Learning Under Uncertainty: Predictive Processing as an Integrative Framework for Educational Research.

Abstract

Research at the intersection of neuroscience and education has generated substantial empirical findings but has often lacked a unifying theoretical framework capable of explaining why those findings belong together. Predictive processing, a family of accounts characterizing the brain as a hierarchical system that continuously generates expectations and updates internal models in response to prediction errors, has been proposed as a candidate umbrella framework for the cognitive sciences, though it remains actively debated. We examine whether its core concepts transfer meaningfully to educational contexts. Rather than deriving concrete instructional prescriptions, we propose four constraints that are especially relevant when instruction aims to produce conceptual model revision under uncertainty. We further suggest that developmental differences between children and adults can be interpreted as systematic variations in these inferential parameters. The contribution of this paper is primarily integrative: findings from educational research have largely been articulated within separate paradigms, and we argue that predictive processing may offer a common inferential vocabulary for describing them. Future work may derive empirically testable predictions from this framework, though the limitations of these accounts must be kept carefully in view.