Davies-Barton, Tyeson and Raja, Vicente and Baggs, Edward and Anderson, Michael L (2022) Debt-free intelligence: Ecological information in minds and machines. [Preprint]

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Abstract

Cognitive scientists and neuroscientists typically understand the brain as a complex information-processing system. A limitation of this information-processing metaphor is that it requires that the brain has access to a finite set of possible informational messages—a neural code—and it is unclear how this can be accounted for without appealing to a priori knowledge. For this reason, Dennett once argued that the information-processing metaphor requires cognitive neuroscience to take out a non-repayable loan of intelligence. However, recent advances in machine learning have resulted in the development of a family of algorithms, including the class of algorithms known as autoencoders, that seem capable of evading the problem of non-repayable loans of intelligence. We evaluate whether autoencoders are indeed resilient against the loans of intelligence problem. We agree that they can be so characterized. We argue, however, that autoencoders can more usefully be understood not in terms of Shannon information but instead as a proof of concept of how neural networks can attune to ecological or Gibsonian information. We thus propose that autoencoders belong to a class of algorithms for modeling the brain that have recently been dubbed direct fit algorithms.

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Item Type:	Preprint
Creators:	Creators Email ORCID Davies-Barton, Tyeson tyesondaviesbarton@gmail.com Raja, Vicente vgalian@uwo.ca Baggs, Edward ebag@sdu.dk Anderson, Michael L mande54@uwo.ca 0000-0003-0572-115X
Keywords:	Cognitive neuroscience · Information theory · Ecological psychology · Autoencoders · Machine learning
Subjects:	Specific Sciences > Neuroscience > Cognitive Neuroscience Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Cognitive Science > Perception
Depositing User:	Mr. Tyeson Davies-Barton
Date Deposited:	10 Apr 2022 18:29
Last Modified:	10 Apr 2022 18:29
Item ID:	20426
Subjects:	Specific Sciences > Neuroscience > Cognitive Neuroscience Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Cognitive Science > Perception
Date:	4 April 2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20426

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