The effects of base rate neglect on sequential belief updating and real-world beliefs.
Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neg...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010796 |
| _version_ | 1811167271828062208 |
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| author | Brandon K Ashinoff Justin Buck Michael Woodford Guillermo Horga |
| author_facet | Brandon K Ashinoff Justin Buck Michael Woodford Guillermo Horga |
| author_sort | Brandon K Ashinoff |
| collection | DOAJ |
| description | Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neglect with sufficient explanatory power to account for longer-term and real-world beliefs is lacking. A Bayesian formalization of base-rate neglect in the context of sequential belief updating predicts that belief trajectories should exhibit dynamic patterns of dependence on the order in which evidence is presented and its consistency with prior beliefs. To test this, we developed a novel 'urn-and-beads' task that systematically manipulated the order of colored bead sequences and elicited beliefs via an incentive-compatible procedure. Our results in two independent online studies confirmed the predictions of the sequential base-rate neglect model: people exhibited beliefs that are more influenced by recent evidence and by evidence inconsistent with prior beliefs. We further found support for a noisy-sampling inference model whereby base-rate neglect results from rational discounting of noisy internal representations of prior beliefs. Finally, we found that model-derived indices of base-rate neglect-including noisier prior representation-correlated with propensity for unusual beliefs outside the laboratory. Our work supports the relevance of Bayesian accounts of sequential base-rate neglect to real-world beliefs and hints at strategies to minimize deleterious consequences of this pervasive bias. |
| first_indexed | 2024-04-10T16:06:32Z |
| format | Article |
| id | doaj.art-e82be40c27bf4efbaaae4a4a94d447fd |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T16:06:32Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e82be40c27bf4efbaaae4a4a94d447fd2023-02-10T05:30:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-12-011812e101079610.1371/journal.pcbi.1010796The effects of base rate neglect on sequential belief updating and real-world beliefs.Brandon K AshinoffJustin BuckMichael WoodfordGuillermo HorgaBase-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neglect with sufficient explanatory power to account for longer-term and real-world beliefs is lacking. A Bayesian formalization of base-rate neglect in the context of sequential belief updating predicts that belief trajectories should exhibit dynamic patterns of dependence on the order in which evidence is presented and its consistency with prior beliefs. To test this, we developed a novel 'urn-and-beads' task that systematically manipulated the order of colored bead sequences and elicited beliefs via an incentive-compatible procedure. Our results in two independent online studies confirmed the predictions of the sequential base-rate neglect model: people exhibited beliefs that are more influenced by recent evidence and by evidence inconsistent with prior beliefs. We further found support for a noisy-sampling inference model whereby base-rate neglect results from rational discounting of noisy internal representations of prior beliefs. Finally, we found that model-derived indices of base-rate neglect-including noisier prior representation-correlated with propensity for unusual beliefs outside the laboratory. Our work supports the relevance of Bayesian accounts of sequential base-rate neglect to real-world beliefs and hints at strategies to minimize deleterious consequences of this pervasive bias.https://doi.org/10.1371/journal.pcbi.1010796 |
| spellingShingle | Brandon K Ashinoff Justin Buck Michael Woodford Guillermo Horga The effects of base rate neglect on sequential belief updating and real-world beliefs. PLoS Computational Biology |
| title | The effects of base rate neglect on sequential belief updating and real-world beliefs. |
| title_full | The effects of base rate neglect on sequential belief updating and real-world beliefs. |
| title_fullStr | The effects of base rate neglect on sequential belief updating and real-world beliefs. |
| title_full_unstemmed | The effects of base rate neglect on sequential belief updating and real-world beliefs. |
| title_short | The effects of base rate neglect on sequential belief updating and real-world beliefs. |
| title_sort | effects of base rate neglect on sequential belief updating and real world beliefs |
| url | https://doi.org/10.1371/journal.pcbi.1010796 |
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