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  1. When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition.Christian P. Janssen & Wayne D. Gray - 2012 - Cognitive Science 36 (2):333-358.
    Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, (...)
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  • The Bright and Dark Sides of Performance‐Dependent Monetary Rewards: Evidence From Visual Perception Tasks.Nan Qin, Jingming Xue, Chuansheng Chen & Mingxia Zhang - 2020 - Cognitive Science 44 (3):e12825.
    Studies have shown that performance‐dependent monetary rewards facilitate visual perception. However, no study has examined whether such a positive effect is limited to the rewarded task or may be generalized to other tasks. In the current study, two groups of people were asked to perform two visual perception tasks, one being a reward‐relevant task and the other being a reward‐irrelevant task. For the reward‐relevant task, the experimental group received performance‐dependent monetary rewards, whereas the control group did not. For the reward‐irrelevant (...)
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