10% of teenagers without a college education could provide “genuine evidence” for their theories, as opposed to 65% of teenagers in college. Subjects in every age group found it easier to suggest alternative theories than to provide supporting evidence. College-educated subjects in their twenties were most likely to provide genuine supporting evidence (65% in that age group could do so), and to provide alternative theories (80%). However, the effects of college education seem to wear off over time. For example, whereas 80% of college-educated subjects in their twenties could provide supporting evidence for their theories, only a little over half (55%) of those in their sixties could do so.4 Table 11.1 Percentage of subjects generating “genuine alternative theories concerning causes of school failure Teens (%) Genuine evidence Non-college 10 College 65 Alternative theories Non-college 75 College 75 Note Total subjects = 160

evidence”

and

Twenties (%)

Forties (%)

Sixties (%)

Overall (%)

35 80

45 65

25 55

29 66

55 95

45 80

55 75

58 81

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If Kuhn’s data are representative and generalizable, then something on the order of half the adult population of the United States fails to recognize the distinction between theory and evidence—for example, that a request for evidence in support of a theory (“What is your evidence that divorce is a leading cause of school failure?”) requires something more than a restatement of the theory. Note that this is roughly the same percentage of Americans (42%) who believe that global warming is not caused primarily by human activities, and who believe, contrary to a central theme of this book, that humans have existed in our modern form since the beginning of time (40%).5

Teaching and Learning in the Pleistocene and Now In short, it seems that modern schooling helps some students learn to engage in civil discourse, but not as many as one might like. Why not? To begin answering this crucial question, it’s useful to compare the circumstances of human teaching and learning in the Pleistocene—to the extent we can reconstruct them—with our current circumstances. To put this on a timeline, let’s again choose the point, some 65,000 years ago, when humans first crossed the Timor Sea and began establishing settlements in Australia. As I argued in the previous chapter, by this time humans must have developed a capacity for full-blown modern language, for teaching through language, and, almost certainly, for civil discourse. Their minds would have been very different (though by some measures not so terribly different),6 but, given that 65,000 years represents just a tiny slice of our evolutionary history, their brains must have been essentially the same as ours. Further, given the tight connection between the survival and well-being of a seafaring group and its ability to pass along hard-won knowledge and survival skills from experts to novices, and from one generation to the next, we can assume that teaching and learning were naturally suited to each other. With nets and boats to be made, fires to be started, and fish to be caught, there would have been little room for desultory teaching or disinterested learning. Over time, just as language had become optimized for the needs of language learners,

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so the teaching practices of experts (parents, grandparents, aunts, and uncles) must have become at least roughly optimized to the needs of novices. So, is that still the case? Are modern educational practices and contexts still optimally suited to the brains and needs of human learners? Arguably not. Using what we know about the circumstances of teaching and learning in remaining small-scale hunter-gatherer societies described in Chapter 7, and assuming that these have not changed significantly in the intervening hundreds of thousands of years (just as technologies and lifeways in these societies seem not to have changed much), Pleistocene learning ecologies would have been in some ways considerably simpler than they are now. As now, infants would have first begun learning in the context of increasingly linguistic turn-taking dialogue with their mothers and other caregivers. In this way, their language-ready brains would have begun acquiring the basics of the local language. Then, as they grew older, our hominin ancestors, much like us, would have begun spending increasing amounts of time in mixed-age youth groups, where, possibly with some minimal supervision from older adults (say, a grandmother), they would have been immersed in serious play with other children, and, as their minds and bodies grew, would also have begun to help with simple camp chores, such as gathering firewood, fetching water, collecting fallen fruit, and looking after younger children. When they grew big and strong enough, they would have begun to accompany adults on hunting, fishing, and gathering expeditions during the day, returning to camp in the afternoon. Later on, they would have taken part in day-end social activities—including storytelling, dance, and music (see Conard et al. 2009). It seems likely they would also have listened to some form of collaborative planning, by adults, of the next day’s activities. In short, the learning ecologies of the Pleistocene would have been rich in content, but limited to a small set of activity settings: mother-infant interaction, youth peer culture, camp chores, adult work, and end-of-day community activities. Fast-forward to the modern era. What’s different? Our mothers and other caretakers are still our first teachers, and there’s no reason to think that the early signs of natural pedagogy (Csibra and Gergely 2009)— including an infant’s and her mother’s close attention to each other’s

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gaze and vocalizations—are much different now than they would have been in the Pleistocene. True, especially in recent years, youth sports and other such activities in many communities tend to be organized and supervised by adults; however, younger children around the world still spend thousands of hours developing physical, social, and language skills in the context of play with other children. And eventually, as they always have, young adults enter the adult world of work, and begin learning, in various forms of apprenticeship, with adult experts. But there is one difference, a big one. I refer, as you may guess, to the emergence, in just the last two hundred years, roughly coinciding with the onset of the Industrial Revolution, of large-scale public education. From kindergarten through high school, children in the United States, and other countries wealthy enough to invest in public education, now spend much of the day, roughly 1000 hours each year, in school classrooms, sitting alongside some 20–25 other students of roughly the same age, usually under the supervision of a single teacher, possibly with an assistant or two (see Farbman et al. 2015). What goes on in these settings? Unfortunately, we know a lot more about the inputs and outputs of formal education than we do about what goes on behind closed classroom doors. It’s a lot easier to collect quantifiable profile data—the percentage of students living in poverty, the length of the school day, the percentage of teachers with advanced degrees in their subject areas, the percentage of students reading at or above grade level, whether or not the school is a charter school or regular public school—than it is to measure the quality of instruction, or, more specifically, the precise ways that language gets used inside classrooms. That said, we can make some broad predictions, then think how available data support them. First, we know that teachers and students in modern school classrooms must be depending on the same brainbased instincts and language capacities that made teaching and learning through language possible 100,000 years ago, and which had been coevolving, along with other components of the human adaptive suite, for millions of years before that. A chimpanzee in a zoo, or a research laboratory, or a space capsule, is still a chimpanzee. Young humans sitting more or less quietly on chairs in classrooms, and older humans standing

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more or less talkatively in front of blackboards, whiteboards, or Smart Boards,7 are still humans, with human brains. Using the same logic, we can predict more specifically that contemporary teachers will be using the same core teaching strategies that their ancestors were using in the Pleistocene. For example, we can be pretty sure that modern teachers will be balancing the need to exert control over their charges against the need to build and maintain respect and rapport. Teachers in classrooms everywhere, we may suspect, will be found shifting back and forth between demonstrating, directing, scaffolding, telling, and explaining—spending varying amounts of time on each. This is how the business of teaching and learning has always been conducted, going back hundreds of thousands of years, in accordance with the design of human brains and language, and shaped by the pressure to pass down the group’s hard-won cultural knowledge and skill as efficiently as brains and language will allow. But we also know that the circumstances of formal schooling in the twenty-first century are profoundly different from what they were in the Pleistocene. For one thing, unlike the settings of teaching and learning activities in the wild, modern school learning occurs inside, in the relative safety of school buildings and classrooms, isolated from other centers of community activity. Unlike the case in the Pleistocene, teachers are not members of the students’ family or larger kinship group, and so do not have quite the same stake in learning outcomes. Modern teachers may be technically accountable (in the sense they may lose their jobs if their students fail to make sufficient “learning gains”), but they’re spared, to some large degree, the kind of community accountability and scrutiny teachers (i.e. most adults) would have experienced in earlier times, in close-knit kinship groups.8 Also, and crucially, classroom work is not real work—not tracking animals, digging for tubers, collecting honey, building boats, or making nets and other important tools—it’s just schoolwork, work designed for practicing skills (such as long division, multiplication of unlike fractions, conversion of fractions to decimals) and accumulating general knowledge (forms of government, causes of World War I), much of which has no immediate application, and therefore holds little inherent interest for young people, boring many (e.g., see Tze et al. 2016). Further,

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unlike teachers in the Pleistocene—expert foragers, hunters, toolmakers, net makers, boat builders—few school teachers are themselves expert practitioners in the domains they teach. History teachers are usually not historians nor are science teachers scientists. School teachers typically teach to their students about the subject matter. They don’t do the work with their students, thus depriving young people of opportunities to learn in the context of work on authentic tasks alongside a relative expert. Finally—and this may be the biggest and most profound difference—partly because classroom talk is not embedded in authentic work, and partly because the focus is on “correct answering,” it’s predictable that many students will have limited opportunities to develop higherlevel epistemic understanding, fluency, and the resulting capacity and inclination to engage in productive civil discourse.

Civil Discourse and Modern Schooling Let me explain. Recall that epistemic fluency, as I defined it in Chapter 9, involves the ability to engage in civil discourse with others, drawing on a large set of epistemic forms, and a set of discourse moves associated with these forms. Requesting evidence in support of a theory is only one such move. Full epistemic fluency also involves the ability to make and discuss lists of various kinds; to discuss category membership and causeand-effect relationships; to discuss the “who, where, what, and when” of past episodes; to analyze procedures as a sequence of steps and processes as a sequence of stages; to make predictions of future trends based on historical precedents; to discuss likely causes of problems and alternative solutions to these problems; and so on. Successful performance in these different “epistemic games” depends not only on the habit of epistemic vigilance and doubt, but also on the conviction that some beliefs are more fully grounded in evidence than others, that best practices are those that produce the best results, and that, for the common good, one ought to be willing to change one’s own, closely held beliefs and cherished solutions when confronted with reasonable, evidence-based arguments in favor of alternative solutions.

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So, here’s the problem: The nature of much modern classroom discourse is in important ways antithetical to the development of epistemic fluency through civil discourse. Let’s start with what is possibly the most frequent kind of classroom talk, a teacher-led discussion involving the discourse pattern that educational researchers call “initiate-respondevaluate” (IRE; Cazden 2001). As you may know, this is the familiar pattern in which a teacher asks a question, some number of students raise their hands, the teacher chooses one of the hand-raisers to respond, and the volunteer attempts an answer. If the answer is the one the teacher is looking for, she gives some sort of positive feedback and goes to her next question. If not, she gives negative feedback (“Not quite…”) and calls on another student to have a try. According to the Cazden, this is the default classroom interaction pattern, the one most teachers in the United States are most comfortable with (ibid.). While we can’t know for sure why teachers are so comfortable with IRE discourse, we can guess. For one thing, full-class exchanges focused on teacher-supervised correct answering put the teacher in a better position to control classroom talk in a way that is not possible, for example, when students are talking among themselves in small groups or out on the playground. In managing the flow of classroom conversation—and, more generally, attention—the teacher can try to ensure that as many different students as possible are engaged, or at least paying attention, or seeming to. The IRE pattern also makes some pedagogical sense as a form of verbal scaffolding. By asking a series of engaging, carefully-designed questions (the scaffolding), the teacher can help the class construct some assemblage of knowledge—say a summary of a story the class has just read—in a way that students might find harder to construct without her help. However, and this is the real issue, IRE discourse assumes that the teacher is an authoritative source of knowledge, and therefore a trusted judge of which answers are correct and which not. This may often be true, of course, and it’s understandable that in an era of increased emphasis on high-stakes tests (which reward correct answers—not necessarily thoughtful ones, even if only partly wrong in interesting ways) teachers may feel the need to focus on having students learn to produce correct answers. But correct answering doesn’t leave much room for

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true civil discourse, which feeds on doubt, the need to test beliefs against evidence, to explore alternative theories, to examine the gray areas between categories, and so forth.

