Learning in Visual Regions as Support for the Bias in Future Value-Driven Choice

Sara Jahfari*, Jan Theeuwes, Tomas Knapen

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Reinforcement learning can bias decision-making toward the option with the highest expected outcome. Cognitive learning theories associate this bias with the constant tracking of stimulus values and the evaluation of choice outcomes in the striatum and prefrontal cortex. Decisions however first require processing of sensory input, and to date, we know far less about the interplay between learning and perception. This functional magnetic resonance imaging study (N = 43) relates visual blood oxygen level-dependent (BOLD) responses to value beliefs during choice and signed prediction errors after outcomes. To understand these relationships, which co-occurred in the striatum, we sought relevance by evaluating the prediction of future value-based decisions in a separate transfer phase where learning was already established. We decoded choice outcomes with a 70% accuracy with a supervised machine learning algorithm that was given trial-by-Trial BOLD from visual regions alongside more traditional motor, prefrontal, and striatal regions. Importantly, this decoding of future value-driven choice outcomes again highlighted an important role for visual activity. These results raise the intriguing possibility that the tracking of value in visual cortex is supportive for the striatal bias toward the more valued option in future choice.

Original languageEnglish
Pages (from-to)2005-2018
Number of pages14
JournalCerebral Cortex
Issue number4
Early online date11 Nov 2019
Publication statusPublished - 14 Apr 2020


This project was funded by an ABC Talent Grant from the University of Amsterdam to S.J., an ERC grant to J.T. (ERC-2012-AdG-323413), and a NWO-CAS grant to T.K. (012.200.012).

FundersFunder number
Seventh Framework Programme833029, 323413
African Bird Club
Universiteit van AmsterdamERC-2012-AdG-323413


    • Bayesian hierarchical modeling
    • decoding
    • perceptual learning
    • random forest machine learning
    • reinforcement learning


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