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But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation (\u201cbat\u201d, in a baseball vs. a vampire context), as well as more nuanced differences of meaning\u2014for example, polysemous words such as \u201cbook\u201d, which can be interpreted in distinct but related senses (\u201cexplain a book\u201d, information, vs. \u201copen a book\u201d, object) whose differences are fine\u2010grained. We study these subtle differences in lexical meaning along the concrete\/abstract dimension, as they are triggered by verb\u2010noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine\u2010grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness\/abstractness.<\/jats:p>","DOI":"10.1111\/cogs.13388","type":"journal-article","created":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T18:14:25Z","timestamp":1702750465000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Modeling Brain Representations of Words' Concreteness in Context Using GPT\u20102 and Human Ratings"],"prefix":"10.1111","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2484-2483","authenticated-orcid":false,"given":"Andrea","family":"Bruera","sequence":"first","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Cognitive Science Research Group Queen Mary University of London"},{"name":"Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2010-8380","authenticated-orcid":false,"given":"Yuan","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Cognitive Science Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0316-9787","authenticated-orcid":false,"given":"Andrew","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Neurology Medical College of Wisconsin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5653-1412","authenticated-orcid":false,"given":"Derya","family":"\u00c7okal","sequence":"additional","affiliation":[{"name":"Department of German Language and Literature I\u2010Linguistics University of Cologne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5494-9770","authenticated-orcid":false,"given":"Janosch","family":"Haber","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Cognitive Science Research Group Queen Mary University of London"},{"name":"Chattermill, London"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8469-2072","authenticated-orcid":false,"given":"Massimo","family":"Poesio","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Cognitive Science Research Group Queen Mary University of London"},{"name":"Department of Information and Computing Sciences University of Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"1","article-title":"Machine learning for neuroimaging withscikit\u2010learn","volume":"14","author":"Abraham A.","year":"2014","journal-title":"Frontiers in Neuroinformatics"},{"issue":"9","key":"e_1_2_12_3_1","first-page":"4379","article-title":"Predicting neural activity patterns associated with sentences using a neurobiologically motivated model of semantic representation","volume":"27","author":"Anderson A. 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