Citation: Mehraei, M. (2018). Identifying Emotion Regulation Altering Targets as Depressive Mood Disorder Treatments Using Fuzzy Stochastic Hybrid Petri Nets. IAFOR Journal of Psychology & the Behavioral Sciences, 4(1). https://doi.org/10.22492/ijpbs.4.1.04
Recent studies support that emotion regulation plays a prominent role in depression and depressive mood related disorders. However, the details related to such relations are still unknown. Therefore, constructing a model to describe and analyze these connections is essential. Fortunately, there exist many strategies to treat depressive mood disorders, but choosing the correct strategy for any individual should be personalized. Thus, there are always alternatives for discovering novel strategies to improve the treatments. The aim of this study is to model the relation between emotion regulation and depression to identify emotion regulation altering targets to improve the treatment of depressive mood disorders. By random sampling method, 108 volunteers were selected from Eastern Mediterranean University. The significant emotion traits, emotion interaction probability distribution, and personality traits of these individuals were measured using a questionnaire. In the present study, Fuzzy Stochastic Hybrid Petri nets were used as a mathematical tool to model this complex psychological system. Fuzzy and stochastic properties made it possible to deal with randomness feature of psychological systems and unknown kinetic parameters, respectively. The simulation results were obtained by finding the mean of 40,000 stochastic runs with 95% confidence level. The simulation results validated that decreasing the level of anger, distress, and fear may decrease the severity of depression. In addition, the comparison of these simulation results revealed that decreasing the level of shame, and increasing the sense of gratification can be considered to be emotion regulation altering targets, and thus as potential psychotherapy of depressive mood disorders.
depressive mood disorders, emotion regulation, fuzzy stochastic petri nets, quantitative modeling