Resting Galvanic Skin Response Reflects Fluctuations in Creativity Potential for Solving Creativity Tasks
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Creative performance fluctuates from moment to moment, suggesting that it depends partly on transient internal states present before creative thinking begins. Although such fluctuations have been identified in central neural activity, it remains unclear whether they are also reflected in autonomic physiology. We examined whether cardiac and electrodermal activity during a brief pre-trial resting period was associated with subsequent creative performance. Photoplethysmography, electrocardiography, and electrodermal activity were recorded while 28 participants completed the Alternative Uses Test and Fusion Innovation Test, assessing divergent and convergent creative thinking, respectively. Data from 27 participants was included in the analyses. Heart rate, heart rate variability, tonic skin conductance level, and phasic skin conductance activity were extracted from observation windows ranging from 11 to 30 s within a 30-s pre-trial rest period. Trial-level creativity was evaluated using GPT-based ratings of novelty, feasibility, and goal attainment. Linear mixed-effects models showed that higher pre-trial tonic skin conductance level was consistently associated with better subsequent creative performance across tasks, with overall model fit peaking at a 21-s observation window. Permutation-based feature-importance analysis provided convergent support for the contribution of tonic skin conductance, whereas the cardiac and phasic electrodermal indicators showed no reliable independent associations. However, the model explained only a small proportion of behavioral variance. These findings suggest that tonic sympathetic arousal reflects a momentary physiological state associated with creativity potential, while autonomic signals alone remain insufficient for accurate individual-level prediction.
AI Use
In the current study, generative artificial intelligence (AI) tools were utilized to assist with code development during data analysis, reference organization, and proofreading during manuscript preparation. The core experimental design, analytical methodologies, and primary drafting of the manuscript were performed entirely by the authors. All AI-generated code and text edits were thoroughly reviewed and verified by the authors to ensure that the analysis strictly adhered to the designated pipeline, parameter selections were theoretically sound and properly referenced, and the final text accurately reflected the original conceptual intent.