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Statistical context dictates the relationship between feedback-related EEG signals and learning

View ORCID ProfileMatthew R. Nassar, View ORCID ProfileRasmus Bruckner, View ORCID ProfileMichael J. Frank
doi: https://doi.org/10.1101/581744
Matthew R. Nassar
1Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, Providence RI 02912-1821, USA
2Department of Neuroscience, Brown University, Providence RI 02912-1821, USA
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Rasmus Bruckner
4Department of Education and Psychology, Freie Universität Berlin, 14195 Berlin, Germany
5Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
6International Max Planck Research School on the Life Course (LIFE), Berlin, Germany
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Michael J. Frank
1Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, Providence RI 02912-1821, USA
3Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence RI 02912-1821, USA
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Abstract

Successful decision-making requires learning expectations based on experienced outcomes. This learning should be calibrated according to the surprise associated with an outcome, but also to the statistical context dictating the most likely source of surprise. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily, demanding increased learning. In contrast, when signal corruption is expected to occur occasionally, surprising outcomes can suggest a corrupt signal that should be ignored by learning systems. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the updating P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but predicted less learning in a context where surprise was indicative of a one-off outlier (oddball). The conditional predictive relationship between the P300 and learning behavior was persistent even after adjusting for known sources of learning rate variability. Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted March 18, 2019.
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Statistical context dictates the relationship between feedback-related EEG signals and learning
Matthew R. Nassar, Rasmus Bruckner, Michael J. Frank
bioRxiv 581744; doi: https://doi.org/10.1101/581744
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Statistical context dictates the relationship between feedback-related EEG signals and learning
Matthew R. Nassar, Rasmus Bruckner, Michael J. Frank
bioRxiv 581744; doi: https://doi.org/10.1101/581744

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