Metrics for individual differences in EEG response to cognitive workload: Optimizing performance prediction. (1st November 2017)

• Record Type: Journal Article
• Authors: Matthews, Gerald; Reinerman-Jones, Lauren; Abich, Julian; Kustubayeva, Almira
• Is Part Of: Personality and individual differences. Volume 118(2017)
• Page Start: 22
• Page End: 28
• Publication Date: 2017-11-01
• Subjects: Personality -- Periodicals; Individuality -- Periodicals; Individuality -- Periodicals; Personality Development -- Periodicals; Personnalité -- Périodiques; Individualité -- Périodiques; 155.205
• DOI: 10.1016/j.paid.2017.03.002
• Languages: English
• ISSNs: 0191-8869

Abstract

Abstract: Robert Stelmack's distinguished contributions to differential psychology demonstrated the importance of appropriate choice of electroencephalographic (EEG) metrics, and of establishing the functional significance of EEG. This article reports a study of individual differences in EEG that developed these themes. 150 participants performed two signal detection tasks, requiring threat and change detection, in a complex, simulated operational environment. EEG was recorded together with subjective stress and additional psychophysiological workload measures. Results showed that five different absolute-level and reactivity EEG metrics differed in psychometric properties, including correlations with subjective stress and task performance. Prediction of performance was optimized with a multivariate model that included subjective stress, heart rate variability, and eye fixation duration, as well as baseline EEG measures. Findings point to the need for more sophisticated interpretation of EEG metrics in individual differences research. Highlights: Robert Stelmack's work highlights the importance of validating EEG metrics. EEG, workload, stress and performance data were collected in a UGV simulation. Alternate EEG metrics for workload response differed psychometrically. Integration of subjective and objective metrics optimizes performance prediction.