A double error dynamic asymptote model of associative learning
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 802
- Type
- D - Journal article
- DOI
-
10.1037/rev0000147
- Title of journal
- Psychological Review
- Article number
- -
- First page
- 506
- Volume
- 126
- Issue
- 4
- ISSN
- 0033-295X
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- 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
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Silent learning is a pending task for reinforcement learning frameworks that rely heavily on behaviour and rewards. No computational model has been able to integrate these phenomena, making conflicting predictions. This output's DDA model is the only one able to fully describe silent learning. It does so with a single assumption that can be empirically tested. Although relevant experimental fields are slow to respond to theoretical advances, the paper has already attracted attention, resulting in seminar invitations (UCL, Cambridge) and academic social-media discussions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
