Literature DB >> 32550774

Towards Automaticity in Reinforcement Learning: A Model-Based Functional Magnetic Resonance Imaging Study.

Burak Erdeniz1, John Done2.   

Abstract

INTRODUCTION: Previous studies showed that over the course of learning many neurons in the medial prefrontal cortex adapt their firing rate towards the options with highest predicted value reward but it was showed that during later learning trials the brain switches to a more automatic processing mode governed by the basal ganglia. Based on this evidence, we hypothesized that during the early learning trials the predicted values of chosen options will be coded by a goal directed system in the medial frontal cortex but during the late trials the predicted values will be coded by the habitual learning system in the dorsal striatum.
METHODS: In this study, using a 3 Tesla functional magnetic resonance imaging scanner (fMRI), blood oxygen level dependent signal (BOLD) data was collected whilst participants (N=12) performed a reinforcement learning task. The task consisted of instrumental conditioning trials wherein each trial a participant choose one of the two available options in order to win or avoid losing money. In addition to that, depending on the experimental condition, participants received either monetary reward (gain money), monetary penalty (lose money) or neural outcome.
RESULTS: Using model-based analysis for functional magnetic resonance imaging (fMRI) event related designs; region of interest (ROI) analysis was performed to nucleus accumbens, medial frontal cortex, caudate nucleus, putamen and globus pallidus internal and external segments. In order to compare the difference in brain activity for early (goal directed) versus late learning (habitual, automatic) trials, separate ROI analyses were performed for each anatomical sub-region. For the reward condition, we found significant activity in the medial frontal cortex (p<0.05) only for early learning trials but activity is shifted to bilateral putamen (p<0.05) during later trials. However, for the loss condition no significant activity was found for early trials except globus pallidus internal segment showed a significant activity (p<0.05) for later trials.
CONCLUSION: We found that during reinforcement learning activation in the brain shifted from the medial frontal regions to dorsal regions of the striatum. These findings suggest that there are two separable (early goal directed and late habitual) learning systems in the brain. Copyright:
© 2020 Turkish Neuropsychiatric Society.

Entities:  

Keywords:  Predicted value; medial frontal cortex; prediction error; reinforcement learning; striatum

Year:  2020        PMID: 32550774      PMCID: PMC7285637          DOI: 10.29399/npa.24772

Source DB:  PubMed          Journal:  Noro Psikiyatr Ars        ISSN: 1300-0667            Impact factor:   1.339


  31 in total

1.  What's the price of a research subject? Approaches to payment for research participation.

Authors:  N Dickert; C Grady
Journal:  N Engl J Med       Date:  1999-07-15       Impact factor: 91.245

2.  Influence of expectation of different rewards on behavior-related neuronal activity in the striatum.

Authors:  O K Hassani; H C Cromwell; W Schultz
Journal:  J Neurophysiol       Date:  2001-06       Impact factor: 2.714

3.  Temporal difference models and reward-related learning in the human brain.

Authors:  John P O'Doherty; Peter Dayan; Karl Friston; Hugo Critchley; Raymond J Dolan
Journal:  Neuron       Date:  2003-04-24       Impact factor: 17.173

Review 4.  Contributions of the ventromedial prefrontal cortex to goal-directed action selection.

Authors:  John P O'Doherty
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5.  Reinforcement learning: computing the temporal difference of values via distinct corticostriatal pathways.

Authors:  Kenji Morita; Mieko Morishima; Katsuyuki Sakai; Yasuo Kawaguchi
Journal:  Trends Neurosci       Date:  2012-05-30       Impact factor: 13.837

6.  The role of the dorsomedial striatum in instrumental conditioning.

Authors:  Henry H Yin; Sean B Ostlund; Barbara J Knowlton; Bernard W Balleine
Journal:  Eur J Neurosci       Date:  2005-07       Impact factor: 3.386

7.  Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning.

Authors:  Masahiko Haruno; Mitsuo Kawato
Journal:  Neural Netw       Date:  2006-09-20

Review 8.  Changes in behavior-related neuronal activity in the striatum during learning.

Authors:  Wolfram Schultz; Léon Tremblay; Jeffrey R Hollerman
Journal:  Trends Neurosci       Date:  2003-06       Impact factor: 13.837

9.  Neuronal encoding of reward value and direction of actions in the primate putamen.

Authors:  Yukiko Hori; Takafumi Minamimoto; Minoru Kimura
Journal:  J Neurophysiol       Date:  2009-10-07       Impact factor: 2.714

10.  A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes.

Authors:  Burak Erdeniz; Tim Rohe; John Done; Rachael D Seidler
Journal:  Front Neurosci       Date:  2013-07-19       Impact factor: 4.677

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