precision-weighting

Compiled with AI assistance and selectively reviewed. Please consult the cited passages; the books remain the authoritative source. About this companion

Editorial synthesis · provisional

Definition

Precision-weighting is the predictive-processing account of how differences between expected and received input count unequally according to their estimated reliability. RB2 connects attention with the modulation of expected precision, giving some prediction errors greater influence than others. The proposal retains that model-specific sense without identifying confidence with truth, reliability with moral importance or every instance of attention with one verified neural mechanism.

Explanation

The direct passage distinguishes errors treated as reliable signals from those discounted as noise. Precision affects how strongly an error can revise expectations or guide response. It is therefore more specific than the mere existence of a discrepancy and does not imply that the largest mismatch always deserves the greatest weight.

RB2 places the account within attention and active sampling, including action that selects what is encountered next. The examples make perception responsive and selective, rather than a neutral intake of equally weighted data. They remain illustrations of the framework, not independent experimental confirmation of every attention-related phenomenon.

The surrounding warning separates a useful unifying vocabulary from a complete ontology of mind. The proposed entry consequently preserves expected reliability as a theoretical quantity, without deriving ethical significance or infallible knowledge from a precision estimate. Phenomenological and biological accounts remain relevant rather than eliminated by this computational description.

Scope and attribution

Keep the local predictive-processing formulation and its theoretical status.

RB2 — The Real Book, Volume II: Becoming Is Being

Expected reliability modulates prediction-error influence within a qualified computational account.

Occurrence locators: 107, 114, 115, 116.

Background used in the editorial explanation

Precision weighting is distinguished from generic limits of predictive processing.