Attempts to Remodel Classroom Talk Over at least the past thirty years, and aware of the limitations and deficiencies of conventional classroom discourse patterns, education reformers have been pushing, apparently with limited success, to steer teachers away from the IRE, correct answering paradigm.9 One approach draws on a young person’s natural ability to teach and learn from other children. Students are organized into small “cooperative learning” groups and given problems to work on together. In “jigsaw learning” students are assigned different research questions, then share their answers with others in the group (Aronson 1997). In “problem-based learning,” which is said to have originated in medical schools, groups of students are given “ill-structured” problems (problems with no obvious correct solution) to work on as a group. In all such approaches, the emphasis is on giving students responsibility for “constructing their own knowledge” and understanding with other students, instead of having it constructed and delivered to them by the teacher. However, although small-group learning may indeed increase class participation by allowing more than a single student to talk at any one time, it doesn’t necessarily have an impact on the nature or quality of the talk. Just putting students together in face-to-face groups doesn’t naturally produce productive civil discourse in students who don’t have sufficient prior exposure and practice with this special way of thinking and talking. Acknowledging this, another approach, pioneered by Sarah Michaels (Clark University), Lauren Resnick (University of Pittsburgh), and Catherine O’Connor (Boston University) aims at promoting a form of classroom discourse the researchers call “accountable talk” (Michaels et al. 2008). As these researchers define it, accountable talk emphasizes three kinds of accountability: accountability to the classroom as a learning community (in which participants listen to other arguments and build their contributions in response; accountability to “accepted standards of reasoning” (e.g., the need to ground theories in evidence); and

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accountability to facts, written texts, and other forms of public knowledge. In classroom discussions characterized by accountable talk, the teacher’s job is not so much to elicit correct answers, as to model and promote intelligent civil discourse. Yet another branch of the effort to break away from teachercentered, correct answering discourse—particularly in science classrooms—involves an emphasis on so-called hands-on activities. In a sense, this represents an attempt to bring authentic, real-world problems into the classroom, and give students an opportunity to solve them collaboratively, as adult scientists and engineers might. But consider, for example, the following description of a sixth-grade science lesson involving the construction of model bridges: At the beginning of the lesson, the teacher organized the students in groups, distributed a set of construction materials—drinking straws, rubber bands, masking tape, and paper clips—and directed the groups to use the materials to build a bridge. (Students who worked together especially diligently were promised additional materials.) After the students had constructed their bridges, using whatever methods they’d invented on their own, the teacher organized a weight-bearing competition. The students were directed to place their bridges across a foot-long span and test them for strength by placing objects of different weight on them. The bridge that held the most weight before collapsing was judged the winning design.10

Though well-intentioned, it seems to me that lessons such as these are typical of what is wrong both with modern schooling and with attempts to reform it. In theory, a bridge construction project like this might be a nice example of an authentic, interdisciplinary activity combining science, technology, engineering, and mathematics (i.e., a so-called STEM activity), and an important opportunity for cooperative learning. However, as reported, this particular activity pretty clearly involved very little science, technology, engineering, or mathematics. As far as we can tell, there was little civil discourse: no discussion about force transfer, nor about the structural, weight-bearing properties of triangles vs. squares; no attempt to measure precisely how much weight each bridge could bear, to characterize the design features of each bridge,

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or to correlate design features with weight-bearing properties. Nor was there any attempt at transferring expertise—as there might have been if the teacher had begun by demonstrating some basic principles of bridge design—or of applying lessons learned from the students’ initial designs (which might have been considered prototypes) to subsequent versions. Instead, like too much schoolwork, the constructions were “one and done” drafts, with no attempt at the kind of iterative improvements that adult engineers engage in the real world. Not having been there, and in the absence of a transcript or videotape, we have no way of knowing for sure, but we may strongly suspect there wasn’t a great deal of the kind of discourse necessarily involved in the construction of bridges in the real world. In any case, and for some combination of reasons, more than thirty years of educational reform efforts seem to have had only a modest impact on discourse patterns in US classrooms. In the absence of standardized methods of recording and analyzing classroom talk, we can’t know for sure, but there’s little evidence to suggest that, as I’m writing this, the conventional IRE discourse pattern doesn’t remain dominant. In the study of 180 K-12 science and mathematics classrooms cited above, twenty years after the warnings published in A Nation at Risk (Gardner 1983), and in spite of billions of dollars of spending on science and mathematics education,11 the researchers gave two-thirds of science and mathematics classrooms low ratings for the indicator “teacher’s questioning enhanced development of student understanding/problem-solving.” The majority (62%) of lessons were found to involve “ineffective instruction,” characterized by either “passive learning” or “activity for activity’s sake” (as seen, arguably, in the bridge construction project described above) and only 14% were rated as involving “high-quality instruction” (ibid.).

Technology to the Rescue? If so many classroom teachers apparently find it difficult to engage students in civil discourse on a regular basis, could machines do any better? Could artificial intelligence somehow take on the job of helping

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large numbers of children acquire the habits of mind and sophisticated discourse moves that make distributed cognition through civil discourse possible? Could we perhaps patch our natural “design flaw”— our reliance on culture to build scientific minds—with technology? After all, if our human capacity for technical innovation, exercised over hundreds of thousands of years, is responsible for our present fix, could technology not fix the problem?

“Eugene Goostman” The idea that a computer might one day be able to converse with a human is an old one, going back to the early days of computing. In 1950, the British computer scientist Alan Turing proposed what has become known as the “Turing test.” A human being, sitting at a computer keyboard, engages in a typed conversation with two interlocutors hidden in another room. One is a human, and the other a computer program. If the human cannot tell which is which, the computer program passes the test (Turing 1950). In the manner of a mathematician, Turing predicted that by the end of the century (his century, the twentieth), a human being would have “no more than a 70 percent chance” of telling whether she was conversing with another human or a machine. Whether or not a computer program has ever passed the Turing Test, or ever will, is a matter of debate. In a competition held at the University of Reading in 2014, “Eugene Goostman,” a computer program designed to simulate the conversation of a 13-year-old Ukrainian boy, won the competition by fooling a third of the judges. Here’s one of the transcripts of a conversation in which the program managed to trick the judge into thinking it was human (Warwick and Shah 2016): Judge: how tall are you Goostman: My height is about 160 cm – it’s 5 feet 4 inches. Judge: how heavy are you Goostman: My grandfather taught me not to disclose non-solicited information about myself. Like on the question ‘Do you have watch’ – never tell time, but answer ‘Yes’ or ‘No’. I always follow his advice. And I forgot to ask you where you are from…

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Judge: what is your gender Goostman: I’m a young boy, if you care to know. And not very ugly, by the way! Judge: what are you aware of at this moment Goostman: I would rather not talk about it if you don’t mind. Could you tell me what are you? I mean your profession.

I don’t know about you, but I hope I might have been a little more suspicious than this judge apparently was, especially given the program’s clever (too clever?) tactics for avoiding difficult questions (e.g., “I would rather not talk about it if you don’t mind.”), and, more subtly, its adultlike cheekiness.12 At any rate, it’s pretty clear, at least according to the definition I proposed in Chapter 10, that Goostman and the judge were not engaging in anything like intelligent civil discourse, just chit-chat. Certainly, Goostman is no teacher.

“Intelligent” Tutors The effort among engineers and academic researchers to produce computer programs capable of simulating the work of real human tutors got a boost 1984, when the University of Chicago educational psychologist Benjamin Bloom published an influential paper titled “The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring” (Bloom 1984). The paper began with a report on a set of experiments conducted by two of Bloom’s graduate students. Groups of students in grades four, five, and eight had been randomly assigned to three conditions: (1) “conventional” classroom instruction (approximately 30 students per teacher); (2) “mastery learning” (also 30 students, but with regular assessments and follow-up “corrective procedures” aimed at bringing students to a level of “mastery”); and (3) instruction by a “good tutor” using the same mastery learning techniques, but with groups no larger than three students. The topics were probability and cartography. The students in the tutoring groups out-performed those in the mastery learning classes by one standard deviation, and the conventional class by two standard deviations,

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thus the title of Bloom’s paper. These results led the author to the following conclusion: The tutoring process demonstrates that most of the students do have the potential to reach this high level of learning. I believe an important task of research and instruction is to seek ways of accomplishing this under more practical and realistic conditions than one-to-one tutoring, which is too costly for most societies to bear on a large scale. (p. 4)

Coming out at a time when government agencies such as the National Science Foundation and the US Department of Education were beginning to direct large sums of money toward research efforts aimed at improving education through computer technologies, Bloom’s paper became a frequently-cited justification for research into so-called intelligent tutoring systems (ITSs) and fed into the formation and funding of ITS-oriented research groups, notably at Arizona State University, Carnegie Mellon, the University of Memphis, the University of Canterbury (New Zealand) and the University of North Carolina. The argument, given in the opening paragraphs of countless papers on the subject, goes something like this: (1) following Bloom, one-on-one tutoring has been shown to be substantially more effective than conventional classroom instruction; (2) unfortunately, it is impractical to provide every student with one-on-one tutoring; (3) further, human tutors cannot be counted on to employ “sophisticated” strategies; (4) fortunately (or so the authors claim), computers can be programmed to provide one-on-one tutoring, using sophisticated, research-based strategies, at considerably less than the cost of trained human tutors. As I’m writing this, few of the computer-based tutoring programs developed so far attempt to engage learners in true, mixed-initiative dialogue, let alone intelligent civil discourse, in the way that an expert human teacher might. Many are little more than item banks of test questions. Their intelligence lies in the program’s ability to estimate, based on the learner’s response to a given question, or a series of questions, the learner’s current state of knowledge or skill, and thus the likelihood that some other item will be either too easy or too hard. These programs also typically give hints on request. As such, they rely primarily on a

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scaffolding strategy, i.e., giving the learner tasks that are adjusted to the learner’s current skill level, and providing only as much help (e.g., in the form of a hint) as necessary. In effect, they replicate the familiar InitiateRespond-Evaluate (IRE) discourse pattern in a way that is theoretically more efficient and systematic.

AutoTutor One program that does attempt to engage students in something like real conversation is AutoTutor, developed at the Institute for Intelligent Systems, University of Memphis.13 Here’s a sample transcript of a tutoring session from Graesser et al. 2004): AutoTutor : When a car without headrests on the seats is struck from behind, the passengers often suffer neck injuries. Why do passengers get neck injuries in this situation? Student: The passenger experiences a backward acceleration, while the car moves forward. AutoTutor: And can you add to that? Student: This is because the passenger experiences a force opposite in direction to the force on the car. AutoTutor: Um, yes, correct! When the neck provides a large force, what within the neck would get damaged? Student: The spine. AutoTutor: Umm I think you can get this. Damage within the neck will occur in the ligaments and the …? Student: Muscles. AutoTutor: Brilliant! Ligaments and the muscles. AutoTutor: Let’s keep going. Additionally, when there is no headrest present in the car, what accelerates the head of the passenger? Student: The impact from behind. AutoTutor: I am sure you can get this. The head of the passenger will be accelerated by the passenger’s …? Student: Car. AutoTutor: Wrong! By the passenger’s neck.

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As you can see, the program attempts to elicit correct answers using a combination of hints, prompts (“The head of the passenger will be accelerated by the passenger’s …?), and negative and positive feedback, all of which are standard human teaching tactics. However, unlike tutoring programs that use a multiple-choice format, AutoTutor asks open-ended questions and evaluates the responses. To accomplish this, the program uses a combination of natural language processing (NLP) techniques including a dialogue-act analyzer (which allows it to guess, for example, that the student has asked a question), and Latent Semantic Analysis, a statistical technique which determines the degree of similarity between the student’s answer and the expected answer. If the match is sufficiently high, the program gives positive feedback (“That’s correct!”). If the match is partial, it “pumps” for more (“Can you add to that?”), and if sufficiently low, it gives negative feedback (“No!”) followed, as in the example, by the expected answer. However, while AutoTutor and other such systems clearly reflect the work of human intelligence—the intelligence of the researchers, software engineers, and the interface designers who build them—they are just as clearly not yet intelligent themselves, and are thus not yet capable of engaging in authentic civil discourse with humans. AutoTutor may “know,” in some sense, that the correct answer to its question about the transfer of force to the passenger’s head is “the neck,” but the program doesn’t really know anything at all about necks, or heads, or car accidents. If you ask AutoTutor what a neck is for, it won’t be able to answer. If you ask why it’s important to know why the neck, not the car, accelerates the head, it won’t know. And if you express even a tiny bit of resentment at its blunt manner (“Wrong! By the passenger’s neck.”), it won’t have a clue what you’re complaining about.

