The Bayesian Mutation Sampler Explains Distributions of Causal Judgments.

ABSTRACT: One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in 'mutation sampling' when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Samp

SUBMITTER: Kolvoort IR 

PROVIDER: S-EPMC10320818 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature