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• W4285586989 abstract "Abstract Reinforcement learning research in humans and other species indicates that rewards are represented in a context-dependent manner. More specifically, reward representations seem to be normalized as a function of the value of the alternative options. The dominant view postulates that value context-dependence is achieved via a divisive normalization rule, inspired by perceptual decision-making research. However, behavioral and neural evidence points to another plausible mechanism: range normalization. Critically, previous experimental designs were ill-suited to disentangle the divisive and the range normalization accounts, which generate similar behavioral predictions in many circumstances. To address this question, we designed a new learning task where we manipulated, across learning contexts, the number of options and the value ranges. Behavioral and computational analyses falsify the divisive normalization account and rather provide support for the range normalization rule. Together, these results shed new light on the computational mechanisms underlying context-dependence in learning and decision-making." @default.
• W4285586989 created "2022-07-16" @default.
• W4285586989 creator A5012466363 @default.
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• W4285586989 date "2022-07-16" @default.
• W4285586989 modified "2023-09-25" @default.
• W4285586989 title "The functional form of value normalization in human reinforcement learning" @default.
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• W4285586989 doi "https://doi.org/10.1101/2022.07.14.500032" @default.
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