Synthetic Approach to Understanding Meta-level Cognition of Predictability in Generating Cooperative Behavior

Abstract

We propose that “predictability” is a meta-level cognitive function that accounts for cooperative behaviors and describe this from a dynamical systems perspective based on a neuro-robotic experiment. In order to bring about cooperative behaviors among individuals, individuals should attempt to predict the behavior of their partners by making internal models of them. However, the behaviors of partners are often unpredictable because individuals possess free will to generate their own independent actions. Thus, acquiring internal models which attempt to completely predict the actions of others seems to be intractable. In the current study we suggest that, when learning internal models for interacting with the partners, cooperative agents should maintain predictability monitoring mechanisms by which attention is oriented more toward predictable segments in spatio-temporal sensory input space.