Characterizing the Paradoxical Interplay of Semantic Perseveration and Derailment in Psychiatry Using Theory-Driven
Robin Quillivic1,2, Raymond J Dolan3,4, Isaac Fradkin5
1CESSP, UMR 8209, Paris 75005, France.
Schizophrenia Bulletin
|November 28, 2025
Summary
New natural language processing (NLP) metrics effectively capture perseveration in formal thought disorder (FTD), distinguishing it from derailment. These density-based metrics show promise for understanding FTD symptoms in various psychiatric conditions.
Area of Science:
- Computational linguistics
- Psychiatric diagnostics
- Natural Language Processing (NLP)
Background:
- Formal thought disorder (FTD), marked by derailment and perseveration, is key in psychosis and present in other psychiatric conditions.
- Existing NLP metrics for FTD, like cosine distance, struggle to differentiate between derailment and perseveration due to their opposing effects.
- Novel NLP metrics are needed to specifically identify and quantify perseveration independently of speech disorganization.
Purpose of the Study:
- To develop and validate novel NLP metrics for accurately measuring perseveration in formal thought disorder.
- To differentiate the linguistic markers of perseveration from those of derailment using advanced NLP techniques.
- To assess the utility of these new metrics in a transdiagnostic sample and in relation to psychopathological dimensions.
Main Methods:
- Synthetic narrative data were generated using large language models, manipulating semantic perseveration and derailment independently.
- Novel density-based NLP metrics were developed, focusing on semantic structure and density via clustering and dimensionality reduction.
- The performance of novel metrics was compared against traditional cosine distance metrics on synthetic data and an 811-participant general population sample.
Main Results:
- Traditional cosine distance metrics effectively detected derailment but were less accurate for perseveration.
- Novel density-based metrics demonstrated superior performance in identifying and separating perseveration from derailment.
- Density metrics also outperformed traditional metrics in identifying perseveration linked to repetitive thinking, depressive symptoms, and negative FTD.
Conclusions:
- Integrating density-based NLP metrics with traditional semantic distance measures offers a more comprehensive approach to capturing FTD symptoms.
- This novel approach enhances the understanding of perseveration and its interplay with thought and speech disorganization.
- Further clinical validation is required, but these metrics hold potential for improved clinical assessment and understanding of FTD.
Keywords:
computational psychiatrylanguage modelnatural language processingperseverationrepetitive thinkingsemantic densitythought disorderMore Related Videos
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