Probabilistic Reward Learning
Probabilistic Reward Learning is a computational learning framework within reward processing that models how organisms acquire and update expectations about rewards based on uncertain or stochastic environmental feedback, distinguishing itself from deterministic reward learning by incorporating outcome uncertainty into prediction error signaling and decision-making.
103
Publications
8.9K
Citations
427
Authors
208
Institutions
Publications per year
2017–2026
45
Parents
103
Rachel Swainson, Robert D. Rogers, Barbara J. Sahakian et al. · Neuropsychologia · 2000 · 503 citations
Medicine, Neurology, Neuroscience +17
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208
Harvard University
Cambridge, United States
14 h-index
35 publications
4K citations

McLean Hospital
Belmont, United States
8 h-index
33 publications
2K citations
University College London
London, United Kingdom
8 h-index
18 publications
1.9K citations
Massachusetts General Hospital
Boston, United States
7 h-index
13 publications
3.5K citations
University of Oxford
Oxford, United Kingdom
6 h-index
14 publications
573 citations
Ruhr University Bochum
Bochum, Germany
5 h-index
17 publications
701 citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
20 publications
3K citations
Max Planck Institute for Human Development
Berlin, Germany
5 h-index
10 publications
1K citations
1–8 of 208
12 publications · 1.6K citations
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3 publications · 149 citations
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3 publications · 197 citations
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Repository · 3 publications · 45 citations
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3 publications · 260 citations
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