Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks

Abstract

Design is a ubiquitous, complex, and open-ended creation behaviour that triggers creativity. The brain dynamics underlying design is unclear, since a design process consists of many basic cognitive behaviours, such as problem understanding, idea generation, idea analysis, idea evaluation, and idea evolution. In this present study, we simulated the design process in a loosely controlled setting, aiming to quantify the design-related cognitive workload and control, identify EEG-defined large-scale brain networks, and uncover their temporal dynamics. The effectiveness of this loosely controlled setting was tested through comparing the results with validated findings available in the literature. Task-related power (TRP) analysis of delta, theta, alpha and beta frequency bands revealed that idea generation was associated with the highest cognitive workload and lowest cognitive control, compared to other design activities in the experiment, including problem understanding, idea evaluation, and self-rating. EEG microstate analysis supported this finding as microstate class C, being negatively associated with the cognitive control network, was the most prevalent in idea generation. Furthermore, EEG microstate sequence analysis demonstrated that idea generation was consistently associated with the shortest temporal correlation times concerning finite entropy rate, autoinformation function, and Hurst exponent. This finding suggests that during idea generation the interplay of functional brain networks is less restricted and the brain has more degrees of freedom in choosing the next network configuration than during other design activities. Taken together, the TRP and EEG microstate results lead to the conclusion that idea generation is associated with the highest cognitive workload and lowest cognitive control during open-ended creation task.

Frequency analysis of EEG microstate sequences in wakefulness and NREM sleep

“…Furthermore, information-theoretical analysis of microstate sequences during a design task has shown that the entropy rate (von Wegner et al 2018) and oscillatory microstate dynamics are modulated by cognitive tasks, and that these metrics indicate the balance between cognitive workload and cognitive control (Jia et al 2021). Yet, we do not know how whether this interpretation is compatible with our understanding of reduced cognition during sleep.…”

“…This observation, together with the lower microstate entropy rate, speak for a more restricted, and therefore more predictable trajectory of the underlying neuronal ensembles in deeper sleep stages. During complex cognitive tasks, the largest microstate entropy rates have been observed in subtasks with the highest cognitive workload and the lowest degree of cognitive control(Jia et al 2021). The current results extend these observations while pointing in the same direction, as they suggest a further decrease in cognitive workload with deepening sleep.…”

Complexity measures for EEG microstate sequences - concepts and algorithms

“…Although the aim of the authors was to address self-similarity and fractality rather than explicitly measuring complexity, the relationship between Hurst exponents and complex systems properties is present throughout the article. We evaluated their approach in relation to Markov models of microstate sequences in (von Wegner et al, 2016) and applied it to cognitive load assessment in (Jia et al, 2021).…”

“…Entropy rate estimation for microstate sequence analysis was introduced in (von Wegner et al, 2018a), and we evaluated its changes during different types of cognitive effort in (Jia et al, 2021), and for NREM sleep stages in (Wiemers et al, 2022). We interpreted entropy rate in terms of sequence predictability in (von Wegner et al, 2018a) and (Jia et al, 2021), and as a complexity measure in (Wiemers et al, 2022).…”

Differences in engineers' brain activity when CAD modelling from isometric and orthographic projections

“…The EEG data processing was conducted in MATLAB ® using the EEGLAB toolbox 53 . An original script for data processing was developed according to the pipelines described by Li et al 54 , Vieira et al 28 , and Jia et al 55 . In the rst step, DC offset speci c for Emotiv EPOC + devices was removed with the in nite impulse response (IIR) lter.…”