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.

Resting-state fMRI studies have revealed that individuals exhibit stable, functionally meaningful divergences in large-scale network organization. The locations with strongest deviations (called network “variants”) have a characteristic spatial distribution, with qualitative evidence from prior reports suggesting that this distribution differs across hemispheres. Hemispheric asymmetries can inform us on constraints guiding the development of these idiosyncratic regions. Here, we used data from the Human Connectome Project to systematically investigate hemispheric differences in network variants. Variants were significantly larger in the right hemisphere, particularly along the frontal operculum and medial frontal cortex. Variants in the left hemisphere appeared most commonly around the TPJ. We investigated how variant asymmetries vary by functional network and how they compare with typical network distributions. For some networks, variants seemingly increase group-average network asymmetries (e.g., the group-average language network is slightly bigger in the left hemisphere and variants also appeared more frequently in that hemisphere). For other networks, variants counter the group-average network asymmetries (e.g., the default mode network is slightly bigger in the left hemisphere, but variants were more frequent in the right hemisphere). Intriguingly, left- and right-handers differed in their network variant asymmetries for the cingulo-opercular and frontoparietal networks, suggesting that variant asymmetries are connected to lateralized traits. These findings demonstrate that idiosyncratic aspects of brain organization differ systematically across the hemispheres. We discuss how these asymmetries in brain organization may inform us on developmental constraints of network variants and how they may relate to functions differentially linked to the two hemispheres.

There are now numerous observations of subtle right hemisphere (RH) contributions to language comprehension. It has been suggested that these contributions reflect coarse semantic coding in the RH. That is, the RH weakly activates large semantic fields—including concepts distantly related to the input word—whereas the left hemisphere (LH) strongly activates small semantic fields—limited to concepts closely related to the input (Beeman, 1993a,b). This makes the RH less effective at interpreting single words, but more sensitive to semantic overlap of multiple words. To test this theory, subjects read target words preceded by either “Summation” primes (three words each weakly related to the target) or Unrelated primes (three unrelated words), and target exposure duration was manipulated so that subjects correctly named about half the target words in each hemifield. In Experiment 1, subjects benefited more from Summation primes when naming target words presented to the left visual field-RH (Ivf-RH) than when naming target words presented to the right visual field-LH (rvf-LH), suggesting a RH advantage in coarse semantic coding. In Experiment 2, with a low proportion of related prime-target trials, subjects benefited more from “Direct” primes (one strong associate flanked by two unrelated words) than from Summation primes for rvf-LH target words, indicating that the LH activates closely related information much more strongly than distantly related information. Subjects benefited equally from both prime types for Ivf-RH target words, indicating that the RH activates closely related information only slightly more strongly, at best, than distantly related information. This suggests that the RH processes words with relatively coarser coding than the LH, a conclusion consistent with a recent suggestion that the RH coarsely codes visual input (Kosslyn, Chabris, Mar-solek, & Koenig, 1992).