Journal article
Bayesian confidence in optimal decisions
- Abstract:
- The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favour of the options. The drift diffusion model (DDM) implements this approach, and provides an excellent account of decisions and response times. However, existing DDM-based models of confidence exhibit certain deficits, and many theories of confidence have used alternative, non-optimal models of decisions. Motivated by the historical success of the DDM, we ask whether simple extensions to this framework might allow it to better account for confidence. Motivated by the idea that the brain will not duplicate representations of evidence, in all model variants decisions and confidence are based on the same evidence accumulation process. We compare the models to benchmark results, and successfully apply 4 qualitative tests concerning the relationships between confidence, evidence, and time, in a new preregistered study. Using computationally cheap expressions to model confidence on a trial-by-trial basis, we find that a subset of model variants also provide a very good to excellent account of precise quantitative effects observed in confidence data. Specifically, our results favour the hypothesis that confidence reflects the strength of accumulated evidence penalised by the time taken to reach the decision (Bayesian readout), with the penalty applied not perfectly calibrated to the specific task context. These results suggest there is no need to abandon the DDM or single accumulator models to successfully account for confidence reports.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.8MB, Terms of use)
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- Publisher copy:
- 10.1037/rev0000472
Authors
- Publisher:
- American Psychological Association
- Journal:
- Psychological Review More from this journal
- Volume:
- 131
- Issue:
- 5
- Pages:
- 114–1160
- Publication date:
- 2024-07-18
- Acceptance date:
- 2023-12-30
- DOI:
- EISSN:
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1939-1471
- ISSN:
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0033-295X
- Language:
-
English
- Keywords:
- Pubs id:
-
1596716
- Local pid:
-
pubs:1596716
- Deposit date:
-
2024-01-08
- ARK identifier:
Terms of use
- Copyright holder:
- Calder-Travis et al.
- Copyright date:
- 2024
- Rights statement:
- © 2024 The Author(s). Open Access funding provided by University of Oxford: This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; http://creativecommons.org/licenses/by/4.0). This license permits copying and redistributing the work in any medium or format, as well as adapting the material for any purpose, even commercially.
- Licence:
- CC Attribution (CC BY)
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