Neurodynamics of Decision-Making—A Computational Approach

Abstract

Decision-making is a complex process that normally seems to involve several brain structures. In particular, amygdala, orbitofrontal cortex (OFC), and lateral prefrontal cortex (LPFC) seem to be essential in human decision-making, where both emotional and cognitive aspects are taken into consideration. In this paper, we present a stochastic population model representing the neural information processing of decision-making, from perception to behavioral activity. We model the population dynamics of the three neural structures significant in the decision-making process (amygdala, OFC, and LPFC), as well as their interaction. In our model, amygdala and OFC represent the neural correlates of secondary emotion, while the activity of OFC neural populations represents the outcome expectancy of alternatives, and the cognitive aspect of decision-making is controlled by LPFC. The results may have implications for how we make decisions for our individual actions, as well as for societal choices, where we take examples from transport and its impact on climate change.