Studying Psychosis Using Natural Language Generation: A Review of Emerging Opportunities

Lena Palaniyappan*, David Benrimoh, Alban Voppel, Roberta Rocca

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    4 Citations (Scopus)
    44 Downloads (Pure)

    Abstract

    Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how and why these symptoms develop. Natural language generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features. In this conceptual review, we propose using NLG systems that are at various stages of development as in silico tools to study linguistic features of psychosis. We argue that a program of in silico experimental research on the network architecture, function, learning rules, and training of NLG systems can help us understand better why thought disorder occurs in patients. This will allow us to gain a better understanding of the relationship between language and psychosis and potentially pave the way for new therapeutic approaches to address this vexing challenge.

    Original languageEnglish
    Pages (from-to)994-1004
    Number of pages11
    JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
    Volume8
    Issue number10
    DOIs
    Publication statusPublished - Oct-2023

    Keywords

    • Computational psychiatry
    • Deep learning
    • Explainable models
    • Large language models
    • Neural networks
    • Neuroimaging

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