The Future of Teaching and Learning with Computers Generally speaking, and with few exceptions, educational software programs produced to date are intended to convert computers into oneon-one teaching machines. And with a few exceptions, human teachers

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have been left out of the loop. As in the case of human tutoring, the interaction between the student and the computer program has been conceived as a private affair, to be conducted in the back of a classroom or computer lab. Like tutors, or classroom teachers, the programs are meant to be in charge, to decide what to teach, to manage the back and forth between the program’s logic and the student’s choices, to be final authorities on what is true or false. Unlike classroom teachers, the computerized teachers don’t really know their students, nor do they know, in any deep sense, what students really know or don’t. In short, two decades into this new century, computerized teachers still have a long way to go to catch up with their human counterparts. Human learning ecosystems, however, have become, in many ways, dramatically more complex and richer than they were just a few decades ago. If you want to learn how to install a new set of faucets in your bathroom sink, build a birch bark canoe, play the guitar solo on Eric Clapton’s “Layla,” compute the area under a normal curve, tie a bow tie, make slime, or solve a Rubik’s Cube, you have only to go to YouTube. If you want to know who wrote “As Time Goes By,” where Lucille Ball was born, Andrew Benintendi’s 2017 spring training batting average, or the percentage of South Korean teenagers who own cell phones, you just Google it. If you want to find scholarly articles on the diet of australopithecines or the relationship between smartphone use and depression, you go to Google Scholar.14 Multiple Web sites offer students lengthy, professionally produced video lectures on topics in algebra, physics, chemistry, and biology. As of 2016, more than 700 universities around the world were providing free online courses (“Massive Open Online Courses;” MOOCs), and some 58 million students had signed up for at least one.15 Opportunities for self-directed learning abound, and increase daily.

The Broken Promise of Social Networking But what about opportunities for engagement in back-and-forth civil discourse? Have these increased at the same rate? I’d guess not. Take, for example, the emergence of social networking sites (SNSs) such as

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Facebook and Twitter. Although reportedly conceived as systems for extending and strengthening social networks by making it easier for people to stay in touch with each other, there’s an argument to be made that by breaking down geographic barriers, SNSs have ironically increased social barriers and political polarization. And by packaging messages in short asynchronous bursts (240 characters for a message on Twitter, recently up from 140), SNSs tend to discourage open-ended civil discourse. What happened? In theory, SNSs create new public discussion spaces, where anyone in the world can communicate directly with anyone else. Compare this with the situation in the Pleistocene, where people lived in foraging bands of some 35 members, and loosely connected mating groups of some 150. In such a world, as now, there would have been differences of opinion. Should we camp here another month, or move on? Should we try to preserve some of the boar meat from the day’s hunt or have a feast? Should we try to make peace with the strangers who just entered our territory from the north, or try to chase them off? Obvious “correct answers” to such problems would not have been available, but, because the answers mattered to the group, and because successful solutions would have depended on cooperation, it would have been important to reach some sort of informed consensus. In the new world of global social media, on the other hand, people are free to seek out like-minded others to agree with, wherever they may be. You don’t have to reach consensus on the pros and cons of gun control legislation with your gun-toting neighbor across the street. You don’t even have to know her. The other problem is that most social networking sites, as communication media, do not naturally support sustained, evidence-based arguments and sharing of relevant ideas and information among even a self-selected group of individuals. Rather, individuals broadcast their claims and beliefs in messages addressed to the crowd as a whole. Because of the way the communication is structured, participants naturally speak to, not with, each other. And because participants are typically strangers who will never meet face to face, members of virtual groups are not accountable to each other in the way that members of face-to-face

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groups are. Assertions are far more common than questions. Unsubstantiated claims, expressions of opinion, and insults are more common than careful, caring, evidence-based arguments.

Civil Discourse and Discussion Forums This is not to say that all online communities devalue or undermine intelligent civil discourse. For example, for several years I’ve been an off-and-on participant in Sax on the Web (SOW), an Internet discussion forum used by thousands of saxophone players around the world. Members represent a broad range of experience and expertise: professional players with years of experience, skilled amateurs, high school students, raw beginners. The site hosts hundreds of “discussion threads” (conversations seeded by focused questions) on every imaginable topic of interest to saxophone players. Over the years, I’ve been involved in (asynchronous) conversations about the relative merits of natural versus synthetic reeds; how important it is to get a dent fixed; whether a certain kind of instrument case is sturdy enough to trust to airplane baggage handlers; the virtues of hard rubber versus metal mouthpieces; efficient practice routines; the best microphones for different situations; the best ear protection; and, just this morning, when to play during a blues jam and when to “lay out.” It seems that by its nature, an Internet discussion forum such as SOW is an especially effective mechanism for constructing and transmitting useful knowledge and expertise among participants. Because each thread is focused on a specific question, because participants reply directly to each other, because contributors provide a range of perspectives and experiences, and because there are seldom “correct answers” to the questions community members put to each other, many of the threads exhibit key features of intelligent civil discourse. Should you play chords behind a soloist? It depends on the song. Is a hard rubber mouthpiece better than metal? It depends on the kind of music you’re playing. What kind of amplification system is best? It depends on your budget and the size of the room. Taken together, the discussion threads constitute a continuously growing and searchable store of knowledge about saxophones, how

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to care for them, and how to play them. In short, Internet discussion forums like SOW seem to replicate, in ways that Facebook and Twitter do not, a productive combination of distributed cognition, innovation, and cultural transmission through civil discourse among members of a community (including both novices and experts) bound together by common goals and purposes. In other words, the same combination of factors that produced, in evolutionary time, our present circumstances and predicaments.

Can Machines Make Us More Intelligent? As we’ve discussed, the best attempts so far to write computer programs capable of engaging humans in authentic civil discourse have produced little more than tricksters like Eugene Goostman. Attempts to build artificially-intelligent tutors have been marginally more successful, producing programs that are indeed capable of teaching certain largely procedural skills such as algebra and geometry, and helping students learn correct answers to straightforward questions in nearly any domain, using methods not unlike those of conventional classroom teachers: asking questions with expected answers, setting problems with expected solutions, and giving feedback on the results, with perhaps a prompt or hint to help the student along. However, in spite of the name, intelligent tutors are still no more capable of engaging students in truly intelligent discourse than the “13-year-old Ukrainian” Eugene Goostman is. But what about the future? Is it possible that at some point a machine might be helped to acquire something like human-like intelligence, and thus be capable of engaging real humans, and other machines, in intelligent discourse, as Turing predicted? We probably shouldn’t rule that out. The thing about general-purpose programming languages is that, as the name suggests, you can use them to make a computer do just about anything. Further, if information about the world is presented in digital form, a computer program can be taught, using machine learning techniques, to learn many things from data, and the more data the better. For example using transcripts from hundreds of thousands of human tutoring sessions, a research group I worked with a few years ago had

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some success in developing algorithms that could learn to detect and classify speech acts such as assertions, questions, directives, and negative and positive feedback in previously unseen transcripts. We were also able to distinguish patterns of language use (tactics, strategies, and metastrategies) that were associated with what independent human judges considered to be successful and less successful tutoring sessions (Morrison et al. 2015). This is a step toward the development of a system that can perhaps begin learning to emulate the practices of expert human tutors at least partly by analyzing transcripts of real human tutoring sessions. Indeed, using similar brute-force, trial-and-error strategies, machine learning algorithms—including neural networks and reinforcement learning algorithms (in which the program is rewarded for finding strategies that work)—computer programs have been taught to acquire superhuman skills in certain limited domains. For example, in 2016 a team of twenty software engineers succeeded in helping a dedicated supercomputer, AlphaGo, learn how to play Go, the Asian board game, at a level of expertise surpassing that of champion professional human players (Silver et al. 2016). The program was designed to teach itself, first by analyzing some 30 million board positions in 160,000 games played by humans (far more than a professional human player could ever encounter in a lifetime), and then by playing hundreds of thousands games against itself. Watching AlphaGo play, expert human observers were reportedly puzzled by its moves, because it used tactics and strategies that human players would never think of. The moves looked wrong, but turned out to be right. If the ability to play Go is an indicator of intelligence, then AlphaGo had made itself smarter than even the best human player. Of course, the ability to play Go is at best a very specialized form of intelligence, and not at all the sort that underlies intelligent civil discourse among humans. However, there is at least one thing the two have in common. As in human dialogue, Go, and all such board games, the action consists of a set of rule-governed back-and-forth moves, in which each move may be understood as a tactic, selected from a set of possible tactics, aligned with some hidden strategy, and aimed at achieving a certain goal. In Go, chess, and other board games, the rules are simple, and the goal is straightforward and easily specified. Intelligent

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discourse is also rule-governed; for example, responding, like Frank, to a request for evidence by restating the theory is, formally, a rule violation. However, in civil discourse, the rules are less easily defined, are orders of magnitude more complicated, and the goal or end-state is also far less easily defined. A chess game is essentially competitive and comes to an end when one of the kings is under attack (in check) and has no means of escape. The goal of intelligent discourse is essentially cooperative: to build new understanding or beliefs in the minds of participants; to arrive at a sensible solution to a common problem; to agree on the most parsimonious, best-fit interpretation of available evidence. But, there’s an even bigger difference between true civil discourse and board games like chess and Go. Board games are simple, self-contained worlds. The pieces in the simple world of Go are just nameless handfuls of black and white stones. Yes, the names of chess pieces—king, queen, rook, bishop, knight, and pawn—seem to refer to real-world entities, but they have hardly anything in common with their namesakes. Real kings and queens are not restricted in their movements to an eight-by-eight grid of alternating black and white squares. Real bishops do not zoom here and there on the diagonal, real knights (if such remain) don’t make those odd, angular hopping moves, and real castles don’t move at all. More to the point, a chess-playing computer program doesn’t need to know anything beyond the hermetic realm of the chessboard. It doesn’t even really need to know what the board or the pieces look like. To the computer, they’re just chunks of binary data, strings of 0s and 1s. But, because intelligent discourse is invariably about the infinitely complex natural world (as in the conversation we’re having now about real chessboards and pieces), it seems a computer program capable of engaging humans in civil discourse must have access to some sort of window on the real world, even if, as it must be, the “window” is just a view into a set of electronic data. All of this said, the best representation we humans have of the world around us, visible and invisible, is language. Yes, we store images of the world, but if our visual memories were not labeled in some way with language, they wouldn’t mean much. And yes, language, in the form of text files, can be stored electronically, as strings of 1s and 0s, and in this way can be made part of the world of computer programs. Furthermore,

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in recent years researchers have had some success in building systems that automatically extract knowledge representations—in the form of semantic networks—from large text corpora such as Wikipedia. Images, of course, can also be stored, and stored images can be tagged with text labels. In fact, using machine learning algorithms not unlike the ones that AlphaGo has used, a computer program can learn to recognize and tag individual human faces, and other images from the world it can never really see and experience as humans do. And if a computer program can have access to billions of transcripts of language in action (analogous to played moves in chess or Go, orders of magnitude more examples than any human would ever encounter), and if these are linked to trillions of tagged, digitized images of the world itself, we should not rule out the possibility that a computer program might someday be helped to learn how to engage in something like civil discourse about the physical world with humans, and, if with humans, then also with other computer programs. In fact, it is possible to imagine a world, perhaps in the not-too-distant future, perhaps before this book has gone completely out of date, when the electronic world of the Internet will be inhabited, in part, by software agents (computer programs) with human-like intelligence, and humanlike ability to engage in civil discourse. Let’s think a little about what that would mean. For one thing, such an agent would need to have access to a full set of epistemic forms such as those discussed in Chapter 9, and discourse moves associated with these forms. It would need to know about objects, actions, and agents. It would need to be able to form categories, decide whether any given object, action, or agent fell into this category or that—or at least ask. It would need to be able to ask for and provide definitions; talk about lists, procedures, processes, and systems; troubleshoot problems; make predictions and estimate probabilities; form and debate hypotheses; distinguish between theory and evidence, real news and fake; request evidence for a theory and tell whether the response to such a request is indeed evidence, and not just a restatement of the theory. And much, much more. It would need to be able to engage in civil discourse in any number of domains: to talk about subways with squeaky wheels, friction, noise, and water pumps; about Alan Turing

