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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

Authors

  • Kyle E. Mathewson,

    Corresponding author
    1. Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA
    • Department of Psychology, University of Illinois, Champaign, Illinois, USA
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  • Chandramallika Basak,

    1. School of Brain and Behavioral Sciences, University of Texas at Dallas, Richardson, Texas, USA
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  • Edward L. Maclin,

    1. Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA
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  • Kathy A. Low,

    1. Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA
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  • Walter R. Boot,

    1. Department of Psychology, Florida State University, Tallahassee, Florida, USA
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  • Arthur F. Kramer,

    1. Department of Psychology, University of Illinois, Champaign, Illinois, USA
    2. Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA
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  • Monica Fabiani,

    1. Department of Psychology, University of Illinois, Champaign, Illinois, USA
    2. Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA
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  • Gabriele Gratton

    1. Department of Psychology, University of Illinois, Champaign, Illinois, USA
    2. Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA
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  • This project was supported in part by Office of Naval Research Multidisciplinary University Research Initiative (MURI) grant (N00014-07-1-1913) to Arthur F. Kramer as well as fellowships from the Natural Science and Engineering Research Council of Canada and the Beckman Institute to Kyle E. Mathewson. We would like to thank Tanya Stanley for her help collecting the data and Lawrence Hubert for the title suggestion.

Address correspondence to: Kyle E. Mathewson, 5247 Beckman Institute, 405 N Mathews Ave., Urbana, IL 61801. E-mail: kylemath@gmail.com

Abstract

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 Space Fortress 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.

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