Information Self-Structuring: Key Principle for Learning and Development

Abstract

Intelligence and intelligence-like processes are characterized by a complex yet balanced interplay across multiple time scales between an agent's brain, body, and environment. Through sensor and motor activity natural organisms and robots are continuously and dynamically coupled to their environments. We argue that such coupling represents a major functional rationale for the ability of embodied agents to actively structure their sensory input and to generate statistical regularities. Such regularities in the multimodal sensory data relayed to the brain are critical for enabling appropriate developmental processes, perceptual categorization, adaptation, and learning. We show how information theoretical measures can be used to quantify statistical structure in sensory and motor channels of a robot capable of saliency-driven, attention-guided behavior. We also discuss the potential importance of such measures for understanding sensorimotor coordination in organisms (in particular, visual attention) and for robot design