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.American Psychological Foundation (Elizabeth Munsterberg Koppitz Fellowship)James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning)National Science Foundation (U.S.) (NSF Faculty Early Career Development Award)Templeton Foundation (Award

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.James S. McDonnell Foundation (Causal Learning Collaborative)American Psychological FoundationTempleton Foundatio

Deconfounding Hypothesis Generation and Evaluation in Bayesian Models

Bayesian models of cognition are typically used to describe human learning and inference at the computational level, identifying which hypotheses people should select to explain observed data given a particular set of inductive biases. However, such an analysis can be consistent with human behavior even if people are not actually carrying out exact Bayesian inference. We analyze a simple algorithm by which people might be approximating Bayesian inference, in which a limited set of hypotheses are generated and then evaluated using Bayes ’ rule. Our mathematical results indicate that a purely computationallevel analysis of learners using this algorithm would confound the distinct processes of hypothesis generation and hypothesis evaluation. We use a causal learning experiment to establish empirically that the processes of generation and evaluation can be distinguished in human learners, demonstrating the importance of recognizing this distinction when interpreting Bayesian models

Training a Bayesian: Three-and-a-half-year-olds' Reasoning about Ambiguous Evidence

Previous work has demonstrated the importance of both naïve theories and statistical evidence to children’s causal reasoning. In particular, four-year-olds can use statistical evidence to update their beliefs. However, the story is more complex for three-year-olds. Although three-and-a-half-year-olds perform as well as four-year-olds when statistical evidence is theory-neutral, several studies suggest that they do not learn from statistical evidence when a statistically likely cause is inconsistent with their prior beliefs (e.g., Schulz et al., 2007). There are at least two possible explanations for younger children’s failure to use statistical data to update their beliefs: one (the Information Processing account) suggests that younger children have a fragile ability to reason about statistical evidence; the other (a Prior Knowledge account) suggests that in some domains, younger children have stronger prior beliefs and thus require more evidence before belief revision is rational. To distinguish these accounts, we conducted a two-week training study with three-and-a-half-year-olds. Children participated in an Information Processing Training condition, a Prior Belief Training condition, or a Control condition. Relative to the Control condition, children in the Prior Belief Training condition, but not children in the Information Processing Training condition showed an overall improvement in their ability to reason about theory-violating evidence. This suggests that at least some developmental differences in statistical reasoning tasks may be due to younger children’s stronger prior beliefs