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To solve a new problem, people spontaneously engage multiple cognitive processes. Previous work has identified a diverse set of oscillatory components critical at different stages of creative problem solving. In this project, we use hidden state modeling to untangle the roles of oscillation processes over time as people solve puzzles. Building on earlier work, we further developed analytical methods, such as incorporating source separating techniques and identifying the optimal number of states using cross-validation. We extracted brain states characterized by spatio-spectral topographies from time-resolved EEG spectral powers. The data driven approach allowed us to infer the dynamic, trial-by-trial, state sequences, and provided a comprehensive depiction of how various oscillation components interact recurrently throughout the trial. The properties of the states suggest their dissociable cognitive functions. For example, we identified three states with dominant activation in alpha bands but having distinct spatial distributions. People were differentially engaged in these states depending on the stages (e.g., onset or response) and outcomes of the trials (solved with insight or analysis). The current approach, applicable to many tasks requiring extended trial duration, can potentially reconcile findings from previous EEG studies and drive new hypotheses to further our understanding of the complex creative process. [ABSTRACT FROM AUTHOR]

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]

Abstract: Much work has investigated the neural representation of specific categories of knowledge, but relatively scant attention has been paid in the cognitive neuroscience literature to the semantic processes that contribute to semantic memory. In this study, we monitored regional cortical activity with fMRI while healthy young adults evaluated visually displayed NATURAL KIND, ARTIFACT, and ABSTRACT nouns with two standard tasks: Typicality judgments and Pleasantness judgments. We observed a significant interaction effect between the category of knowledge and the type of judgment used to evaluate members of these semantic categories. Typicality judgments recruited greater temporal–occipital activation relative to Pleasantness judgments of the same category, and this was seen for comparisons of all three semantic categories. However, when contrasted with Typicality judgments, Pleasantness judgments activated a different anatomic distribution for each semantic category. These findings are consistent with a dynamic approach to semantic memory that includes at least two components: semantic knowledge and semantic processes that interpret this knowledge in several ways depending on the particular semantic challenge. [Copyright &y& Elsevier]

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]

Objective: Electroencephalographic (EEG) peak alpha frequency (PAF) (measured in Hz) has been correlated to cognitive performance between healthy and clinical individuals, and among healthy individuals. PAF also varies within individuals across developmental stages, among different cognitive tasks, and among physiological states induced by administration of various substances. The present study suggests that, among other things, PAF reflects a trait or state of cognitive preparedness.Methods: Experiment 1 involved 19-channel EEG recordings from 10 individuals with traumatic brain injury (TBI) and 12 healthy matched controls, before, during, and after tasks of visual and auditory attention. Experiment 2 involved EEG recordings from 19 healthy young adults before and after a working memory task (WAIS-R Digit Span), repeated on 2 different days to measure within-individual differences.Results: Experiment 1 showed significantly lower PAF in individuals with TBI, mostly during post-task rest. Experiment 2 showed PAF during pre-task baseline to be significantly correlated with Digit Span performance of the same day but not with Digit Span performance of another day. Moreover, PAF was significantly increased after Digit Span for those participants whose PAF was lower than the sample median before the task, but not for those who had it higher. Finally, both PAF and Digit Span performance were increased during the second day.Conclusions: PAF was shown to detect both trait and state differences in cognitive preparedness, as well as to be affected by cognitive tasks. Traits are better reflected during post-task rest, whereas states are better reflected during initial resting baseline recordings. [Copyright &y& Elsevier]