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Dynamics of hidden brain states when people solve verbal puzzles Open Access (recommended)

Descriptions

Resource type(s)
Article
Keyword
insight
creativity
problem-solving
hidden Markov model
EEG
dynamic modelling
Rights
Attribution-NonCommercial-NoDerivs 3.0 United States

Creator
Yu, Yuhua
Oh, Yongtaek
Kounios, John
Beeman, Mark
Abstract
When people try to solve a problem, they go through distinct steps (encoding, ideation, evaluation, etc.) recurrently and spontaneously. To disentangle different cognitive processes that unfold throughout a trial, we applied an unsupervised machine learning method to electroencephalogram (EEG) data continuously recorded while 39 participants attempted 153 Compound Remote Associates problems (CRA). CRA problems are verbal puzzles that can be solved in either insight-leaning or analysis-leaning manner. We fitted a Hidden Markov Model to the time-frequency transformed EEG signals and decoded each trial as a time-resolved state sequence. The model characterizes hidden brain states with spectrally resolved power topography. Seven states were identified with distinct activation patterns in the theta (4-7Hz), alpha (8-9 Hz and 10-13 Hz), and gamma (25Hz 50Hz) bands. Notably, a state featuring widespread activation only in alpha-band frequency emerged, from this data-driven approach, which exhibited dynamic characteristics associated with specific temporal stages and outcomes (whether solved with insight or analysis) of the trials. The state dynamics derived from the model overlap and extend previous literature on the cognitive function of alpha oscillation: the alphastate probability peaks before stimulus onset and decreases before response. In trials solved with insight, relative to solved with analysis, the alpha-state is more likely to be visited and maintained during preparation and solving periods, and its probability declines more sharply immediately preceding a response. This novel paradigm provides a way to extract dynamic features that characterize problem-solving stages and nature of the underlying cognitive processes.
Publisher
DigitalHub. Galter Health Sciences Library & Learning Center
Subject: MESH
Electroencephalography
Problem Solving
Cognitive Neuroscience
Brain--physiology
Grants and funding
This study was supported by NSF grant 1125596 to JK, and through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology.
DOI
10.18131/g3-kv5w-yx59

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