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Are generative models structural representations?

Facchin, Marco (2021) Are generative models structural representations? [Preprint]

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Abstract

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Facchin, Marcomarco.facchin@iusspavia.it0000-0001-5753-9873
Keywords: Structural representations; Antirepresentationalism; Predictive processing; Representation wars; Generative models
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Realism/Anti-realism
Depositing User: Dr. Marco Facchin
Date Deposited: 19 Mar 2021 15:46
Last Modified: 19 Mar 2021 15:46
Item ID: 18824
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Realism/Anti-realism
Date: 2021
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/18824

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