Connell, Louise and Lynott, Dermot (2024) What Can Language Models Tell Us About Human Cognition? Current Directions in Psychological Science, 33 (3). pp. 181-189. ISSN 0963-7214

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Abstract

Language models are a rapidly developing field of artificial intelligence with enormous potential to improve our understanding of human cognition. However, many popular language models are cognitively implausible on multiple fronts. For language models to offer plausible insights into human cognitive processing, they should implement a transparent and cognitively plausible learning mechanism, train on a quantity of text that is achievable in a human’s lifetime of language exposure, and not assume to represent all of word meaning. When care is taken to create plausible language models within these constraints, they can be a powerful tool in uncovering the nature and scope of how language shapes semantic knowledge. The distributional relationships between words, which humans represent in memory as linguistic distributional knowledge, allow people to represent and process semantic information flexibly, robustly, and efficiently.

Item Type:	Article
Keywords:	language models; linguistic distributional knowledge; semantics; cognitive plausibility;
Academic Unit:	Faculty of Science and Engineering > Psychology
Item ID:	19109
Identification Number:	https://doi.org/10.1177/09637214241242746
Depositing User:	Louise Connell
Date Deposited:	29 Oct 2024 11:38
Journal or Publication Title:	Current Directions in Psychological Science
Publisher:	Sage
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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