Motivated by computational analyses, we look at how teaching affects exploration and discovery. In Experiment 1, we investigated children’s exploratory play after an adult pedagogically demonstrated a function of a toy, after an interrupted pedagogical demonstration, after a naïve adult demonstrated the function, and at baseline. Preschoolers in the pedagogical condition focused almost exclusively on the target function; by contrast, children in the other conditions explored broadly. In Experiment 2, we show that children restrict their exploration both after direct instruction to themselves and after overhearing direct instruction given to another child; they do not show this constraint after observing direct instruction given to an adult or after observing a non-pedagogical intentional action. We discuss these findings as the result of rational inductive biases. In pedagogical contexts, a teacher’s failure to provide evidence for additional functions provides evidence for their absence; such contexts generalize from child to child (because children are likely to have comparable states of knowledge) but not from adult to child. Thus, pedagogy promotes efficient learning but at a cost: children are less likely to perform potentially irrelevant actions but also less likely to discover novel information.

Researchers, educators, and parents have long believed that children learn cause and effect relationships through exploratory play. However, previous research suggests that children are poor at designing informative experiments; children fail to control relevant variables and tend to alter multiple variables simultaneously. Thus, little is known about how children's spontaneous exploration might support accurate causal inferences. Here the authors suggest that children's exploratory play is affected by the quality of the evidence they observe. Using a novel free-play paradigm, the authors show that preschoolers (mean age: 57 months) distinguish confounded and unconfounded evidence, preferentially explore causally confounded (but not matched unconfounded) toys rather than novel toys, and spontaneously disambiguate confounded variables in the course of free play.

We look at the effect of evidence and prior beliefs on exploration, explanation and learning. In Experiment 1, we tested children both with and without differential prior beliefs about balance relationships (Center Theorists, mean: 82 months; Mass Theorists, mean: 89 months; No Theory children, mean: 62 months). Center and Mass Theory children who observed identical evidence explored the block differently depending on their beliefs. When the block was balanced at its geometric center (belief-violating to a Mass Theorist, but belief-consistent to a Center Theorist), Mass Theory children explored the block more, and Center Theory children showed the standard novelty preference; when the block was balanced at the center of mass, the pattern of results reversed. TheNo Theory children showed a novelty preference regardless of evidence. In Experiments 2 and 3, we follow-up on these findings, showing that both Mass and Center Theorists selectively and differentially appeal to auxiliary variables (e.g., a magnet) to explain evidence only when their beliefs are violated. We also show that children use the data to revise their predictions in the absence of the explanatory auxiliary variable but not in its presence. Taken together, these results suggest that children's learning is at once conservative and flexible; children integrate evidence, prior beliefs, and competing causal hypotheses in their exploration, explanation, and learning.

Explore-exploit decisions require us to trade off the benefits of exploring unknown options to learn more about them, with exploiting known options, for immediate reward. Such decisions are ubiquitous in nature, but from a computational perspective, they are notoriously hard. There is therefore much interest in how humans and animals make these decisions and recently there has been an explosion of research in this area. Here we provide a biased and incomplete snapshot of this field focusing on the major finding that many organisms use two distinct strategies to solve the explore-exploit dilemma: a bias for information ('directed exploration') and the randomization of choice ('random exploration'). We review evidence for the existence of these strategies, their computational properties, their neural implementations, as well as how directed and random exploration vary over the lifespan. We conclude by highlighting open questions in this field that are ripe to both explore and exploit.

Causal learning requires integrating constraints provided by domain-specific theories with domaingeneral statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB 3 E; CA 3 E; AD 3 E; and so forth. In 1 story, all variables came from the same domain; in the other, the recurring candidate cause, A, came from a different domain (A was a psychological cause of a biological effect). After receiving this statistical evidence, children were asked to identify the cause of the effect on a new trial. Consistent with the predictions of a Bayesian model, all children were more likely to identify A as the cause within domains than across domains. Whereas 3.5-year-olds learned only from the within-domain evidence, 4-and 5-year-olds learned from the cross-domain evidence and were able to transfer their new expectations about psychosomatic causality to a novel task.Keywords: domain-general and domain-specific causal learning, Bayesian models, naive theories, ambiguous evidence, psychosomatic causes Supplemental materials: http://dx.doi.org/10.1037/0012-1649. 43.5.1124.supp By the time children are 5 years old, they understand causal relationships in a variety of domains (Flavell, Green, & Flavell, 1995;Gelman & Wellman, 1991;Gopnik & Meltzoff, 1997;Inagaki & Hatano, 1993;Kalish, 1996;Perner, 1991;Shultz, 1982;Spelke, Breinlinger, Macomber, & Jacobson, 1992). Many researchers have suggested that children's causal knowledge can be best characterized as a set of naive theories: abstract, coherent representations of causal structure that support prediction, intervention, explanation, and counterfactual claims (Carey, 1985;Gopnik, 1988;Gopnik & Meltzoff, 1997;Harris, German, & Mills, 1996;Hickling & Wellman, 2001;Keil, 1989;Perner, 1991;Sobel, 2004;Wellman, 1990;Wellman, Hickling, & Schult, 1997). The view that children's causal representations resemble scientific theories (the theory theory) suggests both that patterns of evidence should affect children's causal commitments and that children's causal commitments should affect their interpretation of evidence. Indeed, this dynamic relationship between domain-appropriate causal beliefs and evidence has been taken as a defining feature of theories (e.g., Gopnik & Meltzoff, 1997).However, despite the expectation that theory and evidence should interact, developmental psychologists have been largely divided between accounts of causal reasoning emphasizing either domain-specific causal knowledge or domain-general learning from data. Thus, some researchers have suggested that children's naive theories might be generated by domain-specific modules (Leslie, 1994;Scholl & Leslie, 1999) or innate concepts in core domains (Carey & Spelke, 1994;Keil, 1995), whereas other researchers have focused on children's ability to learn causal rel...