Testable or bust: theoretical lessons for predictive processing

Type Journal Article
Author Marcin Miłkowski
Author Piotr Litwin
URL https://link.springer.com/10.1007/s11229-022-03891-9
Volume 200
Issue 6
Pages 462
Publication Synthese
ISSN 1573-0964
Date 2022-11-02
Journal Abbr Synthese
DOI 10.1007/s11229-022-03891-9
Accessed 2022-11-04 12:05:41
Library Catalog DOI.org (Crossref)
Language en
Abstract Abstract

The predictive processing (PP) account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which may employ various kinds of scientific representations (from theories to frameworks and toolboxes), differing in the scope of empirical commitments they entail. Two major perspectives on PP
qua
cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science.

Short Title Testable or bust

Source: Publications

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