Latent mechanisms of language disorganization relate to specific dimensions of psychopathology
Year: 2024
Type: article
Study design: Observational Study
Abstract: Comprehensible communication is critical for social functioning and well-being. In psychopathology, incoherent discourse is assumed to reflect disorganized thinking, which is classically linked to psychotic disorders. However, people do not express everything that comes to mind, rendering inferences from discourse to the underlying structure of thought challenging. Indeed, a range of psychopathologies are linked to self-reported disorganized thinking in the absence of language output incoherence. Here we combine natural language processing and computational modeling of free association to detail the relationship between disorganized thinking and language (in)coherence in a large sample of participants varying across different dimensions of psychopathology. Our approach allowed us to differentiate between disorganized thinking, disinhibited thought expression and deliberate creativity. We find evidence for both under-regulated and over-regulated disorganized thinking, which relate to two specific dimensions of psychopathology: self-reported eccentricity and suspiciousness. Broadly, these results underscore the theoretical progress afforded by analyzing latent dimensions underlying behavior and psychopathology.
Source: Nature Mental Health
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $12,290
Awards: G1456649757, G1805438550, Wellcome Centre for Human Neuroimaging, G2834132638, G3018663809, Medical image computing for next-generation healthcare technology, G354723070, G4266322423, G434337789, Dynamics of thought and thought disorders, Wellcome Centre for Human Neuroimaging, G4589903645, The neurobiology of motivation in health and disease., Using computational modelling to characterise and plan treatments for schizophrenia, G5882669802, Finding psychosis subtypes using machine learning, clinical, genetic and multimodal imaging data, G7765275075, G844244329, G8509269580
OpenAlex ID: W4404668410
