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Different slopes for different folks: Alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks
Author(s) -
Mathewson Kyle E.,
Basak Chandramallika,
Maclin Edward L.,
Low Kathy A.,
Boot Walter R.,
Kramer Arthur F.,
Fabiani Monica,
Gratton Gabriele
Publication year - 2012
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/j.1469-8986.2012.01474.x
Subject(s) - psychology , electroencephalography , alpha (finance) , task (project management) , transfer of learning , cognition , variance (accounting) , cognitive psychology , control (management) , analysis of variance , developmental psychology , audiology , pace , statistics , neuroscience , artificial intelligence , computer science , psychometrics , mathematics , medicine , construct validity , business , management , accounting , economics , geodesy , geography
We hypothesized that control processes, as measured using electrophysiological ( EEG ) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event‐related spectral perturbations ( ERSPs ), and event‐related brain potentials during early training of the S pace F ortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs , but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%–20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes.