The dynamics of active categorical perception in an evolved model agent

Notions of embodiment, situatedness, and dynamics are increasingly being debated in cognitive science. However, these debates are often carried out in the absence of concrete examples. In order to build intuition, this paper explores a model agent to illustrate how the perspective and tools of dynamical systems theory can be applied to the analysis of situated, embodied agents capable of minimally cognitive behavior. Specifically, we study a model agent whose "nervous system" was evolved using a genetic algorithm to catch circular objects and to avoid diamond-shaped ones. After characterizing the performance, behavioral strategy and psychophysics of the best-evolved agent, its dynamics are analyzed in some detail at three different levels: (1) the entire coupled brain/body/environment system; (2) the interaction between agent and environment that generates the observed coupled dynamics; (3) the underlying neuronal properties responsible for the agent dynamics. This analysis offers both explanatory insight and testable predictions. The paper concludes with discussions of the overall picture that emerges from this analysis, the challenges this picture poses to traditional notions of representation, and the utility of a research methodology involving the analysis of simpler idealized models of complete brain/body/environment systems.