Where Mechanism Meets Normativity: Predictive Processing in Search of Explanatory Constraints

Abstract

Predictive Processing (PP) is commonly described as a mechanism sketch—an incomplete, primarily mechanistic representation of cognitive processes. While this characterization rightly emphasizes PP’s structural and causal explanatory aspects, I argue that it tends to overlook important functional and normative dimensions that are essential for a comprehensive account of cognitive processes. In response, I claim that PP, while providing mechanism sketches in the standard sense, simultaneously performs a second, normative-functional epistemic role. This expanded view on PP preserves the value of its mechanistic explanation while clarifying the essential role of functional constraints in shaping predictive models. Specifically, I show how PP models postulate explanatory constraints—formal, structural, and functional—that are inherently normative, as they specify the conditions viable mechanisms must satisfy to be biologically plausible and adaptive. Drawing on Levenstein et al. (2023), I situate this account within a pragmatic framework that recognizes the legitimacy of mechanistic, normative, and descriptive theories without reducing one to another. This pluralistic approach enables genuine integration across explanatory levels and points toward more adequate future models in cognitive neuroscience.