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Articles, Behavioral/Cognitive

fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning

Michael J. Frank, Chris Gagne, Erika Nyhus, Sean Masters, Thomas V. Wiecki, James F. Cavanagh and David Badre
Journal of Neuroscience 14 January 2015, 35 (2) 485-494; DOI: https://doi.org/10.1523/JNEUROSCI.2036-14.2015
Michael J. Frank
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
2Brown Institute for Brain Science, Providence, Rhode Island 09212,
3Department of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island 02912,
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Chris Gagne
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
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Erika Nyhus
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
4Department of Psychology and Program in Neuroscience, Bowdoin College, Brunswick, Maine 04011, and
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Sean Masters
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
2Brown Institute for Brain Science, Providence, Rhode Island 09212,
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Thomas V. Wiecki
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
2Brown Institute for Brain Science, Providence, Rhode Island 09212,
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James F. Cavanagh
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
5Department of Psychology, University of New Mexico, Albuquerque, New Mexico 87131
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David Badre
1Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island 02912,
2Brown Institute for Brain Science, Providence, Rhode Island 09212,
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Abstract

What are the neural dynamics of choice processes during reinforcement learning? Two largely separate literatures have examined dynamics of reinforcement learning (RL) as a function of experience but assuming a static choice process, or conversely, the dynamics of choice processes in decision making but based on static decision values. Here we show that human choice processes during RL are well described by a drift diffusion model (DDM) of decision making in which the learned trial-by-trial reward values are sequentially sampled, with a choice made when the value signal crosses a decision threshold. Moreover, simultaneous fMRI and EEG recordings revealed that this decision threshold is not fixed across trials but varies as a function of activity in the subthalamic nucleus (STN) and is further modulated by trial-by-trial measures of decision conflict and activity in the dorsomedial frontal cortex (pre-SMA BOLD and mediofrontal theta in EEG). These findings provide converging multimodal evidence for a model in which decision threshold in reward-based tasks is adjusted as a function of communication from pre-SMA to STN when choices differ subtly in reward values, allowing more time to choose the statistically more rewarding option.

  • basal ganglia
  • decision making
  • drift diffusion model
  • prefrontal cortex
  • subthalamic nucleus
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The Journal of Neuroscience: 35 (2)
Journal of Neuroscience
Vol. 35, Issue 2
14 Jan 2015
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fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning
Michael J. Frank, Chris Gagne, Erika Nyhus, Sean Masters, Thomas V. Wiecki, James F. Cavanagh, David Badre
Journal of Neuroscience 14 January 2015, 35 (2) 485-494; DOI: 10.1523/JNEUROSCI.2036-14.2015

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fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning
Michael J. Frank, Chris Gagne, Erika Nyhus, Sean Masters, Thomas V. Wiecki, James F. Cavanagh, David Badre
Journal of Neuroscience 14 January 2015, 35 (2) 485-494; DOI: 10.1523/JNEUROSCI.2036-14.2015
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Keywords

  • basal ganglia
  • decision making
  • drift diffusion model
  • prefrontal cortex
  • subthalamic nucleus

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