Reward Probability

Reward Probability is a theoretical construct in reward processing that denotes the likelihood or chance of receiving a reward given a specific action or stimulus, distinguishing it from other reward-related parameters such as magnitude or delay by quantifying the probabilistic nature of reward occurrence.

156

Publications

15.2K

Citations

515

Authors

283

Institutions

Publications per year

2017–2026

51

Authors

515

  1. WS
    Wolfram Schultz

    University of Cambridge, United Kingdom

    5 publications · 2.4K citations

    5

    h-index

  2. PN
    Philippe N. Tobler

    University of Zurich, Switzerland

    4 publications · 2.3K citations

    4

    h-index

  3. JM
    Joel Myerson

    Washington University in St. Louis, United States

    4 publications · 563 citations

    4

    h-index

  4. AS
    Alireza Soltani

    Dartmouth College, United States

    4 publications · 98 citations

    4

    h-index

  5. LG
    Leonard Green

    Washington University in St. Louis, United States

    4 publications · 563 citations

    4

    h-index

1–5 of 515

Venues

  1. Journal of Neuroscience

    18 publications · 2.3K citations

    18

    h-index

  2. PLoS ONE

    9 publications · 227 citations

    9

    h-index

  3. Journal of the Experimental Analysis of Behavior

    5 publications · 1.5K citations

    5

    h-index

  4. eLife

    4 publications · 357 citations

    4

    h-index

  5. Frontiers in Behavioral Neuroscience

    4 publications · 199 citations

    4

    h-index

1–5