State Estimation as a Unifying Principle of Cognition

Intelligent behavior across a wide range of activities such as motor control, speech production, learning, and social cognition must be generated and operate under severe uncertainty: sensory feedback is noisy and delayed, actions are imprecise, and the true causes of observable outcomes are rarely directly accessible. We argue that under these conditions, for cognition to be optimal, it cannot be organized only around direct control or evaluation of observable actions. Instead, it must largely operate through inference over latent state variables that summarize the underlying condition of the system and determine its future behavior. From this perspective, movements, sensory inputs, and social behaviors are treated as noisy evidence about hidden causes—such as motor state, articulatory configuration, or intentions—rather than as primary targets of control or judgment. We show that this state-centric organization across many domains is not a modelling preference but a functional necessity for feasible behavior under uncertainty, and that it provides a unified explanation for robustness, tolerance of variability, context sensitivity, and credit assignment across cognitive domains. Cognition, from this view, is best understood as continuous inference over latent states in noisy, embodied, and social systems.