How was the evolution of our unique biological life history related to distinctive human developments in cognition and culture? We suggest that the extended human childhood and adolescence allows a balance between exploration and exploitation, between wider and narrower hypothesis search, and between innovation and imitation in cultural learning. In particular, different developmental periods may be associated with different learning strategies. This relation between biology and culture was probably coevolutionary and bidirectional: life-history changes allowed changes in learning, which in turn both allowed and rewarded extended life histories. In two studies, we test how easily people learn an unusual physical or social causal relation from a pattern of evidence. We track the development of this ability from early childhood through adolescence and adulthood. In the physical domain, preschoolers, counterintuitively, perform better than school-aged children, who in turn perform better than adolescents and adults. As they grow older learners are less flexible: they are less likely to adopt an initially unfamiliar hypothesis that is consistent with new evidence. Instead, learners prefer a familiar hypothesis that is less consistent with the evidence. In the social domain, both preschoolers and adolescents are actually the most flexible learners, adopting an unusual hypothesis more easily than either 6-y-olds or adults. There may be important developmental transitions in flexibility at the entry into middle childhood and in adolescence, which differ across domains.causal reasoning | social cognition | cognitive development | adolescence | life history O ne of the most distinctive biological features of human beings is our unusual life history. Compared with our closest primate relatives, we have a dramatically extended childhood, including an exceptionally long middle childhood and adolescence. Moreover, humans have shorter interbirth intervals than our closest primate relatives, producing an even greater number of less-capable children (1). There is evidence for other human 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 mothers: pair-bonded fathers (2), alloparents (3), and postmenopausal women, in particular, grandmothers (4).It may seem evolutionarily paradoxical that humans would have developed a life history that includes such expensive and vulnerable young for such a long period. However, across many different species, including birds and both placental and marsupial mammals, there is a very general (although not perfect) correlation between relative brain size, intelligence and a reliance on learning, and an extended period of immaturity (5-7). This correlation suggests a relation between our distinctive human life history and our equally distinctive large brains and reliance on learning, particularly cultural learning. Such a relation between biology and culture ...
We argue for a theoretical link between the development of an extended period of immaturity in human evolution and the emergence of powerful and wide-ranging causal learning mechanisms, specifically the use of causal models and Bayesian learning. We suggest that exploratory childhood learning, childhood play in particular, and causal cognition are closely connected. We report an empirical study demonstrating one such connection-a link between pretend play and counterfactual causal reasoning. Preschool children given new information about a causal system made very similar inferences both when they considered counterfactuals about the system and when they engaged in pretend play about it. Counterfactual cognition and causally coherent pretence were also significantly correlated even when age, general cognitive development and executive function were controlled for. These findings link a distinctive human form of childhood play and an equally distinctive human form of causal inference. We speculate that, during human evolution, computations that were initially reserved for solving particularly important ecological problems came to be used much more widely and extensively during the long period of protected immaturity.
Direct instruction facilitates learning without the costs of exploration, yet teachers must be selective because not everything can nor needs to be taught. How do we decide what to teach, and what to leave for learners to discover? Here we investigate the cognitive underpinnings of the human ability to prioritise what to teach. We present a computational model that decides what to teach by maximising the learner's expected utility of learning from instruction and from exploration, and show that children (age 5-7) make decisions that are consistent with the model's predictions (i.e., minimising the learner's costs and maximising the rewards). Children flexibly considered either the learner's utility or their own depending on the context and even considered costs they had not personally experienced to decide what to teach. These results suggest that utility-based reasoning may play an important role in curating cultural knowledge by supporting selective transmission of high-utility information.DECIDING WHAT TO TEACH 3 Humans actively explore their surroundings and learn from their own experience. 1 Even young children direct their own learning through exploratory play and update their 2 beliefs from self-generated evidence 1,2,3,4 . However, exploration involves uncertainty; 3 learners may not know the time and e ort required to make a discovery, and even after 4 much trial-and-error, they may fail to discover anything at all. Such uncertainty can make 5 it especially challenging for novice learners to decide what or when to explore, because they 6 often lack the knowledge or experience to estimate the expected costs and rewards of 7 exploration. 8 Social learning provides an e ective solution to this problem. Teaching, in particular, 9 is a powerful way to facilitate learning. When knowledgeable individuals teach what they 10 know, naïve learners can avoid the uncertainties of exploration and acquire useful 11 knowledge even when self-guided discovery is too costly 5 . For instance, a hunter-gatherer 12 who already knows how to make fire can save others from hours of trial-and-error by 13 showing them how to make it. However, teaching requires an investment of time, e ort, 14 and resources 6 , making it necessary for teachers to be selective in what they choose to 15 teach. Indiscriminately teaching anything would be ine cient; not all of one's knowledge is 16 useful for others, and some knowledge can be acquired easily without being taught or only 17 through direct experience. Relying upon learners' requests to decide what to teach would 18 also be ine ective; ignorant learners may be unaware of what they need to learn or unable 19 to communicate their requests. Thus, while teaching yields significant benefits for learners, 20 it also presents a decision-problem for teachers: What needs to be taught, and what can be 21 left for learners to discover on their own? 22As adults, we share the intuition that it is better to teach things that are useful, 23 novel, and interesting for others (i.e., ...
a b s t r a c tWe explore the developmental trajectory and underlying mechanisms of abstract relational reasoning. We describe a surprising developmental pattern: Younger learners are better than older ones at inferring abstract causal relations. demonstrated that toddlers are able to infer that an effect was caused by a relation between two objects (whether they are the same or different), rather than by individual kinds of objects. While these findings are consistent with evidence that infants recognize same-different relations, they contrast with a large literature suggesting that older children tend to have difficulty inferring these relations. Why might this be? In Experiment 1a, we demonstrate that while younger children (18-30-month-olds) have no difficulty learning these relational concepts, older children (36-48-month-olds) fail to draw this abstract inference. Experiment 1b replicates the finding with 18-30-month-olds using a more demanding intervention task. Experiment 2 tests whether this difference in performance might be because older children have developed the general hypothesis that individual kinds of objects are causal -the high initial probability of this alternative hypothesis might override the data that favors the relational hypothesis. Providing additional information falsifying the alternative hypothesis improves older children's performance. Finally, Experiment 3 demonstrates that prompting for explanations during learning also improves performance, even without any additional information. These findings are discussed in light of recent computational and algorithmic theories of learning.
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