An adaptive computational model of emotion regulation strategies based on gross theory

Strong evidence support the tenet that emotions pose an important positive component in the process of decision making and other cognitive processes. In parallel with that, improper emotional responses can be tracked in many forms of psychopathology. Emotion regulation strategies target this potential risk of having inappropriate (hyper or below) levels of emotions and are aimed at balancing one's emotional levels in different situations. This study considers a computational model developed by Bosse and colleagues which was built recently based on Gross theory of emotions regulation and applies different enhancements to it. In particular, we extend the dynamism of the original model in order to build a more realistic system. The proposed model has a higher degree of adaptation by declaring a dynamic persistence factor in terms of the mood and personality of the individual. Furthermore, it enriches the original model by injecting a component of domain specific knowledge into its regulation strategies assessment.