Children balance theories and evidence in exploration, explanation, and learning.

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. The No 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.

Serious fun: preschoolers engage in more exploratory play when evidence is confounded.

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.

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Causal learning requires integrating constraints provided by domain-specific theories with domain-general 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?E; CA?E; AD?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.

Occam's rattle: children's use of simplicity and probability to constrain inference.

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and inference, but little is known about how children generate and select competing explanations. This study investigates whether young children prefer explanations that are simple, where simplicity is quantified as the number of causes invoked in an explanation, and how this preference is reconciled with probability information. Both preschool-aged children and adults were asked to explain an event that could be generated by 1 or 2 causes, where the probabilities of the causes varied across conditions. In 2 experiments, it was found that children preferred explanations involving 1 cause over 2 but were also sensitive to the probability of competing explanations. Adults, in contrast, responded on the basis of probability alone. These data suggest that children employ a principle of parsimony like Occam's razor as an inductive constraint and that this constraint is employed when more reliable bases for inference are unavailable.

Just do it? Investigating the gap between prediction and action in toddlers' causal inferences.

Adults' causal representations integrate information about predictive relations and the possibility of effective intervention; if one event reliably predicts another, adults can represent the possibility that acting to bring about the first event might generate the second. Here we show that although toddlers (mean age: 24 months) readily learn predictive relationships between physically connected events, they do not spontaneously initiate one event to try to generate the second (although older children, mean age: 47 months, do; Experiments 1 and 2). Toddlers succeed only when the events are initiated by a dispositional agent (Experiment 3), when the events involve direct contact between objects (Experiment 4), or when the events are described using causal language (Experiment 5). This suggests that causal language may help children extend their initial causal representations beyond agent-initiated and direct contact events.

Inductive reasoning about causally transmitted properties.

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.