Brain states predict individual differences in perceptual learning

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Authors
  1. Muller-Gass, A.
  2. Duncan, M.
  3. Campbell, K.
Corporate Authors
Defence Research and Development Canada, Toronto Research Centre , Toronto ON (CAN)
Abstract
Brain states dynamically change with learning and these changes vary widely among individuals. Recent research proposes that electrophysiological measures of brain states can also predict individual variability in successful learning. This study was conducted to examine neural mechanisms of learning and neurological indicators that predict success in a perceptual learning task. EEG was recorded over 20 blocks of trials while subjects learned to categorize a complex visual stimulus that required integration of multiple physical dimensions for successful categorization. For the analysis, final performance scores were used to median split subjects into high and low learners. By the 6th block, high learners began to diverge, eventually achieving 80% accuracy while low learners remained only nominally above chance. ERPs to the visual stimulus revealed a P3b that was significantly larger in high learners even before performance differences had emerged, but that did not vary with learning. Power spectral analyses showed that resting-state alpha was larger for high learners both before and during learning. Finally, alpha power increased for high but not for low learners as learning progressed. These results show that electrophysiological measures, especially alpha power, may not just reflect the learning process but also serve as predictors of eventual learning performance.
Keywords
EEG;ERP;Brain States;Perceptual Learning;Implicit Learning;Categorization;Individual Differences
Report Number
DRDC-RDDC-2017-P020 — External Literature
Date of publication
25 Oct 2017
Number of Pages
33
DSTKIM No
CA045223
CANDIS No
805616
Format(s):
Electronic Document(PDF)

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