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The Bayesian Mutation Sampler Explains Distributions of Causal Judgments
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 pr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320818/ https://www.ncbi.nlm.nih.gov/pubmed/37416078 http://dx.doi.org/10.1162/opmi_a_00080 |
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author | Kolvoort, Ivar R. Temme, Nina van Maanen, Leendert |
author_facet | Kolvoort, Ivar R. Temme, Nina van Maanen, Leendert |
author_sort | Kolvoort, Ivar R. |
collection | PubMed |
description | 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%. |
format | Online Article Text |
id | pubmed-10320818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103208182023-07-06 The Bayesian Mutation Sampler Explains Distributions of Causal Judgments Kolvoort, Ivar R. Temme, Nina van Maanen, Leendert Open Mind (Camb) Research Article 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%. MIT Press 2023-06-15 /pmc/articles/PMC10320818/ /pubmed/37416078 http://dx.doi.org/10.1162/opmi_a_00080 Text en © 2023 Massachusetts Institute of Technology https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Article Kolvoort, Ivar R. Temme, Nina van Maanen, Leendert The Bayesian Mutation Sampler Explains Distributions of Causal Judgments |
title | The Bayesian Mutation Sampler Explains Distributions of Causal Judgments |
title_full | The Bayesian Mutation Sampler Explains Distributions of Causal Judgments |
title_fullStr | The Bayesian Mutation Sampler Explains Distributions of Causal Judgments |
title_full_unstemmed | The Bayesian Mutation Sampler Explains Distributions of Causal Judgments |
title_short | The Bayesian Mutation Sampler Explains Distributions of Causal Judgments |
title_sort | bayesian mutation sampler explains distributions of causal judgments |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320818/ https://www.ncbi.nlm.nih.gov/pubmed/37416078 http://dx.doi.org/10.1162/opmi_a_00080 |
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