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Some dilemmas for an account of neural representation: A reply to Poldrack

Anderson, Michael L and Champion, Heather (2021) Some dilemmas for an account of neural representation: A reply to Poldrack. [Preprint]

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Abstract

“The physics of representation” (Poldrack 2020) aims to (1) define the word “representation” as used in the neurosciences, (2) argue that such representations as described in neuroscience are related to and usefully illuminated by the representations generated by modern neural networks, and (3) establish that these entities are “representations in good standing”. We suggest that Poldrack succeeds in (1), exposes some tensions between the broad use of the term in neuroscience and the narrower class of entities that he identifies in the end, and between the meaning of “representation” in neuroscience and in psychology in (2), and fails in (3). This results in some hard choices: give up on the broad scope of the term in neuroscience (and thereby potentially opening a gap between psychology and neuroscience) or continue to embrace the broad, psychologically inflected sense of the term, and deny the entities generated by neural nets (and the brain) are representations in the relevant sense.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Anderson, Michael Lmande54@uwo.ca0000-0003-0572-115X
Champion, Heatherhchampi2@uwo.ca
Keywords: representations; features; Neural Networks; computational theory of mind
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science > Computation
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: Dr. Michael L Anderson
Date Deposited: 09 Dec 2021 05:03
Last Modified: 09 Dec 2021 05:03
Item ID: 20003
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science > Computation
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 2021
URI: http://philsci-archive.pitt.edu/id/eprint/20003

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