Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood Alison Gopnika,1, Shaun O’Gradya, Christopher G. Lucasb, Thomas L. Griffithsa, Adrienne Wentea, Sophie Bridgersc, Rosie Aboodyd, Hoki Funga, and Ronald E. Dahle aDepartment of Psychology, University of California, Berkeley, CA 94720; bSchool of Informatics, University of Edinburgh, Edinburgh EH1 2QL, United Kingdom; cDepartment of Psychology, Stanford University, Stanford, CA 94305; dDepartment of Psychology, Yale University, New Haven, CT 06520; and eSchool of Public Health, University of California, Berkeley, CA 94720 Edited by Andrew Whiten, University of St. Andrews, St. Andrews, United Kingdom, and accepted by Editorial Board Member Andrew G. Clark April 8, 2017 (received for review January 18, 2017) How was the evolution of our unique biological life history related culture would have been coevolutionary and bidirectional: life- to distinctive human developments in cognition and culture? We history changes allowed changes in cultural learning, which in suggest that the extended human childhood and adolescence turn both allowed and rewarded extended life histories. In this allows a balance between exploration and exploitation, between way, culture could have extended biology. wider and narrower hypothesis search, and between innovation A number of researchers have suggested that our life history is and imitation in cultural learning. In particular, different develop- related to our learning abilities (8–10). But what might this re- mental periods may be associated with different learning strate- lation be like in more detail? It is possible that the extended gies. This relation between biology and culture was probably human childhood and adolescence is simply a waiting period in coevolutionary and bidirectional: life-history changes allowed which a large brain can grow or cultural learning can take place changes in learning, which in turn both allowed and rewarded (11). However, both developmental psychology and neuroscience extended life histories. In two studies, we test how easily people learn an unusual physical or social causal relation from a pattern of suggest that there may be more substantive differences in evidence. We track the development of this ability from early learning and plasticity in different developmental periods, dif- childhood through adolescence and adulthood. In the physical ferences that could contribute to human intelligence and culture. domain, preschoolers, counterintuitively, perform better than We argue that there may be a developmental trade-off be- school-aged children, who in turn perform better than adolescents tween cognitive abilities that allow organisms to learn the and adults. As they grow older learners are less flexible: they are structure of a new physical or social environment, abilities that less likely to adopt an initially unfamiliar hypothesis that is are characteristic of children, and the more adult abilities that consistent with new evidence. Instead, learners prefer a familiar allow skilled action on a familiar environment. Empirical evi- hypothesis that is less consistent with the evidence. In the social dence suggests that children may sometimes be better, and domain, both preschoolers and adolescents are actually the most particularly more flexible, learners than adults. Ideas from the flexible learners, adopting an unusual hypothesis more easily than literature on developmental neuroscience, machine learning, and either 6-y-olds or adults. There may be important developmental cultural learning may help to characterize and explain these transitions in flexibility at the entry into middle childhood and in developmental differences more precisely. adolescence, which differ across domains. We go on to test these ideas by examining cognitive flexibility across the developmental periods of preschool, middle-childhood, causal reasoning | social cognition | cognitive development | adolescence | adolescence, and adulthood, in both the physical and social domain. life history When Younger Learners Do Better. Younger learners usually have ne of the most distinctive biological features of human more difficulty with cognitive tasks than older children and Obeings is our unusual life history. Compared with our closest adults. Young children have characteristic deficits in executive primate relatives, we have a dramatically extended childhood, function, working memory, attentional focus, and control (12, including an exceptionally long middle childhood and adoles- 13). These are precisely the same abilities required for per- cence. Moreover, humans have shorter interbirth intervals than forming complex skilled actions swiftly and effectively in adult- our closest primate relatives, producing an even greater number hood. Indeed, human children are so dependent on others partly of less-capable children (1). There is evidence for other human because of their deficits in these areas. adaptations that helped cope with this flood of needy young. In contrast to our closest primate relatives, human children enjoy the benefits of care from three sources in addition to biological This paper results from the Arthur M. Sackler Colloquium of the National Academy of mothers: pair-bonded fathers (2), alloparents (3), and post- Sciences, “The Extension of Biology Through Culture,” held November 16–17, 2016, at the menopausal women, in particular, grandmothers (4). Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering It may seem evolutionarily paradoxical that humans would have in Irvine, CA. The complete program and video recordings of most presentations are available on the NAS website at www.nasonline.org/Extension_of_Biology_Through_Culture. developed a life history that includes such expensive and vulner- Author contributions: A.G., S.O., C.G.L., T.L.G., A.W., and R.E.D. designed research; A.G., able young for such a long period. However, across many different S.O., A.W., S.B., R.A., and H.F. performed research; A.G., S.O., and C.G.L. analyzed data; species, including birds and both placental and marsupial mam- and A.G. and S.O. wrote the paper. mals, there is a very general (although not perfect) correlation The authors declare no conflict of interest. between relative brain size, intelligence and a reliance on learning, This article is a PNAS Direct Submission. A.W. is a guest editor invited by the Editorial and an extended period of immaturity (5–7). This correlation Board. suggests a relation between our distinctive human life history and 1To whom correspondence should be addressed. Email: [email protected]. our equally distinctive large brains and reliance on learning, par- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. ticularly cultural learning. Such a relation between biology and 1073/pnas.1700811114/-/DCSupplemental. 7892–7899 | PNAS | July 25, 2017 | vol. 114 | no. 30 www.pnas.org/cgi/doi/10.1073/pnas.1700811114 Downloaded by guest on October 2, 2021 PAPER However, at the same time that their executive abilities are so The different strategies that learners might engage in—and COLLOQUIUM limited, human children learn a tremendous amount about the their consequences for those learners—can also be characterized world easily and rapidly. They quickly and spontaneously learn more precisely by considering cognitive development from the about the causal structure of their physical and social environ- perspective of a probabilistic model approach to cognition. This ments, constructing intuitive theories of the physical, biological, approach, inspired by statistical methods that are widely used in and psychological world (e.g., ref. 14). artificial intelligence and machine learning, has become in- There is also empirical evidence that younger learners sometimes, creasingly influential in cognitive science (e.g., refs. 35–40). counterintuitively, actually outperform older ones on learning tasks, This approach applies particularly naturally to learning the causal showing more flexibility. Younger mice learn to reverse a learned structure of the environment. Probabilistic models of cognition use rule more easily than postpubertal mice (15). Older monkeys show sophisticated causal models to specify the probability of observing a neural plasticity when they learn an auditory or tactile pattern, but particular statistical pattern of evidence if a causal hypothesis is true only when the pattern is relevant to their goals; juveniles extract the (41). This makes it possible to use Bayesian inference to determine patterns and demonstrate plasticity independently of goals (16). the probability that the hypothesis is true given that evidence (42). Among humans, younger learners are more able to learn new Rather than simply generating a yes or no decision about whether a linguistic distinctions than older learners (17, 18) and they are particular hypothesis is true, Bayesian inference evaluates multiple better at imagining new uses for a tool (19). Younger children hypotheses and assigns probabilities to those hypotheses (14, 35– also remember information that is outside the focus of goal- 40). Many studies have presented children with evidence patterns directed attention better than adults and older children (20, 21). and alternative hypotheses that might explain those patterns, and We have recently found that preschool children also outperform found that children characteristically choose hypotheses