A Rescorla-Wagner drift-diffusion model of conditioning and timing
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 786
- Type
- D - Journal article
- DOI
-
10.1371/journal.pcbi.1005796
- Title of journal
- PLoS Computational Biology
- Article number
- e1005796
- First page
- -
- Volume
- 13
- Issue
- 11
- ISSN
- 1553-734X
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2017
- 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
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research combined the simplicity of the noisy variability entailed by random walk processes in forced-choice decision making (drift-diffusion) and the Rescorla-Wagner rule's success to offer a computational account of timing behaviour that unifies two rival theories of learning in a single framework. The model not only overcomes limitations held by each approach but shows that associative and dynamic systems are not excluding nor incompatible, acting as a beacon for future research interactions. It has been deemed pertinent in studying the temporal flow of dopaminergic pathways and hippocampal memory, computational accounts of neuropsychiatric disorders, and the credit assignment problem.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
