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 | ||||||
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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 |
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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 |
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Date: | 2019 | ||||||
URI: | https://philsci-archive.pitt.edu/id/eprint/15928 |
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