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Publication Detail
An MEG signature corresponding to an axiomatic model of reward prediction error.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Talmi D, Fuentemilla L, Litvak V, Duzel E, Dolan RJ
  • Publication date:
    02/01/2012
  • Pagination:
    635, 645
  • Journal:
    Neuroimage
  • Volume:
    59
  • Issue:
    1
  • Status:
    Published
  • Country:
    United States
  • PII:
    S1053-8119(11)00697-5
  • Language:
    eng
  • Keywords:
    Brain, Brain Mapping, Decision Making, Electroencephalography, Evoked Potentials, Female, Humans, Magnetoencephalography, Male, Reward, Signal Processing, Computer-Assisted, Young Adult
Abstract
Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data.
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Imaging Neuroscience
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Imaging Neuroscience
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