Emotional intelligence (EI) may predict stress responses and coping strategies in a variety of applied settings. This study compares EI and the personality factors of the Five Factor Model (FFM) as predictors of task-induced stress responses. Participants (N = 200) were randomly assigned to 1 of 4 task conditions, 3 of which were designed to be stressful. Results confirmed that low EI was related to worry states and avoidance coping, even with the FFM statistically controlled. However, EI was not specifically related to task-induced changes in stress state. Results also confirmed that Neuroticism related to distress, worry, and emotion-focused coping, and Conscientiousness predicted use of task-focused coping. The applied utility of EI and personality measures is discussed.

Research indicates that coactors performing cooperative tasks often exhibit spontaneous and unintended similarities in their physiological and behavioral responses--a phenomenon referred to here as physio-behavioral coupling (PBC). The purpose of this research was to identify contributors to PBC; examine relationships between PBC, team performance, and perceived team attributes (e.g., cohesion, trust); and compare a set of time-series measures(cross-correlation [CC], cross-recurrence quantification analysis [CRQA], and cross-fuzzy entropy [CFEn]) in their characterization of PBC across comparisons. To accomplish this, PBC was examined in human postural sway (PS) and cardiac interbeat intervals (IBIs) from dyadic teams performing a fast-paced puzzle task (Quadra--a variant of the video game Tetris). Results indicated that observed levels of PBC were not a chance occurrence, but instead driven by features of the team-task environment, and that PBC was likely influenced by similar individual task demands and interpersonal coordination dynamics that were not "unique" to a particular team. Correlation analysis revealed that PBC exhibited negative relationships with team performance and team attributes, which were interpreted to reflect complementary coordination (as opposed to mimicry) during task performance, potentially due to differentiated team roles. Finally, qualitative comparison of time-series measures used to characterize PBC indicated that CRQA percent recurrence and CFEn (both nonlinear measures) settled on mostly analogous characterizations, whereas linear CC did not. The disparity observed between the linear and nonlinear measures highlights underlying computational and interpretational differences between the two families of statistics and supports the use of multiple metrics for characterizing PBC.

Objective: The purpose of this article is to present and expand on current theories and measurement techniques for assessing team workload.Background: To date, little research has been conducted on the workload experienced by teams. A validated theory describing team workload, which includes an account of its relation to individual workload, has not been articulated.Method: The authors review several theoretical approaches to team workload. Within the team research literature, attempts to evaluate team workload have typically relied on measures of individual workload. This assumes that such measures retain their validity at the team level of measurement, but empirical research suggests that this method may lack sensitivity to the drivers of team workload.Results: On the basis of these reviews, the authors advance suggestions concerning a comprehensive theory of team workload and methods for assessing it in team settings. The approaches reviewed include subjective, performance, physiological, and strategy shift measures. Theoretical and statistical difficulties associated with aggregating individual-level workload responses to a team-level measure are discussed.Conclusion: Conception and measurement of team workload have not significantly matured alongside developments in individual workload.Application: Team workload remains a complex research area without simple measurement solutions, but as a research domain it remains open for contributions from interested and enterprising researchers.

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Though not often mentioned, the price point of many eye tracking systems may be a factor limiting their adoption in research. Recently, several inexpensive eye trackers have appeared on the market, but to date little systematic research has been conducted to validate these systems. The present experiment attempted to address this gap by evaluating and comparing five different eye trackers, the Eye Tribe Tracker, Tobii EyeX, Seeing Machines faceLAB, Smart Eye Pro, and Smart Eye Aurora for their gaze tracking accuracy and precision. Results suggest that all evaluated trackers maintained acceptable accuracy and precision, but lower cost systems frequently also experienced high rates of data loss, suggesting that researchers adopting low cost systems such as those evaluated here should be judicious in their research usage.