Work-physiological synchronization as a determinant of performance in repetitive computer work

Social-physiological compliance as a determinant of team performance

A cybernetic model of behavior predicts that team performance may depend on physiological compliance among participants. This laboratory study tested if compliance in electrodermal activity (EDA), heart rate or breathing in two-person teams (N=16) was predictive of team performance or coordination in a continuous tracking task simulating teleoperation. Visual contact among participants was manipulated. Physiological compliance was scored with weighted coherence and cross correlation. Separate multiple regression analyses revealed that the task completion time was predicted by coherence measures for EDA and heart, but only at a trend level for breathing. Task completion time was also predicted by heart cross correlation. Team tracking error was predicted by coherence measures for EDA, heart and breathing, and also heart cross correlation. While social-visual contact did not have an impact, physiological compliance was predictive of improved performance, with coherence robust over all three physiological measures. Heart cross correlation showed the strongest predictive relationships. These results provide evidence that physiological compliance among team members may benefit team performance. While further study is needed, physiological compliance may someday provide a needed tool for the study of team work, and an objective means to guide the ergonomic design of complex sociotechnical systems requiring a high degree of team proficiency.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.