fNIRS, EEG, ECG, and GSR reveal an effect of complex, dynamically changing environments on cognitive load, affective state, and performance, but not physiological stress

Dybvik, Henrikke; Erichsen, Christian Kuster; Snider, Chris; Steinert, Martin · 2025 · Crossref

DOI: 10.3389/fnhum.2025.1459653

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Summary

This study investigates how complex, dynamically changing environments affect cognitive load, affective state, performance, and physiological stress. Motivated by the need to understand human operators in high-risk settings like aviation and maritime control rooms, the research addresses the gap in using ecologically valid tasks that do not require specialized domain knowledge. The authors utilized a modified Tetris game to simulate varying workloads, arguing that its dynamic nature better represents real-world operational demands than standardized tasks like the n-back test. The experimental design involved 30 participants who completed three Tetris conditions: Easy (constant low difficulty), Hard (constant high difficulty), and Ramp (difficulty increasing from low to high over four minutes). Participants simultaneously performed a secondary auditory reaction task. The study employed a multimodal measurement approach, collecting data via functional near-infrared spectroscopy (fNIRS), electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), performance metrics, and subjective self-reports. fNIRS and EEG data were analyzed in temporal blocks to assess cognitive load and mental fatigue, while ECG and EDA measured physiological stress responses. Results indicated that performance was significantly highest in the Easy condition, followed by Ramp and Hard. Cognitive load, measured by fNIRS activation in prefrontal regions, increased with workload but decreased after a certain threshold, suggesting mental fatigue or disengagement in high-demand scenarios. EEG data revealed increased Delta power, indicative of mental fatigue, particularly in constant workload conditions over time. Crucially, despite significant differences in cognitive load and performance, there were little to no between-condition differences in physiological stress markers derived from ECG and EDA. However, subjective reports showed that participants rated the Easy condition significantly higher in valence, enjoyment, and workload acceptability compared to the Hard condition. The findings suggest that while complex environments significantly impact cognitive load and affective states, they do not necessarily elicit distinct physiological stress responses as measured by autonomic nervous system indicators. The combination of undistinguishable physiological stress and varying affective states implies that participants experienced "eustress" (positive stress) in the Easy condition and "distress" (negative stress) in the Hard condition. This distinction highlights the importance of differentiating between cognitive workload and physiological stress in human-machine interaction design, suggesting that adaptive systems should monitor cognitive and affective states rather than relying solely on physiological stress markers to assess operator status.

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Topics

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• neuro workload indices
• workload measurement
• mental demand

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• Empirical Findings: physiological data, self report data