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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 (4–7 Hz), alpha (8–9 Hz and 10–13 Hz), and gamma (25–50 Hz) 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 " alpha-state " 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 potentially provide a novel window into the nature of the underlying cognitive processes. [ABSTRACT FROM AUTHOR]

Current creativity research reveals a fundamental disagreement about the nature of creative thought, specifically, whether it is primarily based on automatic, associative (Type 1) or executive, controlled (Type 2) cognitive processes. We propose that Type 1 and Type 2 processes make differential contributions to creative production depending on domain expertise and situational factors such as task instructions. We tested this hypothesis with jazz pianists who were instructed to improvise to a novel chord sequence and rhythm accompaniment. Afterward, they were asked to perform again under instructions to be especially creative which, via goal activation, is thought to prompt the musicians to engage Type 2 processes. Jazz experts rated all performances. Overall, performances by more experienced pianists were rated as superior. Moreover, creativity instructions resulted in higher ratings. However, there was an interaction between instructions and expertise, revealing that explicit creativity instructions significantly improved improvisation ratings only for the less experienced musicians. We propose that activating or reconfiguring executive Type 2 processes facilitates creativity for less experienced musicians, but does not improve creative performance significantly for more experienced ones because the latter have largely automatized the processes responsible for high-level improvisation or because they have achieved a near-optimal balance between associative Type 1 and executive Type 2 processes. Thus, increasing controlled Type 2 processing is unlikely to help, and may sometimes even diminish, the creativity of experts' performances. [ABSTRACT FROM AUTHOR]