Deconstructing emotions in self-control through computational modeling

Abstract

Positive and negative emotions have a determining role in self-control, a vital aspect of human decision-making, defined as the dilemma between a smaller sooner reward and a larger later reward. Self-control, as an internal conflict between the higher (pre-frontal cortex) and the lower (limbic system) parts of the brain, has already been simulated using the Iterated Prisoner’s Dilemma game with learning in a computational model. However, the concept of emotions, defined as states elicited by positive and negative reinforcers, is absent from the existing self-control model. By increasing and decreasing the values of the reinforcement signals in the Prisoner’s Dilemma payoff matrix in-between the rounds, we simulated the increment or decrement of positive or negative emotions’ intensity and thus the effects of the presence of emotions, rather than the emotions per se. Our results reflect the restorative role of positive emotions on self-control, the necessity of negative emotions for successful self-control and the impairment of self-control due to intense negative emotions. Furthermore, our results reveal the importance of parameters in self-regulation, such as the intensity of emotions and the frequency it changes. In conclusion, we incorporated the effect of emotions in a computational model of self-control, and with our results complying with cognitive science literature, we demonstrated the cognitive adequacy of our model. We anticipate in this way to provide novel approaches for comprehending self-control behaviour, and to contribute to the general attempt of modeling human behaviour.