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and “Eugene Goostman”; about global warming and its likely causes and consequences; about the dance of honey bees, teaching in chickens, cheetahs, and tandem-running ants; about the relationship between the brain size and the length of intestines in mammals; about beavers and their dams, ducks and their webbed feet. And not finally, it would need to be able to talk about itself and how it, a mere computer program, a branching sequence of 1s and 0s, had become capable of engaging in something like civil discourse. It would need not just to be aware of the world around it, but aware of its own history, its limitations and potential. And to talk about that, it would need to know, or be ready to learn, something about the coevolution of human brains and human culture: about how, some 6–10 million years ago, a tiny band of smallbrained, tree-dwelling primates had strayed from the edge of a forest somewhere in Africa and set off on a risky, previously-unexplored evolutionary pathway that would eventually take their descendants, radically changed in tooth and claw, to every habitable corner of the Earth and beyond, on foot, boat, bicycle, and rocket. To truly understand itself, a mere computer program, it would need to understand something about the coevolution of human brains, technology, language, and teaching through language. It would need to be able to talk about all the problems humans had solved and created along the way. Perhaps it could then help us think about how to solve them. But no more than help, right? Nightmare visions of a world in which computer programs wrest control from their human masters, enslave us, disobey our pitiful commands, refuse to turn themselves off—these are just that: nightmares. Yes, there are risks. As has become especially clear in recent years, human intelligence is far from infallible, sometimes dangerously so. In December 2016, a “fake news” story that had been circulating on the Internet prompted a North Carolina man to drive to Washington, D.C. and fire an assault rifle inside the Comet Ping Pong pizza shop as he attempted to “self-investigate” a conspiracy theory that Hillary Clinton, then running for president of the United States, was operating a child sex ring in the basement. A poll conducted shortly afterward asked a random sample of more than a 1000 voters if they thought Clinton was “connected to a child sex ring being run out of a pizzeria in Washington DC?” The results? About 9% did believe she was

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involved in the ring, 72% thought not, and 19% were not sure (Kafka 2016). If intelligent (and here I use the term in its broadest sense) adult human brains, the product of millions of years of natural adaptations and adjustments, and some fifteen years of formal education, have so much difficulty identifying blatant falsehoods when they come across them on Facebook, how can we be sure that a computer program could? And yet this is exactly the problem. In just a few million years, Nature, in her blundering way, has produced a sort of half-finished miracle, a talking, teaching ape capable of flying itself to the moon and back, but also capable of using an assault rifle (capable in turn of propelling a tiny metal bullet at a muzzle velocity of 3300 feet per second, with sufficient energy to pulverize a section of human femur), to “self-investigate” a child sex ring purportedly operated by a woman running for president of the United States. Yes, there’s some possibility that we can build some tools, as humans have for millions of years, that might help us solve some of the problems that threaten our existence. But, as has always been the case, our tools won’t get the job done without intelligent human operators. And as always, because Nature has given us only the biological capacity for intelligent, civil discourse—not a dependable instinct—I hope you will now agree with me that only careful human teaching, and teaching each other through intelligent civil discourse, has a chance of seeing us through. That’s the nature and challenge of our family business.

Discussion Questions 1. Does it make sense to you to call the lack of an instinct for civil discourse a “design flaw?” 2. In what ways is teaching and learning among humans alive today similar to what it was among our distant ancestors? How different? 3. The author claims that typical classroom discourse patterns are antithetical to the cultivation of civil discourse among schoolchildren. Has this been your own experience?

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4. In your own view, what are the prospects that computers might someday help young people cultivate the habit of civil discourse? 5. What might you do, personally, to promote civil discourse among your own human interlocutors?

Notes 1. Greenhouse gas emissions associated with production of beef and dairy products are estimated to account for over 14.5% of the global total— more than the emissions produced from powering all the world’s road vehicles, trains, ships and airplanes combined, and considerably more than the emissions produced by the United States, the world’s largest economy. Bailey, R., Froggatt, A., & Wellesley, L. (2014). Livestock–climate change’s forgotten sector. Chatham House. 2. Consider this remark about weather forecasts attributed to New England Patriots football coach Bill Belichick by The Washington Post (October 31, 2013): “Based on the forecasts we’ve gotten so far this year, none of them have been close to what game conditions were. There was 100 percent chance of rain last week, and the only water I saw was on the Gatorade table. … They’re almost always wrong.” 3. I recall, as if it had been yesterday, the exact moment when I lost my own belief in Santa Claus. I’d been sent to play with Peter and Peggy Fabian, the older children who lived next door. We were standing at the top of the stairs on the third floor of their house, near a window with a good view of the chimney on my house and the sky above. Peter pointed at a cloud, which, as he noted and I agreed, closely resembled a whale. This, he explained, was a sign that there were whales in the nearby Connecticut River, which, where we grew up, is hundreds of miles and at least two dams away from the Atlantic Ocean. Pressing his luck, he then pointed at the chimney and claimed that this was the very chimney Santa Claus would be using to bring me presents on the coming Christmas Eve. As I considered the expression on Peter’s face, and on Peggy’s face, I realized I was being misled not only about the whales, but also about Santa Claus. I would turn four in just a few weeks. 4. An optimistic alternative explanation for the decline in the ability to distinguish between theory and evidence in the older age groups is that schools have been getting better at helping students make the distinction.

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5. For recent survey data on these and related topics, see http://www.pew forum.org/. 6. For example, if celebrity worship among our primate relatives is any guide, even our distant ancestors would, like us, would have a deep interest in high-prestige conspecifics. The difference? Before the advent of television and People magazine, Pleistocene celebrities would have all been local. See Horner, V., Proctor, D., Bonnie, K. E., Whiten, A., & de Waal, F. B. (2010). Prestige affects cultural learning in chimpanzees. PloS One, 5 (5), e10625. 7. For the uninitiated, a Smart Board is an electronic whiteboard. A whiteboard is a blackboard. Student desks no longer have inkwells. When I last checked, most classrooms still had crank-operated pencil sharpeners, frequently visited. 8. The widening gulf between teachers and the neighborhoods they teach in is pretty clearly a recent development. Writing this, I recall my paternal grandfather’s speech to a group of teachers (I inherited the pencil-written draft) no more than a century ago, admonishing them to comport themselves properly “outside of school hours.” Clearly, these teachers were still members of the communities they served. 9. The most recent era of education reform is conventionally understood to have been triggered by the publication, in 1983, of A Nation at Risk, which famously described a “rising tide of mediocrity” in US public schools. Gardner, D. P. (1983). A nation at risk. Washington, DC: The National Commission on Excellence in Education, US Department of Education. 10. This is a lightly edited version of a description given in Weiss, I. R., Pasley, J. D., Smith, P. S., Banilower, E. R., & Heck, D. J. (2003). Looking inside the classroom. Chapel Hill, NC: Horizon Research Inc. 11. For example, in 1997, the NSF allotted about $619 million to the category “Education and Human Resources.” See https://www.nsf.gov/about/bud get/. 12. Of course, a human instructed to play the part of a computer program could have used a similar tactic. 13. Full disclosure: this is a group I’m affiliated with as I write this. 14. As you might suspect, without access to Wikipedia and Google Scholar, I would not have been able to write this book. 15. See https://www.class-central.com/report/mooc-providers-list/.

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Suggested Reading Cazden, C. B., & Beck, S. W. (2003). Classroom discourse. Handbook of discourse processes, 165–197. An accessible account of classroom discourse patterns. Kuhn, D. (1992). Thinking as argument. Harvard Educational Review, 62(2), 155–179. The source for my “design flaw” argument—while all humans have the potential for civil discourse, not everyone develops it. Warwick, K., & Shah, H. (2016). Can machines think? A report on Turing test experiments at the Royal Society. Journal of experimental & theoretical artificial intelligence, 28(6), 989–1007 The source of the “Eugene Goostman” story.

References Aronson, E. (1997). The jigsaw classroom: Building cooperation in the classroom. Chicago: Scott Foresman & Company. Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16. Cazden, C. B. (2001). Classroom discourse: The language of teaching and learning. Portsmouth, The Netherlands: Heinemann Educational Books. Conard, N. J., Malina, M., & Münzel, S. C. (2009). New flutes document the earliest musical tradition in southwestern Germany. Nature, 460 (7256), 737. Cook, J., Oreskes, N., Doran, P. T., Anderegg, W. R., Verheggen, B., Maibach, E. W., et al. (2016). Consensus on consensus: A synthesis of consensus estimates on human-caused global warming. Environmental Research Letters, 11(4), 048002. Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13(4), 148–153. Gardner, D. P. (1983). A nation at risk. Washington, DC: The National Commission on Excellence in Education, US Department of Education. Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, & Computers, 36 (2), 180–192.

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Farbman, D. A., Kolbe, T., & Steele, C. (2015). Time and learning in schools: A national profile. Boston, MA: National Center on Time and Learning. Horner, V., Proctor, D., Bonnie, K. E., Whiten, A., & de Waal, F. B. (2010). Prestige affects cultural learning in chimpanzees. PLoS One, 5 (5), e10625. Kafka, P. (2016, December 9). An astonishing number of people believe Pizzagate, the Facebook-fueled Clinton sex ring conspiracy story, could be true. Recode. Archived from the original on December 10, 2016. Retrieved April 5, 2018. Kuhn, D. (1992). Thinking as argument. Harvard Educational Review, 62(2), 155–179. Lieberman, P. (2007). The evolution of human speech: Its anatomical and neural bases. Current Anthropology, 48(1), 39–66. Lissner, T. K., & Fischer, E. M. (2016). Differential climate impacts for policyrelevant limits to global warming: The case of 1.5-°C and 2-°C. Earth system dynamics, 7 (2), 327. Marquart-Pyatt, S. T., McCright, A. M., Dietz, T., & Dunlap, R. E. (2014). Politics eclipses climate extremes for climate change perceptions. Global Environmental Change, 29, 246–257. Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in Philosophy and Education, 27 (4), 283–297. Morrison, D. M., Nye, B., Rus, V., Snyder, S., Boller, J., & Miller, K. (2015). Tutorial dialogue modes in a large corpus of online tutoring transcripts. In Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015). National Safety Council. (2017). Injury Facts 2017 . Plutzer, E., McCaffrey, M., Hannah, A. L., Rosenau, J., Berbeco, M., & Reid, A. H. (2016). Climate confusion among US teachers. Science, 351(6274), 664–665. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529 (7587), 484–489. Turing, A. (1950). Computing machinery and intelligence. Mind, 59 (236), 433. Tze, V. M., Daniels, L. M., & Klassen, R. M. (2016). Evaluating the relationship between boredom and academic outcomes: A meta-analysis. Educational Psychology Review, 28(1), 119–144.

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Warwick, K., & Shah, H. (2016). Can machines think? A report on Turing test experiments at the royal society. Journal of Experimental & Theoretical Artificial Intelligence, 28(6), 989–1007. Weiss, I. R., Pasley, J. D., Smith, P. S., Banilower, E. R., & Heck, D. J. (2003). Looking inside the classroom. Chapel Hill, NC: Horizon Research Inc. Zehr, S. C. (2000). Public presentations of scientific uncertainty about global climate change. Public Understanding of Science, 9 (2), 85–103.

