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Unsupervised learning and the natural origins of content

Korbak, Tomasz (2019) Unsupervised learning and the natural origins of content. [Preprint]

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Abstract

In this paper I evaluate the prospects and limitations of radical enactivism as recently developed by Hutto and Myin (henceforth, “H&M”) (2017). According to radical enactivism, cognition does not essentially involve content and admits explanations on a semantic level only as far as it is scaffolded with social and linguistic practices. Numerous authors argued this view to be indefensible because H&M’s objections against semantic accounts of basic minds are flawed and they fail to provide a positive research program for cognitive science. I investigate these concerns focusing on H&M’s criticism of predictive processing account of cognition (dubbed Bootstrap Hell argument) and their own account of the emergence of content (the Natural Origins of Content). My claim is that H&M fail in both of these fronts, which cast a shadow of doubt on whether radical enactivism is a philosophically and empirically interesting approach at all.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Korbak, Tomasz0000-0002-6258-2013
Keywords: Hard Problem of Content, radical enactivism, predictive processing, language models
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science > Concepts and Representations
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: Mr. Tomasz Korbak
Date Deposited: 25 Apr 2019 04:51
Last Modified: 25 Apr 2019 04:51
Item ID: 15928
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science > Concepts and Representations
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 2019
URI: http://philsci-archive.pitt.edu/id/eprint/15928

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