A COMPUTATIONAL MODEL OF TEMPORAL DYNAMICS FOR ANXIETY IN INTERVIEWEE MENTAL STATE (Journal of Information and Communication Technology, 2020)
Anxiety is an aversive motivational state that occurs when an individual perceives threat at events. This condition creates harmful effects for candidates during interview sessions. An interviewee overwhelmed in such a state deploys worry as a resource to cope with the threat hence losing the ability to present the self positively for favourable assessments. Most of the digital approaches to assist interviewees in this condition are focused on coaching of verbal and non-verbal cues. The aspect of understanding interviewees’ psychological complexities that influence their behavioural tendencies is lacking in these approaches. As the first step in building an intelligent digital basedtherapy platform to overcome this issue, this article provides a building block to understanding the interviewees’ anxiety state by means of a computational model. This model is developed based on the conceptual model derived from generalized anxiety disorder theories. The formal model is valuated using mathematical analysis to determine possible equilibria state and the simulation results are tested against known cases in the literature. The simulation results showed that the degree of threats perceived at events is based on task demands and the resources to cope. Threat is the building block of anxiety through worry which is controlled by one’s personality and inherent trait anxiety. The results conform to established facts in the literature. Consequently, this model can serve as a basis to build an integrated interviewee mental state model embedded with self-efficacy and motivation constructs as a holistic approach to support interviewees in coaching environments during simulated training.