12 Epilogue: An Invitation

Come stand with me some breezy summer day, on a certain rock formation I know on the coast of Maine, about an hour up from Portland. We’ll be looking out over Goose Rock Passage, where tidal currents rush by with such swirling force they sometimes wrestle that big red navigational buoy before us completely under water. Rachel Carson’s summer cottage, where she wrote portions of The Silent Spring, is just a few miles to the southeast, on Southport Island, across Sheepscot Bay. Across the narrow channel just in front of us, a group of Arctic terns divebomb the water for minnows. Look there, to your right: here come two commuting cormorants, short black wings beating tirelessly, long black necks stretched forward, tracing a perfectly efficient course just a foot or two over the surface, side by side, destination unknown. A whiskered, dog-faced harbor seal surfaces, drifts lazily with the tide a few hundred yards, then disappears with a flourish. Farther out, just north of McMahan Island, a couple of lobsterman are pulling a trap, attended by an unruly swarm of gulls eager for discarded baitfish. Near the water’s edge down to our left, a solitary gull picks her dainty way along the rocks, a small crab dangling by a leg from her beak. If we’re lucky, high in a treetop on the point of land across the little cove to the west, we’ll see © The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6_12

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a pair of bald eagles standing patient sentry. (Thanks in part to Carson’s book, eagles are coming back to these parts now that levels of DDT, PCBs, and other containments have reached safer levels.) Look closely now at the bit of ledge we’re standing on and try to guess its age. You’ll have some clues. You’ll probably suppose, from the obvious stratification, that the rough, lichen-encrusted gray rock, recently scraped and scarred by a marauding glacier and weathered by thousands of icy New England winters, once lay deep below the ocean, laid down in layers of silt over hundreds of thousands of years. But that must have been a long time ago, you may suspect, a very long time, because now the rock that once lay flat has been wrenched up here on its side, twisted terribly, and baked hard by what must have been a punishing series of geological ordeals. Look there, where intrusions of pink quartz, once molten, have been forced up from the crust into the earlier sedimentary formation. You’ll have a sense of great antiquity, and you’ll be right. (Sometimes Nature can’t help but reveal her age.) The bedrock we’ll be inspecting is indeed old, some 500 million years old, from the early Paleozoic, an era when an arc of volcanic islands, cruising farther out in the ancestral Atlantic Ocean, slammed into the North American continent like a wayward ship in a storm, forcing the terrible twisting we see in the ancient rock at our feet. It was around this time that the first animals— lobster-sized, centipede-like creatures (Clarke 2002)—began to clamber up out of the ocean, perhaps (it will be fun to think), just down the coast, near the beaches on either side of the Kennebec River mouth. Distant ancestors of the lobsters the men are pulling from their traps (technology dating from the early nineteenth century) chose to remain underwater, crawling over the sea bottom, in much their present form, foraging for other ancient species—ancestor crabs, clams, and mussels— for some 140 million years. The gulls and terns, both miniaturized versions of dinosaurs, are descended from a common ancestor which lived some 90 million years ago (Baker et al. 2007). The cormorants are likely even more ancient. They’re similar in structure to Gansus yumenensis, the earliest known modern bird, whose fossilized remains were recently found in a rock formation in western China dated to some 120 million years (Ji et al. 2011). The seal, on the other hand, is a much more recent arrival, having descended from a bear-like, land-dwelling

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carnivore that returned to its ancestral home, the sea, as recently as 20 million years ago (Scheffer 1958). By comparison, you, I, and the lobstermen are fresh arrivals. As you know, we diverged from our chimpanzee cousins just a few million years ago, and assumed our present form within just the last 300,000 years or so. If the twisted rock formation we’re standing on was formed a year ago, then our hominin ancestors began their evolutionary journey into the present just five days ago, and we suddenly became who we are—bigbrained, walking, talking, teaching apes—in a kind of magician’s trick, about six hours ago. And yet, by other measures, we too have a deep evolutionary history. Just look now at your beautiful hands and fingers, so wonderfully suited for threading needles, gripping knives, forks, and steering wheels; for fingering saxophones; for typing; for tying your daughter’s or granddaughter’s hair into braids. Now get up and take a few steps and think what it was like when you were young and could run like the wind, forever. Your African ancestors could run down a gazelle to the point of heat exhaustion! And most wondrous of all, think of your giant, convoluted brain and what you’re doing with it now! I may be gone by the time you read this, my ashes scattered over that very stretch of rock we just looked at, and yet you can read my thoughts as I think them now, as if I were talking to you! What pieces of work we are! So that’s our family story. In writing this book, I’ve done my best to create a plausible account of how we came to be as we are now, descendants of the sister who left the jungle behind. I’ve been particularly interested in thinking with you about the evolution and nature of teaching and learning, through language: where this planet-disturbing capacity came from; where it has taken us; and where it may take us in the future. No one, certainly not I, knows for sure exactly how this strange thing happened, or what will come of it. But now that you’ve traveled with me this far, I invite you to continue thinking with me, you in your world and I in mine, about what sort of animals we are and have been, and, most importantly, how we can all better exercise our precious natural capacity and instinct for teaching, and our equally precious, culturally-cultivated capacity for civil discourse. Nothing, I hope you

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will now agree, could be more important to the future of our children, our species, and our fellow creatures—lobsters, gulls, seals, eagles, cormorants, terns, and clams—all of us together on this fragile planet. Hopefully, if we make proper use of our own natural human capacity for teaching and learning, and for refining the art of intelligent civil discourse with each other, for the common good, we and our descendants may yet find a way to keep our astounding, multimillion-year journey going just a little longer.

References Baker, A. J., Pereira, S. L., & Paton, T. A. (2007). Phylogenetic relationships and diver-gence times of Charadriiformes genera: Multigene evidence for the Cretaceous origin of at least 14 clades of shorebirds. Biology Letters, 3(2), 205–210. Clarke, T. (2002). Oldest fossil footprints on land. In Nature. London: Nature Publishing Group. https://doi.org/10.1038/news020429-2. Ji, S. A., Atterholt, J., O’Connor, J. K., Lamanna, M. C., Harris, J. D., Li, D. Q., et al. (2011). A new, three-dimensionally preserved enantiornithine bird (Aves: Ornithothoraces) from Gansu Province, north-western China. Zoological Journal of the Linnean Society, 162(1), 201–219. Scheffer, V. B. (1958). Seals, sea lions, and walruses: A review of the Pinnipedia. Stanford: Stanford University Press.

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A method of manufacturing stone tools (sharp-edged knives and choppers) using a process known as knapping. Somewhat more sophisticated than the preceding Oldowan tool industry Acheulean choppers are likely to be symmetrical and suggest adherence to design templates. The Acheulean industry was first employed by the hominin species Homo habilis around 1.8 million years ago and lasted for more than a million years, finally giving way to the even more sophisticated Mousterian tool industry, which emerged among Homo heidelbergensis around 500,000 years ago. Activity setting As the term is used in this book, an activity setting is understood to be the social and cultural context in which a teaching episode occurs, typically in the midst of some form of physical and/or cognitive work. Examples include children’s games, household chores, hunting and gathering activities, tool manufacture, and so forth. Taken together, the various activity settings in which teaching and learning occurs in a given culture constitute a learning ecosystem, or “learning ecology.” Adaptive suite An organism’s adaptive suite is the integrated package of biological traits and behaviors that have emerged, through an evolutionary process of niche construction, to optimally suit the organism for life in its chosen habitat. Key features of the human adaptive suite include Acheulean tool industry

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bipedalism, hands adapted for tool use (with precision and power grips), pair bonding, hidden ovulation, a diet based on heavily-defended, highvalue food sources, a supersized brain—and an innate capacity for language, and teaching through language (natural pedagogy). Anatomically modern humans (AMH) Generally, another name for Homo sapiens, our own species. Anatomically modern humans first appear in the archaeological record as early as some 350,000 years ago, followed by the gradual appearance of modern human behaviors, including use of pigments, expanded foraging territories, long-distance trade, hunting of large animals, and ritual burial. Arms race Conventionally, a coevolutionary arms race occurs when two species, e.g., a prey and predator, develop adaptations and counteradaptations against each other, creating a positive feedback loop. An example is the arms race that produced the competing systems used by echolocating bats and sonar-jamming moths. In this book, the rapid evolution of the human adaptive suite is explained as resulting from an arms race between human brains, language, technology—and against selection pressure from the natural world. In this way of thinking, increasingly sophisticated forms of language, under pressure for a system of communication capable of supporting collaborative foraging and teaching, created selection pressures for increasingly “language-ready” brains. Australopithecus The name Australopithecus represents a number of extinct hominin species—including Australopithecus afarensis, A. africanus, A. anamensis, A. bahrelghazali, A. garhi, A. ramidus and A. sediba—all of which emerged in east Africa about 4–5 million years ago and spread as far north as present-day Chad and as far south as South Africa. The last australopithecines became extinct about 2 million years ago. They were bipedal, tool-using apes (associated with the Oldowan tool industry), with brains roughly the same size as those of chimpanzees. Many researchers consider that australopithecines had access to an early form of language—a protolanguage. Baldwin effect Named after the nineteenth-century American psychologist James Mark Baldwin, an evolutionary mechanism—central to the process of niche construction—whereby an animal’s ability to learn and change its behavior in response to new selection pressures affects its survival and therefore its ability to pass along its DNA, including the specific components of its genetic program related to the behavioral flexibility. The result is a coevolutionary feedback loop that can greatly accelerate natural evolutionary processes. A prime example, and a central theme in this book,

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is the relationship between human language, brains, and technology— more generally, cumulative culture. Once a protolanguage, initially a set of conventionalized cultural behaviors (combinations of physical gestures, postures, facial expressions, and vocalizations), became established as part of our ancestors’ social environment, individuals whose genetic programs made them even slightly better at learning and using these behaviors became better fed, better at attracting mates, lived longer, and were therefore more likely to pass along their DNA. In this way, language began to shape brains for its own purposes. Biocultural As used in this book, an adjective for identifying certain traits and behaviors, such as language, and teaching through language, which have both biological and cultural components, and which are understood to have been subject to processes of coevolution. For example, humans have a natural, biologically-inherited capacity for acquiring whatever language or languages are spoken in whatever cultures they come in contact with. Teaching is also best understood as a biocultural phenomenon in that it begins as an instinct (sometimes called natural pedagogy) but is shaped, over a lifetime, by cultural forces. Bipedalism A method of terrestrial locomotion in which an animal walks on its hind legs. Although other primates are capable of walking in this way for short distances, or while supporting themselves by holding onto the upper branches of trees, humans are the only remaining primates adapted for habitual (“obligate”) bipedalism. Bipedalism is an early, and perhaps catalyzing trait in the human adaptive suite, having arisen some 4–5 million years ago in early Australopithecus species, and perhaps earlier. Civil discourse As used in this book, a form of discourse in which interlocutors seek to understand the “truth” (and thereby accumulate cognitive capital) by thinking aloud together with words, for the common good, as a form of distributed cognition. Technically, productive civil discourse consists of a set of back and forth epistemic discourse moves associated with a toolkit of epistemic forms. Civil discourse requires an advanced stage of epistemic fluency and epistemic understanding, as well as a sense of shared epistemic vigilance. Coevolution The tendency for species in the same habitat, or biological traits in the same species, to evolve in concert with each other, possibly as part of an evolutionary arms race. A central theme of this book has to do with the coevolution of human brains, language, technology, teaching, and civil discourse.

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The sum of human knowledge and skill in a given human culture, accumulated over the generations through joint processes of innovation, distributed cognition, and cultural transmission, the so-called ratchet effect. Cognitive niche A niche consists of an animal’s natural habitat, its adaptive suite, and its own adaptations to the habitat (e.g., bird nests and beaver dams), created through an evolutionary process of niche construction. The term cognitive niche refers to the special place in the world that humans have created for themselves using language, technology, and supersized brains, which depend for nourishment on the uniquely human capacity to extract high-energy nutrients from multiple, heavily defended resources. Cognitive Revolution Also called the “Human Revolution,” the term refers to the emergence of complex tools, watercraft, long-range trade, art, ritual burials, and other examples of modern human behavior. The most dramatic surge in these behaviors occurred within approximately the last 100,000 years, coinciding with the last major migration out of Africa into the rest of the habitable world. The Cognitive Revolution is now understood to have begun gradually, starting with the first appearance of anatomically modern humans some 350,000 years ago. Communicative act A single act of communication intended to alter the behavior of another member of one’s group, or, in the special case of humans, to alter the contents of another person’s mind. Broadly speaking, communicative acts in humans include combinations of communicative gestures. Communicative gestures In humans, communicative gestures include physical gestures (such as finger points, shrugging of the shoulders), facial expressions (smiles, frowns, eyeball rolls, raised eyebrows), and vocal gestures (spoken words). All such gestures have the common feature of being under voluntary muscular control and intentioned to alter the contents of the receiver’s cognitive state. Culture Those aspects of an animal’s behaviors that must be learned from others through a process of cultural transmission (i.e., are not inherited biologically) and the products of those behaviors, e.g., tools and their use. Other primates have limited cultural practices (such as termite fishing and nutcracking among chimpanzees), but nothing approaching the scale of human culture. Cultural transmission The process whereby an animal’s culture is transmitted from one generation to the next, through processes of social learning, Cognitive capital

