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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”) (2013, 2017). According to radical enactivism, cognition does not essentially involve content and admits explanations on a semantic level only as far as cognition is scaffolded with social and linguistic practices. I investigate their claims, focusing on H&M’s criticism of the predictive processing account of cognition (dubbed the bootstrap hell argument) and their own account of the emergence of content (the natural origins of content). I argue that H&M fail on two fronts: unsupervised learning can arrive at contentful representations and H&M’s account of the emergence of content assumes an equivalent bootstrapping. My case is illustrated with Skyrms’ evolutionary game-theoretic account of the emergence of content and recent deep learning research on neural language models. These arguments cast a shadow of doubt on whether radical enactivism is philosophically interesting or empirically plausible.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Korbak, Tomasz0000-0002-6258-2013
Keywords: hard problem of content; radical enactivism; predictive processing; neural language models; deep learning; bootstrap hell; semantic information
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: 15 Sep 2019 02:15
Last Modified: 15 Sep 2019 02:15
Item ID: 16429
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: https://philsci-archive-dev.library.pitt.edu/id/eprint/16429

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