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What does the mind learn? A comparison of human and machine learning representations

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Spicer, Jake and Sanborn, Adam N. (2019) What does the mind learn? A comparison of human and machine learning representations. Current Opinion in Neurobiology, 55 . pp. 97-102. doi:10.1016/j.conb.2019.02.004 ISSN 0959-4388.

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Official URL: https://doi.org/10.1016/j.conb.2019.02.004

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Abstract

We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contain an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation. We suggest that continued applications will allow cognitive researchers the ability to model the complex real-world problems where machine learning has recently been successful, providing more complete behavioural descriptions. This will however also require careful consideration of appropriate algorithmic constraints alongside these methods in order to find a combination which captures both the strengths and weaknesses of human cognition.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Science > Psychology
Journal or Publication Title: Current Opinion in Neurobiology
Publisher: Elsevier Science BV
ISSN: 0959-4388
Official Date: April 2019
Dates:
DateEvent
April 2019Published
11 March 2019Available
7 February 2019Accepted
Volume: 55
Page Range: pp. 97-102
DOI: 10.1016/j.conb.2019.02.004
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 6 March 2019
Date of first compliant Open Access: 11 March 2020
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