Emotion-performance relationship in safety-critical human-machine systems

Abstract

Affect-adaptive systems (AAS) monitor the emotional user state, analyze it concerning the current context and apply appropriate adaptation strategies when necessary to support the user. In safety-critical work environments, human-machine systems aim at maintaining high performance and avoid failure. The detection of critical emotional user states requiring adaptation should take individual differences in the emotion-performance relationship into account. Hence, we suggested the Affective Response Categories (ARCs), a classification concept considering these differences. In the present contribution, we investigated if the proposed ARCs are part of a stable trait or states changing over time. Physiological measures, such as pupil diameter and facial expression; as well as performance data from two air space surveillance laboratory experiments differing in context and sample origin (N1 = 50; N2 = 17) were analyzed. Results of two one-sided tests (TOSTs) and intraclass correlation coefficients (ICC) based on data from both experiments provided evidence for stability of the categories over time, indicating that a continuous user state diagnosis based on a single classification of a user's ARC is possible. With these results, the present contribution provides the foundation for developing a diagnostic engine of users' emotional state.