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

Authors

427

  1. DA
    Diego A. Pizzagalli

    Harvard University, United States

    12 publications · 2.1K citations

    12

    h-index

  2. CB
    Christian Bellebaum

    Ruhr University Bochum, Germany

    5 publications · 209 citations

    5

    h-index

  3. BD
    Brian D. Kangas

    McLean Hospital, United States

    4 publications · 117 citations

    4

    h-index

  4. RB
    Ryan Bogdan

    Washington University in St. Louis, United States

    4 publications · 800 citations

    4

    h-index

  5. ID
    Irene Daum

    Ruhr University Bochum, Germany

    3 publications · 151 citations

    3

    h-index

1–5 of 427

Venues

  1. Journal of Neuroscience

    12 publications · 1.6K citations

    12

    h-index

  2. Cognitive Affective & Behavioral Neuroscience

    3 publications · 149 citations

    3

    h-index

  3. Learning & Memory

    3 publications · 197 citations

    3

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 45 citations

    3

    h-index

  5. eLife

    3 publications · 260 citations

    3

    h-index

1–5