Proceedings of the Annual Meeting of the Cognitive Science Society

Investigating how people evaluate more or less complex causal theories has been a focal point of research. However, previous studies have either focused on token-level causation or restricted themselves to very small sets of explanatory variables. We provide a new approach for modeling theory selection that foregrounds the balance between observed and latent structure in the mechanism being explained. We combine a Bayesian framework with program induction, allowing an unbounded and partially observable model space through sampling, and reflecting how a preference for simplicity emerges naturally in this setting. Through simulation, we identify two rational principles: (1) Simpler explanations should be favored as latent uncertainty (the number of hidden variables) increases; (2) latent structure is attributed a larger role when the observable patterns become less compressible. We conducted a behavioral experiment and found that human judgments tended to reflect these principles, indicating that people are sensitive to latent uncertainty when selecting between explanations.