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2021 Brain Structure & Function
Reward and fictive prediction error signals in ventral striatum: asymmetry between factual and counterfactual processing.
Santo-Angles A, Fuentes-Claramonte P, Argila-Plaza I, Guardiola-Ripoll M, Almodóvar-Payá C, Munuera J, McKenna PJ, Pomarol-Clotet E, Radua J

Servei limitat a col·laboradors/res de la xarxa de centres de Germanes Hospitalàries. Rebreu un missatge al vostre correu-e amb un enllaç per a la descàrrega del present article.

Abstract

Reward prediction error, the difference between the expected and obtained reward, is known to act as a reinforcement learning neural signal. In the current study, we propose a model fitting approach that combines behavioral and neural data to fit computational models of reinforcement learning. Briefly, we penalized subject-specific fitted parameters that moved away too far from the group median, except when that deviation led to an improvement in the model's fit to neural responses. By means of a probabilistic monetary learning task and fMRI, we compared our approach with standard model fitting methods. Q-learning outperformed actor-critic at both behavioral and neural level, although the inclusion of neuroimaging data into model fitting improved the fit of actor-critic models. We observed both action-value and state-value prediction error signals in the striatum, while standard model fitting approaches failed to capture state-value signals. Finally, left ventral striatum correlated with reward prediction error while right ventral striatum with fictive prediction error, suggesting a functional hemispheric asymmetry regarding prediction-error driven learning.
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Darrera modificació: 26/11/2021
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