Literature DB >> 31711119

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

Sara Jahfari1,2, Jan Theeuwes3, Tomas Knapen1,3.   

Abstract

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.
© The Author(s) 2019. Published by Oxford University Press.

Entities:  

Keywords:  Bayesian hierarchical modeling; decoding; perceptual learning; random forest machine learning; reinforcement learning

Year:  2020        PMID: 31711119      PMCID: PMC7175016          DOI: 10.1093/cercor/bhz218

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  68 in total

1.  Dopamine-mediated reinforcement learning signals in the striatum and ventromedial prefrontal cortex underlie value-based choices.

Authors:  Gerhard Jocham; Tilmann A Klein; Markus Ullsperger
Journal:  J Neurosci       Date:  2011-02-02       Impact factor: 6.167

2.  Learned Value Shapes Responses to Objects in Frontal and Ventral Stream Networks in Macaque Monkeys.

Authors:  Peter M Kaskan; Vincent D Costa; Hana P Eaton; Julie A Zemskova; Andrew R Mitz; David A Leopold; Leslie G Ungerleider; Elisabeth A Murray
Journal:  Cereb Cortex       Date:  2017-05-01       Impact factor: 5.357

3.  The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention.

Authors:  Seung-Lark Lim; John P O'Doherty; Antonio Rangel
Journal:  J Neurosci       Date:  2011-09-14       Impact factor: 6.167

4.  Reinforcement learning in multidimensional environments relies on attention mechanisms.

Authors:  Yael Niv; Reka Daniel; Andra Geana; Samuel J Gershman; Yuan Chang Leong; Angela Radulescu; Robert C Wilson
Journal:  J Neurosci       Date:  2015-05-27       Impact factor: 6.167

Review 5.  Attention, reward, and information seeking.

Authors:  Jacqueline Gottlieb; Mary Hayhoe; Okihide Hikosaka; Antonio Rangel
Journal:  J Neurosci       Date:  2014-11-12       Impact factor: 6.167

6.  Cross-Task Contributions of Frontobasal Ganglia Circuitry in Response Inhibition and Conflict-Induced Slowing.

Authors:  Sara Jahfari; K Richard Ridderinkhof; Anne G E Collins; Tomas Knapen; Lourens J Waldorp; Michael J Frank
Journal:  Cereb Cortex       Date:  2019-05-01       Impact factor: 5.357

7.  Caudate encodes multiple computations for perceptual decisions.

Authors:  Long Ding; Joshua I Gold
Journal:  J Neurosci       Date:  2010-11-24       Impact factor: 6.167

8.  Value-based modulations in human visual cortex.

Authors:  John T Serences
Journal:  Neuron       Date:  2008-12-26       Impact factor: 17.173

9.  Reward speeds up and increases consistency of visual selective attention: a lifespan comparison.

Authors:  Viola Störmer; Ben Eppinger; Shu-Chen Li
Journal:  Cogn Affect Behav Neurosci       Date:  2014-06       Impact factor: 3.526

10.  Is there "one" DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation.

Authors:  Edna C Cieslik; Karl Zilles; Svenja Caspers; Christian Roski; Tanja S Kellermann; Oliver Jakobs; Robert Langner; Angela R Laird; Peter T Fox; Simon B Eickhoff
Journal:  Cereb Cortex       Date:  2012-08-23       Impact factor: 5.357

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  1 in total

1.  The postdictive effect of choice reflects the modulation of attention on choice.

Authors:  Mowei Shen; Yiling Zhou; Luo Chen; Jifan Zhou; Hui Chen
Journal:  J Vis       Date:  2020-12-02       Impact factor: 2.240

  1 in total

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