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 computational-level 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.

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