Delegated precision: a predictive-processing hypothesis on external affective labeling and self-interpretation

Predictive-processing accounts describe affective inference as the integration of bodily evidence and learned expectations. Emotion-aware systems increasingly supply affective labels during that process. This Hypothesis and Theory article asks when repeated external interpretation changes subsequent self-interpretation, rather than merely adding a useful cue. We propose delegated precision as a conditional, history-dependent alteration in uncertainty about the reliability of endogenous affective evidence and its later use. A counterexample establishes that increasing an external cue's relative weight does not itself degrade the internal channel. A minimal model separates external consultation, internal inspection, qualifying feedback, reliability beliefs, and choices. Under an explicitly additional, episode-based discounting assumption, a Gamma belief about internal precision becomes less concentrated while its mean remains fixed; marginalization produces a Student- t predictive likelihood. Two analytic results identify a finite maximum internal log Bayes factor against a conflicting label and a parameter-dependent crossover in local evidence sensitivity. Neither result by itself establishes a human deficit: the consequences depend on experimentally constrained priors, cue validity, response policy, and the feasible stimulus range. We specify conditions for low internal inspection, an illustrative mean-field stationary point, a competing no-discount account, and a prospective study crossing source history, internal inspection, feedback, and independently manipulated label validity. The central hypothesis is challenged if sustained reliance and reduced internal inspection produce no meaningful delayed change in calibrated cue-free internal-evidence use within the prespecified stimulus and opposing-evidence range under an adequately powered, identifiable test. Sensory loss, reduced emotional granularity, and altered phenomenal experience remain separate hypotheses. The framework proposes a discriminable learning pathway, not an established effect of emotion AI.