Literature DB >> 23095124

Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

Kyle E Mathewson1, Chandramallika Basak, Edward L Maclin, Kathy A Low, Walter R Boot, Arthur F Kramer, Monica Fabiani, Gabriele Gratton.   

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.
Copyright © 2012 Society for Psychophysiological Research.

Mesh:

Year:  2012        PMID: 23095124     DOI: 10.1111/j.1469-8986.2012.01474.x

Source DB:  PubMed          Journal:  Psychophysiology        ISSN: 0048-5772            Impact factor:   4.016


  30 in total

1.  Brain network modularity predicts cognitive training-related gains in young adults.

Authors:  Pauline L Baniqued; Courtney L Gallen; Michael B Kranz; Arthur F Kramer; Mark D'Esposito
Journal:  Neuropsychologia       Date:  2019-05-25       Impact factor: 3.139

Review 2.  Brain Modularity: A Biomarker of Intervention-related Plasticity.

Authors:  Courtney L Gallen; Mark D'Esposito
Journal:  Trends Cogn Sci       Date:  2019-02-28       Impact factor: 20.229

Review 3.  Exercise and cognitive function in patients with end-stage kidney disease.

Authors:  Nadia M Chu; Mara A McAdams-DeMarco
Journal:  Semin Dial       Date:  2019-03-22       Impact factor: 3.455

4.  Performance prediction in a visuo-motor task: the contribution of EEG analysis.

Authors:  Fabrizio Vecchio; Francesca Alù; Alessandro Orticoni; Francesca Miraglia; Elda Judica; Maria Cotelli; Paolo Maria Rossini
Journal:  Cogn Neurodyn       Date:  2021-09-11       Impact factor: 5.082

5.  Leveraging technology to personalize cognitive enhancement methods in aging.

Authors:  David A Ziegler; Joaquin A Anguera; Courtney L Gallen; Wan-Yu Hsu; Peter E Wais; Adam Gazzaley
Journal:  Nat Aging       Date:  2022-06-17

6.  Resting-state cortical connectivity predicts motor skill acquisition.

Authors:  Jennifer Wu; Ramesh Srinivasan; Arshdeep Kaur; Steven C Cramer
Journal:  Neuroimage       Date:  2014-01-25       Impact factor: 6.556

7.  Interventions to Preserve Cognitive Functioning Among Older Kidney Transplant Recipients.

Authors:  Nadia M Chu; Dorry Segev; Mara A McAdams-DeMarco
Journal:  Curr Transplant Rep       Date:  2020-10-21

Review 8.  Brain enhancement through cognitive training: a new insight from brain connectome.

Authors:  Fumihiko Taya; Yu Sun; Fabio Babiloni; Nitish Thakor; Anastasios Bezerianos
Journal:  Front Syst Neurosci       Date:  2015-04-01

Review 9.  Cognitive enhancement through action video game training: great expectations require greater evidence.

Authors:  Joseph Bisoglio; Timothy I Michaels; Joshua E Mervis; Brandon K Ashinoff
Journal:  Front Psychol       Date:  2014-02-19

10.  Alterations in resting-state activity relate to performance in a verbal recognition task.

Authors:  Rocío A López Zunini; Jean-Philippe Thivierge; Shanna Kousaie; Christine Sheppard; Vanessa Taler
Journal:  PLoS One       Date:  2013-06-13       Impact factor: 3.240

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