Towards enhanced performance: an integrated framework of emotional valence, arousal, and task demand

Extensive evaluations exist concerning the linkage between objective task demands and subsequent effects on user performance. However, the human user also experiences a range of emotions related to external task demands. Problematically, little is known about the associations between emotional valence, and arousal associated with the task demand-performance axis. In this paper, we advance a theoretical model concerning such interactive influences using three dimensions: (1) emotional valence, (2) arousal, and (3) task demand. The model evaluates the impact of these dimensions on user performance. It also identifies critical emotional user states, particularly those resulting in negative performance effects, as well as non-critical emotional states that can positively impact performance. Finally, we discuss the implications for affect-adaptive systems that can mitigate the impact of critical emotional states while leveraging the benefits of non-critical ones. To effectively model performance and prevent errors in safety-critical human-machine systems, it is crucial to consider user states of emotional valence, arousal, and the current task demand. The proposed model enables the classification of critical and non-critical states within affect-adaptive systems. States characterised by negative valence, high arousal, and overload should be avoided to foster high performance, especially in safety-critical environments. Additionally, the present work offers recommendations for preserving and restoring non-critical states to ensure optimal performance and provides implications for training.