State Estimation as a Feasibility Condition for Cognition under Partial Observability

Intelligent behavior across domains such as motor control, speech production, learning, and social cognition must operate under severe uncertainty: sensory feedback is noisy and delayed, actions are imprecise, and the causes of observable outcomes are often ambiguous. Under these conditions, cognition cannot be organized solely around observable behavior. When multiple latent states generate identical observations, regulation, learning, and evaluation based on behavior alone become fundamentally ill-posed. We argue that inference over latent state is therefore not a modeling choice but a necessary condition for feasible cognition. Observable signals—movements, sensory inputs, and social actions—must be treated as noisy evidence about hidden causes, such as motor state, articulatory configuration, or intentions, rather than as primary targets of control or judgment. This perspective explains why diverse domains exhibit robustness, tolerance of variability, context sensitivity, and structured credit assignment. These properties arise not from precise control of behavior, but from regulation with respect to inferred, task-relevant state under conditions of partial observability. Cognition, from this view, is organized around maintaining and updating latent state estimates that render behavior, learning, and evaluation well-defined under uncertainty.