Decision making in social neurobiological systems modeled as transitions in dynamic pattern formation

Abstract

Extant models of decision making in social neurobiological systems have typically explained task dynamics as characterized by transitions between two attractors. In this paper, we model a three-attractor task exemplified in a team sport context. The model showed that an attacker–defender dyadic system can be described by the angle x between a vector connecting the participants and the try line. This variable was proposed as an order parameter of the system and could be dynamically expressed by integrating a potential function. Empirical evidence has revealed that this kind of system has three stable attractors, with a potential function of the form V(x)=−k₁x+k₂ax²/2−bx⁴/4+x⁶/6, where k₁ and k₂ are two control parameters. Random fluctuations were also observed in system behavior, modeled as white noise ε _t , leading to the motion equation dx/dt = −dV/dx+Q^0.5ε _t , where Q is the noise variance. The model successfully mirrored the behavioral dynamics of agents in a social neurobiological system, exemplified by interactions of players in a team sport.