Testable or bust: theoretical lessons for predictive processing.
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...
| Publicado en: | Synthese Vol. 200; no. 6; pp. 1 - 19 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Dec2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=160035335&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 160035335 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2022 vid: 200 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 160035335 10.1007/s11229-022-03891-9 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Testable or bust: theoretical lessons for predictive processing. aug: au: Miłkowski, Marcin Litwin, Piotr affil: Institute of Philosophy and Sociology, Polish Academy of Sciences, ul. Nowy Świat 72, 00-330, Warszawa, Poland Consciousness Lab, Institute of Psychology, Jagiellonian University, ul. Ingardena 6, 30-060, Kraków, Poland sug: ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2022. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
|---|