Cognitive Biotypes Identified Through ECG-Derived Workload and Behavioral Accuracy
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Individual differences in physiological effort during cognitive workload, which we define as mental demand during task execution, are well established, yet self-reports often fail to reflect actual physiological effort. We hypothesized that combining cognitive performance with ECG-derived workload would reveal distinct biotypes of performance and physiological effort and that these biotypes would differ in how closely subjective appraisals align with objective measures. A sample of 100 participants completed cognitive tasks while ECG data were analyzed in real time using a validated workload classification algorithm. Clustering based on standardized performance accuracy and workload revealed three biotypes: (1) high performers with low workload, (2) average-to-high performers with high workload, and (3) low performers with variable workload. These biotypes exhibited distinct patterns of perceptual bias: Clusters 1 and 3 showed smaller discrepancies between subjective and objective workload, while Cluster 1 notably underestimated task success relative to their actual performance. These findings demonstrate that clustering behavioral and physiological data can reveal meaningful cognitive stress response profiles and suggest that subjective-objective misalignment may serve as a potential marker of cognitive resilience or vulnerability. This taxonomy may aid future efforts to personalize assessments or interventions aimed at optimizing performance under stress.