What Does Temporal Brain Signal Complexity Reveal About Verbal Creativity?

Abstract

Recent empirical evidence reveals that creative idea generation builds upon an interplay of multiple neural networks. Measures of temporal complexity yield important information about the underlying mechanisms of these co-activated neural networks. A few neurophysiological studies investigated brain signal complexity (BSC) during the production of creative verbal associations and resting states, aiming to relate it with creative task performance. However, it is unknown whether the complexity of brain signals can distinguish between productions of typical and original verbal associations. In the present study, we investigated verbal creativity with multiscale entropy (MSE) of electroencephalography (EEG) signals, which quantifies complexity over multiple timescales, capturing unique dynamic features of neural networks. MSE was measured in verbal divergent thinking (DT) states while emphasizing on producing either typical verbal associations or original verbal associations. We hypothesized that MSE differentiates between brain states characterizing the production of typical and original associations and is a sensitive neural marker of individual differences in producing original associations. Results from a sample of N = 92 young adults revealed slightly higher average MSE for original as compared with typical association production in small and medium timescales at frontal electrodes and slightly higher average MSE for typical association production in higher timescales at parietal electrodes. However, measurement models failed to uncover specificity of individual differences as MSE in typical vs. original associations was perfectly correlated. Hence, individuals with higher MSE in original association condition also exhibit higher MSE during the production of typical associations. The difference between typical and original association MSE was not significantly associated with human-rated originality of the verbal associations. In sum, we conclude that MSE is a potential marker of creative verbal association states, but replications and extensions are needed, especially with respect to the brain-behavior relationships.

Exploring Neural Signal Complexity as a Potential Link between Creative Thinking, Intelligence, and Cognitive Control

“…Importantly for the aims of the present study, Kaur et al (2020) showed that BSC, as indexed by MSE, was higher in those neural states in which individuals were asked to produce unusual, creative (as opposed to usual), fluent verbal associations. This was the first evidence to prove BSC as a neural state marker of creative verbal associations.…”

“…Generally, in line with Benedek et al (2014), we expect that inhibition would partly account for the creative thinking-intelligence relationship at the behavioral level. Furthermore, a previous study (Kaur et al 2020) has shown that the MSE in different task conditions of verbal creativity (i.e., during productions of original vs. typical associations in a verbal DT task) were different on average but were highly correlated. However, different task types (e.g., creative thinking vs. inhibition) involve different mental operations, and signal complexity during such task states is expected to show weaker relationships.…”

“…To assess creative thinking, we used the original verb generation task (adapted from Prabhakaran et al 2014) which was modified by translating the stimulus material into the German language (for the task paradigm and further details, see Kaur et al 2020). The task required the participants to produce a verb that is semantically related to the presented noun stimulus.…”

“…The temporal complexity measures of brain signals yield explicative information about the functional activity of neural networks during resting and task-processing states. Studies have demonstrated brain signal complexity (BSC) to be a sensitive neural marker of creative thinking (Kaur et al 2020;Ueno et al 2015), healthy brain functioning, knowledge representation (Heisz et al 2012), cognitive control (Grundy et al 2019;Wang et al 2020), and cognitive performance in general (for a review, see Garrett et al 2013). Nevertheless, it remains to be explored how BSC measures that were captured during different tasks are associated with each other, especially with respect to the creative thinking and inhibitionrelated neural states.…”

Color Texture Image Complexity—EEG-Sensed Human Brain Perception vs. Computed Measures

“…Therefore, we leave open the quest to further explore the human brain, directly tapping the corresponding relations between imagination, memory, emotions, and user specific understanding of complexity based on own background, the stock of knowledge, and previous experiences [81], which influence the reasoning, perception, and understanding of complexity, and further integrate the contributing top-down mechanisms in a vast model of complexity assessment. In terms of image complexity measures, since color entropy and color fractal dimension are not the only measures for describing texture complexity [82][83][84][85][86], measures, like lacunarity (which measures the gaps distributions [87]), succolarity (which expresses the degree of filaments that allow percolation or flow through the texture [88,89]), and artistic complexity derived from neuroscience [84] could be further used for comparison.…”