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.
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Original Article
Different slopes for different folks: Alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks
Article first published online: 23 OCT 2012
DOI: 10.1111/j.1469-8986.2012.01474.x
Copyright © 2012 Society for Psychophysiological Research
Additional Information
How to Cite
Mathewson, K. E., Basak, C., Maclin, E. L., Low, K. A., Boot, W. R., Kramer, A. F., Fabiani, M. and Gratton, G. (2012), Different slopes for different folks: Alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks. Psychophysiology, 49: 1558–1570. doi: 10.1111/j.1469-8986.2012.01474.x
Publication History
- Issue published online: 1 NOV 2012
- Article first published online: 23 OCT 2012
- Manuscript Accepted: 8 SEP 2012
- Manuscript Received: 6 JUL 2012
Funded by
- Office of Naval Research Multidisciplinary University Research Initiative (MURI). Grant Number: N00014-07-1-1913
- Natural Science and Engineering Research Council of Canada
- Beckman Institute
- Abstract
- Article
- References
- Cited By
Keywords:
- Video game training;
- Space Fortress;
- Electroencephalogram (EEG);
- Event-related spectral perturbations (ERSPs);
- Event-related brain potentials (ERPs);
- Skill learning;
- Cognitive control;
- Alpha rhythm
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.