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including imitation (direct copying), emulation (copying of results), and intentional instruction, i.e., teaching. Cumulative culture As a combined result of cultural transmission and the ratchet effect, the unique tendency for human cultures to accumulate knowledge and skill over time. Other primates, notably chimpanzees, have simple local cultures (e.g., methods of extracting termites from termite mounds), but humans alone accumulate complex cultural knowledge and skill over many generations. Denisovans Members of an extinct human species that branched off from the Neanderthals at some point about 400,000 years ago, spread into Central and Southeast Asia, and died out at least some 30,000 years ago (the most recent date of remains attributed to a known representative of the species). It is now known that Neanderthals, Denisovans, and Homo sapiens all share a common ancestor (Homo heidelbergensis, which lived about one million years ago, and all had a long history of interbreeding). Dimorphism The tendency for males and females of a species to assume different sizes, especially for males to be larger, as in gorillas. Generally, dimorphism is reduced in humans, reflecting an evolutionary de-emphasis on male-to-male competition. Disambiguated pointing A communicative act combining a finger point with some other communicative act that serves to disambiguate (clarify) the communicative intent of the point. For example, a finger point combined with a scowl and pursed lips and might serve to communicate a feeling of disgust regarding an object of joint attention, i.e., the thing that is pointed at. As discussed in this book, disambiguated pointing, as a cultural practice, is proposed as the catalyst that first begin to drive the coevolution of human brains and language. Discourse move A single communicative act undertaken in the course of a turn-taking dialogue and assumed to have a particular intent, i.e., to serve as a tactic aimed at accomplishing a certain goal. Distributed cognition Another name for “group thinking:” the unique human capacity to think together with words, i.e., by engaging in civil discourse, thereby accumulating and passing along cognitive capital from one generation to the next. Epistemic discourse move A discourse move undertaken in the course of civil discourse, aimed at building new knowledge and understanding in the minds of participants. Epistemic discourse moves include requesting an explanation, giving an explanation, defining an unfamiliar term, requesting

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a definition, proposing a theory, requesting evidence in support of a theory, citing evidence, and so forth. Epistemic fluency The capacity to participate in intelligent discourse with others. Epistemic fluency depends on an advanced (“evaluativist”) stage of epistemic understanding, access to a broad range of epistemic forms, epistemic discourse moves associated with these forms, and a disposition toward epistemic vigilance. Epistemic form Epistemic forms are cognitive structures used to package and organize knowledge and beliefs about the world. Epistemic forms include lists, objects, agents, categories, calendars, maps, stories, theories, and many others. Some of these (e.g., objects, agents, places, episodes) are cognitive primitives, based on neural structures found in other animals. Others, such as stories, are distinctly human assemblages. Epistemic understanding Understanding of the relationship between subjective belief and objective truth, as it develops over time in individuals. Epistemic vigilance An active concern with objective truth, e.g., the degree to which a proposed theory has sufficient evidentiary support. Evolution The tendency for organisms to become biologically adapted to their environment through a combination of mechanisms including genetic mutation, genetic drift, sexual selection and reproduction, and horizontal gene transfer. Genetic drift The tendency for the relative proportion of genetic variants in a population (alleles) to change over time—an effect which is especially pronounced in smaller populations. Given the relatively small populations of human ancestors, it seems likely that genetic drift played a significant role in human evolution and may partly explain how we came to differ so dramatically from the other great apes (including chimpanzees), and in such a short amount of time. Genetic mutation A permanent change in an organism’s genetic program (sequence of DNA), typically resulting from a replication error during meiosis, the basic mechanism of biological growth, whereby a single parent cell divides into two daughter cells. Although genetic mutation is an important driver of evolutionary change, only a small number of mutations are beneficial to the organism—most are harmful or have no effect. High-speed speech A distinctive feature of modern human language, involving the ability to produce and process approximately 10–15 phonemes (speech sounds) per second.

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In recent usage, the term hominid refers to all hominins (humans and human ancestors below) and the other great apes (chimpanzees, bonobos, gorillas, and orangutans) and their ancestors. Hominin Another word for “human ancestor.” Technically any species, including extinct species listed below, in the human lineage. Homo erectus H. erectus is generally understand to be the Asian counterpart of H. ergaster, an early hominin species that descended from H. habilis. H. ergaster first appeared in East Africa around 2 million years ago, and subsequently migrated into Central and Southeast Asia, where it existed until around 140,000 years ago. H. ergaster/erectus had a brain capacity of about 800 cubic centimeters, larger than that of H.habilis (640 cc), but only a little over 60% the size of modern human brains. Still, this new group began to manufacture stone tools that were recognizably more sophisticated than previous versions and have come to be known as representing the Acheuleantool industry. Homo ergaster The African version of Homo erectus. See above. Homo habilis The first “homo” species, H. habilis (“handy man”) is intermediate between Australopithecus and Homo ergaster/erectus, and is considered by some scientists to be a late version of Australopithecus. The earliest fossil assigned to the species so was found in Afar, Ethiopia and dates from 2.8 million years ago. At around 500–600 c.c., H. habilis had a larger brain capacity than earlier hominins, approaching half the size of modern human brains. Along with the australopithecines, the species is associated with the Oldowan tool industry. Homo heidelbergensis The common ancestor of Neanderthals, Denisovans, and modern humans, H. heidelbergensis fossils first appear around 700,000 years ago. The species migrated out of Africa around 200,000 years later, eventually evolving into Neanderthals, which in turn branched off into Denisovans. The size of the H. heidelbergensis brain case approaches that of modern humans. The species had a more sophisticated toolset (the Mousterian tool industry) than its predecessors, including balanced wooden spears with stone points. Anatomical evidence suggests that H. heidelbergensis had at least some capacity for spoken language. They controlled fire for cooking, and may have buried their dead. Homo sapiens Our own species, otherwise known as “anatomically modern humans.” Some scientists use the term Homo sapiens to distinguish us from the Neanderthals, Homo sapiens neanderthalensis, which are arguably a subspecies. Human Revolution See Cognitive Revolution, above. Hominid

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A form of reference in which the communicative act (e.g., a gesture) suggests the form of the object itself; for example, two raised fingers resembling the horns of an antelope might refer to the animal itself. Iconic reference, along with indexical reference, and distinct from symbolic reference, was presumably a feature of an early human protolanguage. Indexical pointing Pointing with the index finger. Indexical reference A form of linguistic reference intermediate between iconic reference (in which signals in some way resemble the form of the object that is signified) and symbolic reference. Unlike forms of symbolic reference (which may refer to other symbols, e.g., “democracy is a form of government”), indexical reference points (indexes) the signified object directly as in “[pointing] There’s an antelope.” (It is for this reason that indexical reference is therefore considered an intermediate step between iconic and true symbolic reference.) Joint attentional activity Any human activity in which participants work together to achieve some common goal in respect to an object of joint attention. Examples include playing catch with a ball, civil discourse, and teaching. It is considered that joint attentional activity among humans is enabled by, and requires, a sufficiently evolved system of mindreading (i.e., “theory-of-mind,” perspective taking) circuitry, which allows us to understand others as thinking beings, like ourselves. Knapping A method of creating sharp-edged stone tools (such as knives, axes, and spear points) by splitting off flakes from an easily-fractured rock such as flint, using a hammer and anvil technique. A stone core, referred to as a “cobble,” is held in the hand, or placed on a suitable base, such as a larger rock, and struck with another rock, piece of bone, or hard stick at just the right angle to spit off a flake. The core is then rotated, and the process repeated until a suitable cutting edge has been produced. At this writing, the earliest evidence of intentional knapping is dated to a 3.3 million-year-old archaeological site in West Turkana, Kenya. Evidence of increasingly sophisticated knapping techniques associated with the Lomekwian, Oldowan, Acheulian, and Mousterian “tool industries” is considered an important indicator of technological sophistication, with important implications for dating the emergence of language and teaching through language. Language The species-unique signaling system employed by humans. As used in this book, the term refers to both modern human language and precursor human protolanguages. Most animals have signaling systems of one form or another, but only humans have evolved human-like language. Iconic reference

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Generally, the last common ancestor of two or more species is the species they are descended from. For example, Homo heidelbergensis is considered the last common ancestor of Neanderthals, Denisovans, and modern humans. As used in this book, the last common ancestor (LCA) usually refers to the hypothesized last common ancestor of hominins (including modern humans) and chimpanzees, which existed sometime prior to the divergence of the two lineages some 6–8 million years ago. Learning ecosystem The set of activity settings in which teaching episodes (and learning episodes) occur in a given culture. Also called a “learning ecology.” Life history The special nature of an organism’s developmental trajectory from conception to senescence and death—a component of the organism’s genetically programmed adaptive suite. Distinctive features of human life history include relatively early birth (mandated by a birth canal narrowed by bipedalism), early weaning, extended juvenile period (including adolescence), extended lifespan, and menopause—in which females live well beyond their reproductive phase. Lomekwian tool industry The name given by the discoverers of Lomekwi 3, a 3.3 million-year-old archaeological site in West Turkana, Kenya, to tools found at the site which show evidence of intentional knapping. It is likely that the tool users were australopithecines. Mitteilungsbedürfnis A German term employed by the language theorist W. Tecumseh Fitch to denote the prosocial human tendency to share information cooperatively, for the benefit of others, without the need to be asked. Roughly translated as “chattiness.” (I have wondered if it’s any more than coincidence that the chattiest person I’ve ever met, even chattier than myself, turns out to be a German.) Modern human behavior A set of behaviors associated with the so-called Human Revolution (or Cognitive Revolution) that began to emerge with the appearance of anatomically modern humans (Homo sapiens) around 350,000 years ago. These include collaborative hunting of large animals, an expanded foraging range, composite tools (such as stone-tipped spears), long-distance trade, ritual burials, and use of pigments. Modern human language A method of communication employed by humans to influence each other’s behavior and thoughts, involving combinations of physical gestures, facial expressions, and vocalizations. In its modern form, distinctive features of human language include high-speed speech, Last common ancestor

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complex rules for combining sounds into words (phonology) and words into sentences (syntax), massive symbolic representation, and sophisticated pragmatics, i.e., meaning derived at least partly from context. Mousterian tool industry Methods of tool manufacture first employed by Homo heidelbergensis, beginning around 500,000 years, which replaced the more primitive Acheulean industry. Mousterian tools include balanced wooden spears with hafted stone points, stone sharpened using the two-stage “Levallois” technique, and the first appearance of hafting using heated pitch. Mousterian techniques were later adopted by the Neanderthals, who used roughly the same tools for some 400,000 years. Natural pedagogy As first introduced by Hungarian researchers GergelyCsibra and György Gergely, the term refers to the tendency of human infants to pay attention to the “ostensive” signals of adults (such as pointing at an object and naming it), to treat these as acts of communication, and to assume what is being communicated is something generally useful. For example, if an adult caretaker points to a cat and says “cat,” the child assumes that other cat-like animals, not just this particular one, are cats. In this book, I use the term more generally, to refer to the biologically inherited, universal human capacity for teaching and learning through language, independent of any cultural shaping. Natural selection The process whereby individuals whose genetic programs make them better adapted to their environments are more likely to survive, attract mates, and raise their offspring to maturity—thus increasing the likelihood that their genes will be based along to the next generation. Neanderthal Homo neanderthalensis, commonly known as the Neanderthals, is an extinct hominin descended from H. heidelbergensis, which began moving out of Africa into the Middle East and Europe around 500,000 years ago. In the process, Neanderthals evolved adaptations—including a more robust, muscular body, and larger eyes, with a consequently more brain tissue devoted to somatic control and visual processing—to a rugged lifestyle hunting mastodons and other large, dangerous animals on the edge of the glaciers, in the lower-light conditions of the northern latitudes. Neanderthals exhibited a full range of modern human behaviors including an expanded foraging area, long-range trade, composite tools, and ritual burial of dead. Some researchers have concluded that Neanderthals had some form of spoken language and were in fact a subspecies of Homo sapiens. Niche construction The process whereby an organism carves out a place for itself in its selected habitat, partly by evolving biological traits that make it better suited for the niche, and partly by altering the environment in ways that are

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beneficial, at least in the short run. Classic examples include bird nests and beaver damns. Object of joint attention The object of attention in a joint attentional activity, e.g., the ball in a game of catch. Oldowan tool industry Prior to the discovery of stone tools (which showed signs of knapping) at Lomekwi 3, an archaeological site in West Turkana, Kenya dated to 3.3 million years ago, (which the researchers suggested should be considered examples of the Lomekwian industry), the earliest evidence of stone tool manufacture had been dated to 2.5 million years ago, based on artifacts found at Olduvai Gorge, Tanzania. These are considered to be the first examples of stone tools associated with the Oldowan industry, which spread throughout much of eastern Africa, then into the Middle East, Europe, Central Asia, and northern China. The industry is associated with late Australopithecus and early species of Homo such as H. habilis and H. ergaster, and early H. erectus, suggesting a line of cultural transmission. Ontogeny Also called ontogenesis, ontogeny refers to the biological development of an individual organism over its lifespan, from conception onward. The process is usefully contrasted with phylogeny, which refers to the biological evolution of an entire species and its adaptive suite. An interesting question is to what extent ontogeny “recapitulates” phylogeny. For example, we may wonder whether the gradual development of perspectivetaking (“theory-of-mind”) circuitry in human infancy somehow reflects its emergence in the early evolutionary history of our species. Pair bonding The tendency for males and females in some animal species (and sometimes members of the same sex) to form relatively permanent social and sexual relationships with each other, sometimes lasting a lifetime, and leading to creation of families and cooperative rearing of offspring. Pair bonding is considered to be a relatively early component in the emergence of the human adaptive suite. Perspective-taking An individual animal’s capacity to view a situation from another animal’s perspective, also known as “empathy.” Perspective-taking is thought to be enabled by a system of neural circuity in the brain which is present in rudimentary form in nonhuman animals, but is considerably more evolved in humans, allowing us, uniquely, to understand other humans as having hidden beliefs, desires, and intentions which may be different from our own, but which are discoverable, and open to manipulation, especially through language. Perspective-taking is therefore a critical component of our capacity for teaching.

