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EEG activity predictive of learning through feedback

Matthew Danyluik
University of Alberta, Canada
Sucheta Chakravarty
University of Alberta ~ Psychology
Jeremy B. Caplan
University of Alberta, Canada ~ Psychology

Much of human learning is incremental through feedback. Brain activity that tracks such learning at the item level could inform our understanding of basic neural processes and guide memory training protocols. We sought to identify learning-relevant spectral EEG features in a task where 45 participants learned stimulus-response mappings for each of 48 words through trial-and-error learning with feedback. Frontal midline theta activity, an established univariate marker of feedback processing, was not predictive of subsequent item-specific knowledge. However, multivariate classifiers (LDA, SVM) incorporating a broad range of frequencies and electrodes predicted whether an item was learned to a substantial degree (AUC ~0.7). Interestingly, classifiers only succeeded when classifying correct but not error trials. These findings validate the classifier approach to tracking feedback-guided learning following positive outcomes, and suggest that highly replicated univariate EEG features are not as relevant for learning as multivariate activity.


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Cite this as:

Danyluik, M., Chakravarty, S., & Caplan, J. (2021, November). EEG activity predictive of learning through feedback. Paper presented at MathPsych at Virtual Psychonomics 2021. Via