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Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics., (© 2024 Massachusetts Institute of Technology.)

Insight occurs when a person suddenly reinterprets a stimulus, situation, or event to produce a nonobvious, nondominant interpretation. This can take the form of a solution to a problem (an "aha moment"), comprehension of a joke or metaphor, or recognition of an ambiguous percept. Insight research began a century ago, but neuroimaging and electrophysiological techniques have been applied to its study only during the past decade. Recent work has revealed insight-related coarse semantic coding in the right hemisphere and internally focused attention preceding and during problem solving. Individual differences in the tendency to solve problems insightfully rather than in a deliberate, analytic fashion are associated with different patterns of resting-state brain activity. Recent studies have begun to apply direct brain stimulation to facilitate insight. In sum, the cognitive neuroscience of insight is an exciting new area of research with connections to fundamental neurocognitive processes.

Semantic richness refers to the amount of semantic information associated with a concept. Reaction-time (RT) studies have shown that words referring to rich concepts elicit faster responses than those referring to impoverished ones, suggesting that richer concepts are activated more quickly. In a recent functional neuroimaging study, richer concepts evoked less neural activity, which was interpreted as faster activation. The interpretations of these findings appear to conflict with event-related potential (ERP) studies showing no evidence that speed of concept activation is influenced by typical semantic variables. Resolution of this apparent contradiction is important because the interpretation of 40 years of semantic-memory RT studies depends on whether factors such as semantic richness influence the duration of initial concept activation or later decision and response processes. Consistent with previous studies of the effects of semantic factors on ERP, the present study shows that richness influences the magnitude, but not the latency, of the P2 and N400 ERP components (which are early relative to behavioral responses), suggesting that effects of richness on RT reflect temporal effects on downstream decision or response mechanisms rather than on upstream concept activation.

After a person has become stuck on a problem, they sometimes achieve a clear and sudden solution through insight--the so-called Aha! experience. Because of its distinctive experience, the origins and characteristics of insight have received considerable attention historically in psychological research. However, despite considerable progress in characterizing insight, the underlying mechanisms remain mysterious. We argue that research on insight could be greatly advanced by supplementing traditional insight research, which depends on a few complex problems, with paradigms common in other domains of cognitive science. We describe a large set of mini-insight problems to which multiple methods can be applied, together with subjective reports to identify insight problem-solving. Behavioral priming and neuroimaging methods are providing evidence about what, where, and how neural activity occurs during insight. Such evidence constrains theories of component processes, and will help to demystify insight.

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.

Behavioral and neuroimaging findings indicate that distinct cognitive and neural processes underlie solving problems with sudden insight. Moreover, people with less focused attention sometimes perform better on tests of insight and creative problem solving. However, it remains unclear whether different states of attention, within individuals, influence the likelihood of solving problems with insight or with analysis. In this experiment, participants (N = 40) performed a baseline block of verbal problems, then performed one of two visual tasks, each emphasizing a distinct aspect of visual attention, followed by a second block of verbal problems to assess change in performance. After participants engaged in a center-focused flanker task requiring relatively focused visual attention, they reported solving more verbal problems with analytic processing. In contrast, after participants engaged in a rapid object identification task requiring attention to broad space and weak associations, they reported solving more verbal problems with insight. These results suggest that general attention mechanisms influence both visual attention task performance and verbal problem solving. [ABSTRACT FROM AUTHOR]

People sometimes solve problems with a unique process called insight, accompanied by an ''Aha!'' experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1) revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2) revealed a sudden burst of high-frequency (gamma-band) neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them. [ABSTRACT FROM AUTHOR]