{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:48:03Z","timestamp":1767340083933,"version":"3.32.0"},"reference-count":46,"publisher":"MIT Press","issue":"4","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":266,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Large Language Models (LLMs) and humans acquire knowledge about language without direct supervision. LLMs do so by means of specific training objectives, while humans rely on sensory experience and social interaction. This parallelism has created a feeling in NLP and cognitive science that a systematic understanding of how LLMs acquire and use the encoded knowledge could provide useful insights for studying human cognition. Conversely, methods and findings from the field of cognitive science have occasionally inspired language model development. Yet, the differences in the way that language is processed by machines and humans\u2014in terms of learning mechanisms, amounts of data used, grounding and access to different modalities\u2014make a direct translation of insights challenging. The aim of this edited volume has been to create a forum of exchange and debate along this line of research, inviting contributions that further elucidate similarities and differences between humans and LLMs.<\/jats:p>","DOI":"10.1162\/coli_e_00539","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T20:06:19Z","timestamp":1727121979000},"page":"1201-1210","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["Language Learning, Representation, and Processing in Humans and\n                    Machines: Introduction to the Special Issue"],"prefix":"10.1162","volume":"50","author":[{"given":"Marianna","family":"Apidianaki","sequence":"first","affiliation":[{"name":"University of Pennsylvania, Department of Computer and Information Science. marapi@seas.upenn.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdellah","family":"Fourtassi","sequence":"additional","affiliation":[{"name":"Aix Marseille University, CNRS, LIS. abdellah.fourtassi@gmail.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Pad\u00f3","sequence":"additional","affiliation":[{"name":"University of Stuttgart, Institute for Natural Language Processing (IMS). pado@ims.uni-stuttgart.de"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2024,12,1]]},"reference":[{"issue":"4","key":"2024122021045580500_bib1","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1162\/coli_a_00530","article-title":"Exceptions, instantiations, and\n                        overgeneralization: Insights into how language models process\n                        generics","volume":"50","author":"Allaway","year":"2024","journal-title":"Computational Linguistics"},{"key":"2024122021045580500_bib2","article-title":"A neural probabilistic language\n                        model","volume-title":"Advances in Neural Information Processing\n                        Systems","author":"Bengio","year":"2000"},{"issue":"4","key":"2024122021045580500_bib3","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1162\/coli_a_00534","article-title":"Humans learn language from situated\n                        communicative interactions. 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