The Bayesian Mutation Sampler Explains Distributions of Causal Judgments.

Autor:	Kolvoort IR; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.; Institute for Logic, Language, and Computation, University of Amsterdam, Amsterdam, The Netherlands., Temme N; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands., van Maanen L; Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands.
Jazyk:	angličtina
Zdroj:	Open mind : discoveries in cognitive science [Open Mind (Camb)] 2023 Jun 15; Vol. 7, pp. 318-349. Date of Electronic Publication: 2023 Jun 15 (Print Publication: 2023).
DOI:	10.1162/opmi_a_00080
Abstrakt:	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%. (© 2023 Massachusetts Institute of Technology.)
Databáze:	MEDLINE
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