ArticleOpen mind : discoveries in cognitive science2023
The Bayesian Mutation Sampler Explains Distributions of Causal Judgments.
Article in Open mind : discoveries in cognitive science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A Magic Act in Causal Reasoning: Making Markov Violations Disappear.Entropy (Basel, Switzerland) · 2025Article
- Noise in Cognition: Bug or Feature?Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Models of Variability in Probabilistic Causal Judgments.Computational brain & behavior · 2025Article
- Probabilistic causal reasoning under time pressure.PloS one · 2024Article
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3 authors.
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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 Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.
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