Content and misrepresentation in hierarchical generative models.
In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization (PEM) framework....
| Publicado en: | Synthese Vol. 195; no. 6; pp. 2387 - 2416 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2018
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| Materias: | |
| 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=129833592&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129833592 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Jun2018 vid: 195 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 129833592 10.1007/s11229-017-1435-7 ppf: 2387 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 619KB tig: atl: Content and misrepresentation in hierarchical generative models. aug: au: Kiefer, Alex Hohwy, Jakob affil: City University of New York Graduate Center, New York, NY, USA Cognition & Philosophy Lab, Monash University, Melbourne, Australia su: Mental representation Sensory perception Cognitive analysis Bayesian analysis Statistical decision making Inference (Logic) sug: subj: Mental representation Sensory perception Cognitive analysis Bayesian analysis Statistical decision making Inference (Logic) keyword: Functional role semantics Generative model Kullbach-Leibler divergence Misrepresentation Prediction error minimization Problem of content Recognition model Structural resemblance Unsupervised learning Variational Bayesian inference ab: In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization (PEM) framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski (Synthese 193(2):559-582, <xref>2016</xref>) and Gładziejewski and Miłkowski (Biol Philos, <xref>2017</xref>), to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach-Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2018. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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