Psychosis linked to reduced information compression in speech patterns
Psychosis involves a deficit of information compression in connected speech
Computation and Language
Summary
Psychosis affects how people organize and compress information when they speak. The authors found that people with psychosis produce speech that is less predictable and less efficiently organized, especially in sentence structure. They studied this by comparing the surprise levels of words based on their frequency and their context within sentences. This shows that a problem with grammar and information compression might underlie speech issues in psychosis.
What this means in practice
- •For clinical speech analysts: Better detect and characterize speech abnormalities in psychosis by measuring information compression deficits using language model surprisal metrics.
- •For mental health app developers: Inform the creation of tools that monitor changes in speech patterns for early detection or tracking of psychosis progression using compression and prediction deficits.$Commercial implications: Enables development of diagnostic or monitoring apps for psychosis leveraging speech analysis based on this linguistic deficit.
Authors
Samuele Vallisa, Claudio Palominos, Rui He, Emre Bora, Burcu Verim, Cemal Demirlek, Berna Yalincetin, Philipp Homan, Wolfram Hinzen
Abstract
Large language models (LLMs) with human-like performance on linguistic tasks have transformed the study of language in neurodiverse conditions. LLMs provide representations of linguistic input in the form of high-dimensional vectors (embeddings), and next-token predictions computed from these embeddings. Previous crosslinguistic evidence suggests a complexity reduction in the form of both lower intrinsic dimensionality (ID) of LLM representations and higher mean surprisal (prediction error) in psychosis. We hypothesized that these metrics reflect a general deficit of information compression in psychosis, linked to grammatical organization as what enables predictions in language.We operationalized surprisal difference as the difference between surprisal as estimated from word frequency and surprisal as based on a contextual LM, which is sensitive to grammatical organization over and above lexical concepts. Using a dataset of 144 Turkish speakers, including 106 patients with schizophrenia-spectrum disorders (SSD) - 56 with chronic schizophrenia (SZH), 33 with first-episode psychosis (FEP), and 17 with schizoaffective disorder (SZA) - and 38 healthy controls. We report: (1) Surprisal difference is attenuated in all clinical groups relative to controls, independently of word count; (2) Compressibility (intrinsic dimension) is reduced in SZH and FEP; (3) Syntactic complexity and compressibility both predict surprisal difference. These results, further refining an alteration in the geometry of the semantic space in psychosis as previously attested, suggest a broader deficit in information compression in this disorder, with a mechanistic underpinning in the operations of grammar.