Doubly Bayesian analysis of confidence in perceptual decision-making
- Submitting institution
-
University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 219634145
- Type
- D - Journal article
- DOI
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10.1371/journal.pcbi.1004519
- Title of journal
- PLoS Computational Biology
- Article number
- -
- First page
- e1004519
- Volume
- 11
- Issue
- 10
- ISSN
- 1553-734X
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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A - Artificial Intelligence and Autonomy
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Focusing on the analysis of a specific human confidence dataset, the paper developed a simple probabilistic method for dealing with ordinal confidence ratings (e.g. on a 1-6 scale): understanding how these ratings might arise from an underlying continuous decision-variable, and selecting the best model for the decision-variable, while remaining agnostic to the mapping onto ordinal confidence ratings. This method becomes a standard-practice in the field, and motivated a theoretical model of human behaviour in which people have a separately tunable "average" level of confidence, which can be adapted to different situations (Bang, Aitchison et al, Nature Human Behaviour2017).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -