Prediction–Processing Discrepancies: A Regulatory Principle of Predictive Cognition

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Authors: Brouillet
Cited by: 0

Abstract

In contemporary cognitive science, a consensus is emerging: cognition is continuously guided by predictions about future states. Although discrepancies between predictions and ongoing processing are therefore inevitable, their functional role remains poorly understood. The present article introduces the concept of Prediction–Processing Discrepancies (PPDs) as the psychological expression of predictive regulation. Rather than viewing discrepancies solely as cues for subsequent judgments, we propose that they act as transient regulatory signals that reorganize cognitive processing before conscious experience emerges. Two experiments tested the prediction that PPDs should influence cognitive processing beyond the stimulus that generated the discrepancy. In Experiment 1, a discrepancy induced by a contextual stimulus facilitated subsequent perceptual discrimination. Experiment 2 extended this finding to lexical decision, demonstrating that the effect generalizes beyond low-level perceptual processing. Across both experiments, the consequences of PPDs transferred to subsequent cognitive operations, supporting their interpretation as transient regulatory mechanisms rather than mere cues for attribution. These findings extend Whittlesea's discrepancy-based theory of processing fluency by addressing the functional role of discrepancies before conscious judgment. More broadly, they suggest that PPDs provide a psychological construct linking computational theories of predictive processing with experimentally observable cognitive phenomena. Within this framework, predictive regulation precedes conscious interpretation, offering a unified account of processing fluency and a broader range of discrepancy-based phenomena in cognitive psychology.

Fields of Study & Subjects

Embodied and Extended Cognition Cognitive Science and Education Research Neural and Behavioral Psychology Studies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 August 2026
Language
EN
Document type
Dataset