Philosophy of Science 87 (4):590-611 (2020)
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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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| Keywords | models artificial intelligence connectionism inference |
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| Reprint years | 2020 |
| DOI | 10.1086/709730 |
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References found in this work BETA
Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience.Carl F. Craver - 2007 - Oxford University Press, Clarendon Press.
Simulation and Similarity: Using Models to Understand the World.Michael Weisberg - 2013 - Oxford University Press.
Connectionism and Cognitive Architecture: A Critical Analysis.Jerry A. Fodor & Zenon W. Pylyshyn - 1988 - Cognition 28 (1-2):3-71.
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Citations of this work BETA
Prediction Versus Understanding in Computationally Enhanced Neuroscience.M. Chirimuuta - 2020 - Synthese 199 (1-2):767-790.
Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence.Hajo Greif - 2022 - Minds and Machines 32 (1):111-133.
Mapping representational mechanisms with deep neural networks.Phillip Hintikka Kieval - 2022 - Synthese 200 (3):1-25.
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