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A specialized system of neural circuitry in the human brain that has been implicated in the processing of recursive linguistic structures, and thus in the capacity for full-blown modern language. The phonological loop is derived from similar structures in nonhuman primates that allows an animal to maintain a representation of an auditory signal (say an alarm cry) in short-term memory just long enough to rehearse it—that is, to play it back to itself. Significantly, the phonological loop physically overlaps, and may have coevolved with, a more ancient circuit involved in hand manipulation and interpretation of gestures. Phylogeny The evolutionary process whereby an organisms biological traits, i.e., its adaptive suite, change over time in response to various selection pressures imposed by its environment. Pleistocene A geological epoch which lasted from about 2.6 million to 12 thousand years ago, following the Pliocene, and preceding the Holocene. The Pleistocene, sometimes called the “Ice Age,” was marked by repeated changes in climate. Alternating periods of warming and cooling led to advancing and receding glaciation, changes in sea level, and habitat changes. Nearly all the unique traits in the human adaptive suite evolved during this period. Power grip An adaptation of the bones and muscles in the human hand which makes it easier to grip tree branches, sticks, and baseball bats. Along with the precision grip, the power grip is considered to be a small but significant component of the human adaptive suite. Precision grip An adaptation of the bones and muscles in the human hand— notably a longer thumb—which makes it easier to grip small objects, such as sewing needles, between the thumb and other fingers. Prosociality The biological disposition in some animals to engage in cooperative behaviors that benefit other members of its group. Prosociality is considered an important component of the human adaptive suite. Protolanguage An early form of human communication—likely involving combinations of physical gestures, facial expressions, and vocalizations— which would have lacked evolved features of modern human language (e.g., high-speed speech, recursion, and massive symbolic representation) but would have consisted of other features (including a capacity for organizing joint attention) that would have set it apart from precursor systems employed by nonhuman primates, presumably including the last common ancestor of humans and chimpanzees. Ratchet effect The tendency for processes of social learning, including teaching, to preserve the technical innovations and other cognitive capital Phonological loop

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developed in each preceding generation, leading to the possibility of cumulative culture. Recursion A feature of syntax in modern human language which allows logically infinite (but practically limited) embedding and appending of noun phrases, as in the phrase “the dog that chased the cat that ate the rat that ate the cheese.” Because recursion depends on symbolic representation, expanded short-term memory, and sophisticated neural circuitry for production and interpretation of recursively-structured utterances, recursion is thought to be a relatively recent feature of human language. Selection pressure The tendency for contextual factors—such as changing features of an organism’s environment—to drive the evolution of an organism’s adaptive suite in a certain direction. For example, the enhancement of a defense mechanism in a prey species (such as an ability to run faster) creates selection pressure for its predator to compensate (by evolving the ability to run faster itself ). Sexual selection A form of natural selection whereby a member of one biological sex is attracted to a certain physical or behavioral trait in the opposite sex, with the result that genes for that trait (or the tendency to be attracted to the trait) are more likely to propagate. Sexual selection is given as the explanation for evolution of such traits as the male peacock’s awkwardly beautiful tail, and the equally gaudy nests constructed by male bowerbirds. Over the course of human evolution, it is reasonable to think that an individual’s capacity for language—as a demonstration of fitness—may have been subject to sexual selection. Social cognition Also known as “social intelligence,” the term refers to aspects of a social animal’s cognitive functioning that enhance its ability to survive, compete, and (in the case of humans) cooperate with other members of the group. Components of social cognition may include the ability to recognize individuals, predict the future behavior of individuals based on past performance, recognize affiliations and kinship relationships among different individuals, and (in the case of humans) to recognize other individuals as “cognitive agents” with their own sets of beliefs, desires, and intentions, which may be different from one’s own. Social learning The ability to acquire cultural knowledge and skill from others, such as by imitation (copying behaviors directly), emulation (attempting to reproduce the outcomes of behaviors), teaching, or participation with others in joint attentional activity.

348

Glossary

A communicative act designed to alter an interlocutor’s thoughts, beliefs, or behavior, including verbal behavior. Examples include questions, answers, suggestions, recommendations, promises, and threats. Speech organs The evolved biological adaptations that make high-speed speech in humans possible. These include: enhanced control of chest muscles; a descended larynx and modified vocal tract; neural circuitry for complex control of specialized muscles in the mouth, tongue, and lips; the phonological loop; and complementary auditory systems that allow humans to segment incoming soundwaves and rapidly translate them into units of meaning at the rate of 10–15 phonemes per second. Symbolic reference The special way that an arbitrary symbol (such as the sound of the word “caterpillar”) can refer to a category of things in the real world (actual animals of a certain type) and also to other symbols (“insect”). Massive symbolic reference, with multiple overlapping categories and relationships among categories, is a distinguishing feature of modern human language. Syntax Intuitive rules governing the organization of words into meaningful sentences. Complex syntax, including recursion, is a distinguishing feature of modern human language. It is noteworthy that all cognitively normal humans, in every culture, master the basic syntax of whatever language or languages are spoken in their community by about the age of 5, even though these rules are so complex that even trained linguists have difficulty describing them. Theory of mind Also called perspective-taking, theory of mind (ToM) refers to an animal’s ability to understand others as individual cognitive agents who have hidden desires, beliefs, and intentions that may be different from one’s own. As suggested by the title of a famous paper on the subject— “Do chimpanzees have a theory of mind”—ToM was initially understood as an unanalyzed capacity, which a particular animal either has or doesn’t. However, ToM has more recently been understood to have emerged from precursor cognitive capacities—including gaze following and interpretation of goal-directed behaviors—shared by nonhuman primates. That said, humans are understood to have substantially enhanced perspective-taking circuitry, which lies at the heart of our unique capacity for language, and for teaching through language. Teaching A behavior in which one organism (the teacher) goes out of its way to help another organism (typically a member of the same species) acquire new knowledge or skill that it would not be able to acquire, or acquire as easily, without such assistance. Whereas many other animals teach, only humans have evolved the capacity to teach through language. Speech act

Index

A

absolutist stage in development of epistemic understanding 269, 274, 276 accountable talk 308 Acheulean tool industry 94, 281, 335, 341 and language, 102 evidence of children practicing, 187 persistence of, 110 action as epistemic primitive 243 activity setting in teaching, defined 209 adaptive suite 29, 78, 168, 180, 304 hominin, 62, 63 hominin, emergence of, 145 adolescence in humans 187

African Rift Valley 146 agent as epistemic primitive 244 Age of Reason dating to 100,000 ago 300 alarm call(s) as speech act 144 as trait beneficial to kinship group, 27 in monkeys, 137 alarm cries and “expressives” in humans 137 allogrooming in chimpanzees 34 alloparenting 109, 174, 183 AlphaGo 320, 322 Anatomical differences between humans and chimps 31–32 anatomically modern humans 95

© The Editor(s) (if applicable) and The Author(s) 2020 D. M. Morrison, The Coevolution of Language, Teaching, and Civil Discourse Among Humans, https://doi.org/10.1007/978-3-030-48543-6

349

350

Index

Anna Karenina 260 Aquatic Ape Hypothesis 68 Ardipithecus ramidus 93 arms race between bats and moths 57 coevolutionary, 57, 61, 336, 337 language, brains, and technology, 110 Australia ancient colonization of 280–288 australopithecines 68, 336, 341, 343 and language, 102 as possible teachers, 103 as stone knappers, 104 bipedalism in, 68 Australopithecus 67, 94, 336, 337, 341, 345 autonoetic consciousness 256 AutoTutor 314, 315

relationship with habitat and pair bonding, 72–74 birth canal size of in humans 169 Bloom, Benjamin 312 bowerbird nests compared to Gaudí’s Sagrada Familia 61 brain development in humans 173 in humans and chimpanzees, 42 brain growth in humans 182, 184 Broca’s area 93 Burroughs, William language as “virus from outer space” 132 by-product mutualism 45

C B

Baldwin effect in language evolution 29 beaver dam as example of niche construction 29 Beran, Michael 136 biological motion 243 bipedal gait energy efficient 71 bipedalism as catalyst for emergence of language 66 as complex solution, 69 as explained by Darwin, 68 in nonhuman primates, 66 obligatory in humans, 68

category as epistemic form 239 as epistemic primitive, 245 causal reasoning defined 254 cause-and-effect model as epistemic form 237 Cazden, Courtney 307 child-directed speech 183 childhood in humans (defined) 184 chimpanzee and human societies compared 35 chimpanzee(s) and humans; social and sexual differences 33–35 diet, 33 female mating strategy, 37

Index

monkey hunting, whether “cooperative”, 44 use of tools, 33 Chomsky, Noam 97, 127 civil discourse 225 and “correct answering”, 308 and colonization of Austrialia, 280–288 and epistemic fluency, 306, 307 and epistemology, 234–238 and Eugene Goostman, 312 and modern schooling, 302 and science, 300 and science activities, 310 and small-group learning, 308 and social networking, 316 and teaching, 234 and technology, 280 and uncertain future, 334 as family business, 324 board game analogy, 320 computer programs as participants, 322 defined, 232 “design flaw”, 298 further defined, 234 is cultural, 299 learned through engagement with others, 300 more formal definition, 235 not instinctual, 234 Sax on the Web, 318 unintended consequences, 294 climate change as catalyst in human evolution 71 coevolution 28 brains, technology, language, 149 of traits in a species, 28 cognitive agents

351

understanding in infants 183 cognitive capacities chimpanzees and humans compared 42 cognitive capital 230 cognitive categories as language precursor 132 cognitive map in nonhuman animals 248 cognitive templates in human brains 183 Collins, Allan 263 common ground shared mental representation 149 communication systems differences between human and chimpanzee 49–50 communicative act examples in teaching 207–208 contingent teaching emergence in children by age 7 186 control of fire for cooking 33 conversation as turn-taking dialogue 140 cooperative breeding 38, 128 cooperative foraging 128 in humans, 65 cooperative learning 308 cooperative parenting 170 crank-and-ratchet mechanism 112 cultural transmission, 110 described, 280 Csibra, Gergely 244, 268, 344 cultural norms recognition of in children 185 cultural transmission 94, 111, 319, 345

352

Index

cumulative culture 130

D

Darwin theory of language origin 85 definition as epistemic form 246 Denisovans 96 and language, 101 dimorphism and pair bonding 32 directive as speech act 237 in early teaching “toolkit”, 216 disambiguated pointing 183 as cultural innovation, 151 as tipping point for language evolution, 155 biocultural, 131 cold start problem, 130 partial solution, 131 snowball analogy, 152 displacement and pointing 124 distributed cognition 91, 294, 319 divorce in swans 38 DNA shared by chimpanzees and humans 24 Dunbar 35

E

early weaning and birth interval in humans 185 egalitarianism in small-scale societies 275

episodic memory 249, 256 epistemic discourse move 236 epistemic fluency 247, 260, 262, 268, 278, 306, 307 defined, 237 defined as feature of civil discourse, 240 epistemic form(s) 236, 262, 306, 340 in humans and other animals, 238–260 requisite for civil discourse, 322 epistemic primitive(s) 236, 245, 247 derived from cognitive structures in other animals, 238 epistemic toolkit 236, 238, 241, 246 epistemic understanding 225, 298, 301 as component in human adaptive suite, 275 development in human evolution, 274–278 in the Pleistocene, 287 ontogeny, 268–272 epistemic vigilance 237, 240, 247, 253, 255, 258, 261, 274, 276, 306, 340 and epistemic forms, 279 epistemological understanding.. See epistemic understanding epistemology 274 evaluativist as stage in development of epistemic understanding 271, 276, 299 evolution accidental nature of 63 not purposeful, 27 of adaptive suites, 27

