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Published July 2009 | Published
Journal Article Open

Posterior Beta and Anterior Gamma Oscillations Predict Cognitive Insight

Abstract

Pioneering neuroimaging studies on insight have revealed neural correlates of the emotional "Aha!" component of the insight process, but neural substrates of the cognitive component, such as problem restructuring (a key to transformative reasoning), remain a mystery. Here, multivariate electroencephalogram signals were recorded from human participants while they solved verbal puzzles that could create a small-scale experience of cognitive insight. Individuals responded as soon as they reached a solution and provided a rating of subjective insight. For unsolved puzzles, hints were provided after 60 to 90 sec. Spatio-temporal signatures of brain oscillations were analyzed using Morlet wavelet transform followed by exploratory parallel-factor analysis. A consistent reduction in beta power (15–25 Hz) was found over the parieto-occipital and centro-temporal electrode regions on all four conditions—(a) correct (vs. incorrect) solutions, (b) solutions without (vs. with) external hint, (c) successful (vs. unsuccessful) utilization of the external hint, and d) self-reported high (vs. low) insight. Gamma band (30–70 Hz) power was increased in right fronto-central and frontal electrode regions for conditions (a) and (c). The effects occurred several (up to 8) seconds before the behavioral response. Our findings indicate that insight is represented by distinct spectral, spatial, and temporal patterns of neural activity related to presolution cognitive processes that are intrinsic to the problem itself but not exclusively to one's subjective assessment of insight.

Additional Information

© 2008 Massachusetts Institute of Technology. Posted Online April 24, 2009. The research was supported by a GEAR grant from the University of Houston (B. R. S.), Cline Discovery Grant—Caltech (B. R. S.), JST.ERATO Shimojo project ( J. B.), and ONBJubila umsfondprojekt ( J. B.). J. B. also thankfully acknowledges the support of the Sloan–Swartz Foundation. PARAFAC models and diagnostics were calculated using ''The N-way toolbox'' for MATLAB kindly provided by Claus Andersson and Rasmus Bro (www.models.kvl.dk/source/nwaytoolbox). We thank Harald Sto¨gbauer for the programs on artifact removal, Larry Varghese for the verbalized protocol analysis, and Vinu Ipe for help in finding appropriate puzzles.

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