Feasible cognition requires latent state.

Intelligent behavior across motor control, speech production, learning, and social cognition operates under uncertainty: sensory feedback is noisy and delayed, actions are imprecise, and observable outcomes often have ambiguous causes. When multiple latent states generate identical or sufficiently similar observations, regulation, learning, and evaluation based on behavior alone become underdetermined. We argue that inference over latent state is therefore a feasibility condition for cognition under partial observability, rather than merely a modeling convenience. Observable signals-movements, sensory inputs, speech outputs, and social actions-should be understood as noisy evidence about hidden causes, including motor state, articulatory configuration, task context, and intentions. This perspective suggests that separate psychological literatures share a common control-theoretic structure: regulation under noise, delay, ambiguity, and partial observability. It also helps explain why diverse domains exhibit robustness, tolerance of variability, context sensitivity, and structured credit assignment. These properties arise from regulation of inferred, task-relevant state, rather than precise control of observable behavior. The account is not proposed as a replacement for belief-state, internal-model, predictive-coding, or active-inference approaches, but as a cross-domain feasibility argument: latent-state representations support control, learning, and evaluation only when task-relevant hidden distinctions are detectable from observations and stabilizable or actionable. Cognition, from this view, depends on latent state estimates that render behavior, learning, and evaluation well-defined under uncertainty.