arXiv subjects

Emre Bora

Publications and source records attributed to Emre Bora.

2 recordsLinked to original sources

arXiv2026

Psychosis involves a deficit of information compression in connected speech

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.

cs.CL↗

arXiv2026

Deviance from a pink noise regime in the temporal organization of semantic relations in psychosis

The notion of pink noise refers to 'scale-invariant' temporal dynamics, where fluctuations exhibit similar statistical structure across time scales. Departures from a regime associated with such scale-free organization toward uncorrelated 'white' noise or overly persistent 'brown' noise have been widely identified as markers of pathology across physiological and cognitive domains. Whether comparable alterations characterize the temporal organization of language remains largely unexplored. We address this question in the domain of psychosis, where language anomalies are pervasively documented. Specifically, we apply detrended fluctuation analysis (DFA) to quantify temporal scaling in BERT-derived continuous cosine-similarity time series capturing trajectories through semantic space, using clinical transcripts from patients and controls across three independent datasets. DFA scaling exponents were extracted to characterize the strength of long-range temporal correlations. Across all datasets, patients exhibited significantly elevated scaling exponents relative to controls, indicating abnormally strong long-range correlations with excessive persistence in semantic fluctuations. This temporal analysis opens a window into the multi-timescale organization of meaning as it unfolds in discourse. The results reveal a signature of altered temporal scaling in speech, consistent with deviations from criticality in physiological domains, paralleling known departures from criticality in brain function in psychosis and suggesting possible links between these two domains.

cond-mat.stat-mech↗