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From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence

Stinson, Catherine (2019) From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence. [Preprint]

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Abstract

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and targets are picked out, how modeling inferences are justified, and the metaphysical status of models.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Stinson, Catherinehello@catherinestinson.ca0000-0003-2770-7922
Additional Information: forthcoming in Philosophy of Science
Keywords: artificial intelligence, models, idealization, kinds, connectionist, metaphysics
Subjects: General Issues > Scientific Metaphysics
Specific Sciences > Cognitive Science
Specific Sciences > Cognitive Science > Computation
Specific Sciences > Computer Science
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Natural Kinds
Depositing User: Dr. Catherine Stinson
Date Deposited: 31 Oct 2019 05:59
Last Modified: 31 Oct 2019 05:59
Item ID: 16602
Subjects: General Issues > Scientific Metaphysics
Specific Sciences > Cognitive Science
Specific Sciences > Cognitive Science > Computation
Specific Sciences > Computer Science
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Natural Kinds
Date: 2019
URI: http://philsci-archive.pitt.edu/id/eprint/16602

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