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Lookup NU author(s): Professor Douglas Turkington, Emeritus Professor Nicol Ferrier, Dr Stuart WatsonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The AuthorsCross-linguistic studies have demonstrated that individuals with schizophrenia—particularly those exhibiting formal thought disorder (FTD)—show distinctive distributions of noun phrases (NPs) in spontaneous speech. NPs (e.g., the picture; a husband) serve to organize the referential structure of meaning. Extracting such referential NP features, however, has traditionally required manual annotations. In this study we applied state-of-the-art large language models (LLMs) to extract these features automatically, using an existing, manually annotated dataset, in which English-speaking participants described a comic strip: 30 individuals with schizophrenia (SZ) (15 with moderate or severe FTD (SZ + FTD), 15 with minimal or no FTD (SZ−FTD), 15 neurotypical controls (NC). We first show that LLM-based analyses replicate the findings based on manual annotation, particularly highlighting that definite NPs tied to prior discourse, markers of grammatical and cognitive complexity and narrative coherence, were underused in the SZ + FTD group. Secondly, we demonstrate that LLMs, especially when used with in-context (few-shot) learning, offer a promising avenue for the automatic extraction of referential features. These results show that a cross-linguistically validated and clinically important linguistic pattern of deviance is accessible to automatized assessment with NLP.
Author(s): Cokal D, Filizer M, Villalba M, Turkington D, Ferrier IN, von Heusinger K, Watson S, Hinzen W, Poesio M
Publication type: Article
Publication status: Published
Journal: Neuropsychologia
Year: 2026
Volume: 230
Online publication date: 22/05/2026
Acceptance date: 19/05/2026
Date deposited: 15/06/2026
ISSN (print): 0028-3932
ISSN (electronic): 1873-3514
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.neuropsychologia.2026.109505
DOI: 10.1016/j.neuropsychologia.2026.109505
Data Access Statement: The underlying code for this study is available in [repository name] and can be accessed via this link: https://osf.io/p3bnf/overview? view_only=f20b3c6ad0b0492f86064333caf7d2e1. Users of this code are kindly requested to cite the associated study. The datasets generated and/or analyzed during the current study are not publicly available due to privacy or ethical considerations but are available from the corresponding author upon reasonable request. The data are available in the following link for review process: https://osf.io/p3bnf/overview? view_only=f20b3c6ad0b0492f86064333caf7d2e1
PubMed id: 42176801
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