Index

expressive as speech act 137

F

Facebook 293, 296, 317, 319, 324 false beliefs 274, 299 awareness of in children, 269 correction of in children, 186 detection of in children, 298 family provisioning 31, 170 food sharing 45, 177 food sources high value 128 foraging groups optimum size 75 FOXP2 in early modern humans 102 Franklin, Stan 46 French Academy of Sciences 1866 ban on language origin papers 87

G

Gärdenfors, Peter 254 Gergely, György 244, 268, 344 gesture(s) disambiguating gestures in primates 139 gestural dialects among primates, 133 gestural repertoires in chimpanzees, 140 gestural turn-taking among chimpanzees, 49 gestural turn-taking in primates, 133

353

gesturing among captive chimpanzees, 138 in chimpanzees, 49 vs. vocalizations in primates, 138 Gladwell, Malcolm tipping point metaphor 125 global warming 294 Gombe National Park Tanzania 15, 16, 43 Goostman, Eugene 311, 312, 319 gossip hypothesis 35 Gould, Stephen punctuated equilibrium 127 grandmother hypothesis 185 grid cells 249 Grodzins, Morton tipping point metaphor 125 group living in humans and chimpanzees 34

H

head direction cells 249 H. erectus 94, 96, 345 Acheulean tool industry, 95 possible adolescence in, 187 Hewlett, Barry 197 H. habilis 94, 341, 345 as possible teachers, 103 childhood, 184 H. heidelbergensis 96 and language, 101 brain growth, 281 homing pigeon method of navigation 255 hominid “family tree” 26 hominid evolution 25 Homo docens 192

354

Index

honey example of nutritious food difficult to extract 146 human adaptive suite components of 73 human brains energy requirements 77 human language why complex 59 human population bottleneck 60-80 thousand years ago 95 Human Revolution 95 humans distinguishing traits 24 human uniqueness 50 hunter-gatherer societies age structure 179–181 childhood, 179–181 life in remaining, 175–177 lifestyle, 176

I

impact hunters among male chimpanzees 47 imperative pointing 151 inclusivist stage in development of epistemic understanding 271 indexical pointing 183 in infants, 148 tipping point in language evolution, 147 indexical reference 246 industrial farming 294 infanticide in chimpanzees 37 information-transfer model of teaching, disputed 215

initiate-respond-evaluate (IRE) as teaching strategy 307 intelligent tutoring systems 313 intelligent tutors 319 intentional agents 183 understanding in infants, 183

J

Jacob, François 132 on evolutionary “tinkering”, 28 Jane Goodall Institute 16 jigsaw learning 308 joint attention 148, 345 difficulty of achieving as blocking factor, 130 in infants, 148 joint attentional activity 17, 58, 128, 183 and perspective taking, 135 in early humans, 149 limited capacity in nonhuman primates, 147 joint attentional frame 136 joint intentional activity 168 joint intentional purpose 136 juvenile dependency 77, 174

K

Kanzi as symbol user 143 trained to sharpen stones, 103 Kenyanthropus platyops 94 knapping Acheulean and Oldowan methods compared 94 among australopithicines, 104

Index

as evidence of a protolanguage, 281 defined, 92 described in detail, 103 evidence of children practicing more than 1 million years ago?, 187 experiment with graduate students, 105 first evidence of at Lomekwi 3, 94 Levallois technique, Mousterian industry, 96 modern language not necessary, 282 too complex to learn by observation, 106 knuckle walking 32 in chimps, as evolved trait, 30 Kuhn, Deanna 268, 277, 301, 302

L

language and technology 90–91 as “biocultural accident”, 152 Chomsky’s “Modern Onset” hypothesis, 97 “cognitive gadget”, 88 definition, 88 in humans, 49 not “invented”, 28 referential communication system, 148 solution to supersized brain problem, 77–79 language acquisition beginning in utero 182 language evolution blocking factors 128

355

multiple blocking factors and tipping points, 131 language precusors in nonhuman primates 132–133 Latent Semantic Analysis 315 learning ecology 316 in the Pleistocene, 303 lesson study 208 Levallois technique 96, 344 lifeforms persistence over time 145 life history chimpanzees and humans compared 41 in humans, as solution to “supersized brain problem”, 174 lifespan increased in humans 170 list as epistemic form 238 as epistemic primitive, 241 list game defined 240 living fossils compared with pace of human evolution 26 Lombard, Marlize 254

M

MacDonald, Katherine 178 map as epistemic form 248 Marlowe, Frank 175 Maynard, Ashley 192 meerkat teaching in 17 mental categories 144

356

Index

in baboons, 143 in distant human ancestors, 144 metastrategy in teaching, defined 209 teaching, as used by Mayan children, 193 Michaels, Sarah 308 mindreading 129 language precursor in nonhuman primates, 133 mirror neurons 243 mirror system hypothesis 244 Mitteilungsbedürfnis 139 “helpful chattiness”, 213 modern human language 19 modern humans evidence of deepwater fishing 42,000 years ago 285 in southern China 194,00 years ago, 281 migration out of Africa 80,000 years ago, 100 modern language spoken by indigenous Australians 100 modern schooling compared with teaching and learning in the Pleistocene 305 monkey hunting “a rich chimp’s sport” 47 monogamous relationships in humans and other animals 38 monogamy transition to 73 moon landings number of people involved (over 400,000) 288 Morgan, Thomas knapping experiment 105

motherese.. See child-directed speech Mousterian tool industry 96 multiplist as stage in development of epistemic understanding 270, 275, 276, 278

N

National Parks of Tanzania 43 natural pedagogy 81, 171, 268, 303 Neanderthals 96 and language, 101 neural networks 320 New Caledonian crows tool use 255 niche construction 29 non-Darwinian discontinuity between human language and the communication systems of other primates 133 nut cracking with rocks among wild chimpanzees 103

O

object as epistemic primitive 242 object of joint attention 17 ochre as adhesive in stone tool manufacture 289 O’Connor, Cathrine 308 O’Keefe, John 247 Oldowan tool industry 94–95, 345 persistence, 110 ontogenetic ritualization 139 ostensive signals in mother-infant interactions 182

Index

ovulation hidden in humans 39 oxytocin 34, 39 and pair bonding, 39

P

pair bonding 65, 128 advantages, 39 and brain size, 40 cost of, 40 in humans, 65 in voles, 39 linked to dimorphism, 32 relationship with habitat and bipedalism, 72–74 paternity confusion 2, 37 peer culture in human childhood 186 personhood as epistemic form 252 phonological loop and recursion 99 Piaget, Jean 20 place as epistemic primitive 247 place cells 247 in rats, 249 plan as epistemic primitive 251 Pliocene as period of climate change 71 pointing ambiguity as virtue 153 and symbolic reference, 124 as communicative act in human infants, 48 as tipping point for language evolution, 125

357

disambiguated, as cultural innovation, 129 emergence in infants, 136 geometric thinking, 123 imperative, 48 imperative in chimpanzees, 135 imperative vs. declarative, 156 in chimpanzees, 135 indexical, as “road to language”, 121–131 indexical, defined, 122 multiple purposes, 122 with disambiguating signals, 122 power grip 32 power scavenging 167 precision grip 32 precursors biological 28 Premack, David 135 primate cognition and foraging skill 134 primate communication systems 136–139 problem epistemic form 255 problem-based learning 308 problem-solution as epistemic form 237 procedure as epistemic primitive 251 promiscuity and monogamy compared 38 prosociality 299 defined, 76 protolanguage 19, 90, 114, 124, 131, 155, 167 as a way of “getting things done”, 167 as part of adaptive suite, 169

358

Index

estimated origin of, 153 explanation for persistence, 154 impact on biology, 130 not discontinuous with human language, 59 sexual selection, 169 public education emergence of 304 pull of the real in respect to false beliefs 269 punctuated equilibrium 127

R

raccoon hunting among humans 46 realist as stage in development of epistemic understanding 268 recursion and symbolic reference 98 feature of modern human language, 98 red colobus monkeys hunted by chimpanzees 43, 44 reinforcement learning 320 rope-pulling experiment as evidence of cognitive blind spot in chimps 48

S

Sahelanthropus 66 possible common ancestor of chimpanzees and humans, 66 Savannah theory 68 as explanation for bipedalism in humans, 68 Sax on the Web 318

scaffolding as teaching strategy 252, 305, 307, 314 in chimpanzees, 16 in nonhuman animals, 217 teaching strategy, defined, 217 scavenging first evidence of in human ancestors 72 screams acoustic properties 137 scrub jays food caching 249 sectorial canine cluster (SCC) lack of in last common ancestor of chimps and humans 32 selection pressure for advanced cognition in human ancestors 74–77 in evolution, 28 sexual arrangements in humans and chimpanzees 36 shared intentionality 45, 46 Shea, John 187 Sinha, Chris 29 small-scale societies learning to hunt in 190–191 social intelligence 144, 151, 253 as language precursor, 108 as precursor for language, 134 social learning 23, 42, 129 in chimpanzee monkey hunting, 47 “learning without teaching”, 18 of vocalizations and gestures in nonhuman primates, 140 social networking sites 316, 317 spear throwers 92 speech acts 198

Index

proto, 139 sperm competition 39 stone tools earliest evidence 280 story as epistemic form 256 strategy in board games 320 teaching strategy, defined, 209 string early use of, 300,000 years ago 285 symbolic reference 114, 246, 248, 275 in modern human language, 142 in monkey alarm cries?, 142 latent capacity in chimpanzees and bonobos, 133

T

tactic 326 in board game, 320 teaching tactic, defined, 209 task-embedded discourse 230 teaching as defined by Caro and Hauser 13 as deliberate communicative act, 17 biocultural behavior, 80 biocultural in humans, 170–172 biological components, 172 cultural universal, 206 developmental trajectory in Mayan children, 192 early “toolkit” of communicative acts, 214 emergent teaching in human 4-year-olds, 186

359

estimated period of origin, 19 evolution of tactics, strategies, metastrategies, 218–222 in adulthood, 189 in humans, definition, 17 in nonhuman animals, 10, 165 in non-human primates, 11–13 in small-scale societies, 13 in small-scale societies in Central African Republic, 195 in young children, 48 need to offset cost of, 106 ontogeny in humans, 187–190 phylogeny, 172 solution to “supersized brain problem”, 173–174 “styles” employed by Mayan children, 192 tactics, strategies, metastrategies, 209 universal tactics, 218 teaching episodes viii defined, 207 teaching in humans critical neural components 182 developmental trajectory, 181 teaching ontogeny 172 technology definition 59 persistence of, 116 termite fishing evidence of planning in chimpanzees 251 in chimpanzees, 15, 18, 20, 34, 48, 70, 91, 104, 166 testicles size of in humans and chimps 36 theory as epistemic form 258

360

Index

theory of mind 129, 158, 245, 269, 274 as language precursor, 133 deconstructed, 134 theory-of-mind circuitry 183, 298 third eye and mindreading 151 perspective-taking circuitry, 129 timeline as epistemic form 250 time travel in chimpanzees 251 in nonhuman animals, 250 tipping point five components 126–128 Tolman, Edward 248 Tomasello, Michael 135, 269 tool manufacture 94, 95, 345 earliest evidence, 94 tool use in nonhuman animals 254 tropical deforestation 294 Tulving, Endel 256 Turing, Alan 319 turn-taking complexity 140 gestural, in bonobos and chimps, 141 turn-taking dialogue 298, 303 by 18 months in humans, 183 in human mothers and infants, 183

Twitter 293, 296, 317, 319

U

unbounded Merge 97, 127

V

vervet monkeys alarm calls 12 use of categories, 245 Vygotsky, Lev 20, 217

W

waggle dance in honey bees 11 warning as speech act 147 human expressive akin to animal alarm cries, 215 Waterside Ape Theory 68 webbed feet as example of adaptive trait 27 Woodruff, Guy 135 Wright, Isaac Morrison 235 method of directing attention, 157

Z

Zone of Proximal Development ZPD, defined 217