arXiv subjects

Lena Palaniyappan

Publications and source records attributed to Lena Palaniyappan.

3 recordsLinked to original sources

arXiv2026

The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance

Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compresses meaning uncertainty cross-linguistically, which is reflected in brain and selectively disrupted in disorders. Compression was operationalized as the relative difference between non-contextual surprisal estimated from lexical frequency, and contextual surprisal from grammar-sensitive models. In narratives from 20 languages, contextual surprisal reduced frequency-based surprisal. This reduction closely tracked the surprisal cost of reversing word order, and scaled with richer, non-redundant lexis as organized by more complex but optimal dependency structure. During fMRI, surprisal and its reduction explained BOLD activity for comprehension and production in overlapping but distinct regions. Uncertainty reduction was significantly attenuated in aphasia, dementia, and schizophrenia, but remained intact where primary deficit is not language. These findings position uncertainty reduction via grammar as a foundational concept that illuminates principles, brain basis, and disruptions of language.

cs.CL↗

arXiv2026

Reading Between the Lines: Combining Pause Dynamics and Semantic Coherence for Automated Assessment of Thought Disorder

Formal thought disorder (FTD), a hallmark of schizophrenia spectrum disorders, manifests as incoherent speech and poses challenges for clinical assessment. Traditional clinical rating scales, though validated, are resource-intensive and lack scalability. Automated speech recognition (ASR) allows for objective quantification of linguistic and temporal features of speech, offering scalable alternatives. Furthermore, ASR-derived utterance timestamps provide access to pause dynamics, which are thought to reflect the cognitive processes underlying speech production. Yet, their added value beyond semantic measures remains insufficiently explored. In this study, we evaluated a scalable multimodal framework that integrates pause features with semantic coherence metrics across three datasets: naturalistic self-recorded diaries (AVH), structured picture descriptions (TOPSY), and dream narratives (PsyCL). Pause-related features were evaluated alongside established coherence measures using support vector regression to predict clinical FTD scores. Models using pause features alone robustly predict manually rated FTD severity consistently across datasets. Integrating pause features with semantic coherence metrics enhanced predictive performance compared to coherence-only models, with late fusion yielding the most robust and consistent gains in all three datasets. On average across datasets, Spearman correlation increased from \r{ho} = 0.413 for semantic-only models to \r{ho} = 0.455 with late fusion. The performance gains from semantic and pause features integration held consistently across all contexts, though the nature of the most informative pause patterns was dataset-dependent. These findings suggest that both pause dynamics and semantic coherence reflect complementary aspects of thought disorganization.

cs.CL↗

arXiv2025

Sleep and Activity Patterns as Transdiagnostic Behavioral Biomarkers in Psychiatry: Initial Insights from the DeeP-DD study

Background: Symptom rating scales in psychiatry are limited by reliance on self-report, and lack of predictive power. Actigraphy, a passive wearable-based method for measuring sleep and physical activity, offers objective, high-resolution behavioral data that may better reflect symptom fluctuations, but most studies have focused on narrow diagnostic groups or fixed time windows, limiting clinical translation. Objective: To examine whether actigraphy-derived sleep and activity features correlate with psychiatric symptom severity in a transdiagnostic psychiatric sample, and to identify which features are most clinically relevant across multiple temporal resolutions. Methods: We present a feasibility case series study with preliminary data from eight outpatients enrolled in the DeeP-DD study, a transdiagnostic study of digital phenotyping. Participants wore GENEActiv actigraphy devices and symptom severity was measured using a variety of validated scales. We performed intra-individual Spearman correlations and inter-individual repeated measures correlations across daily, weekly, monthly, and full-duration averages. Results: Intra-individual analyses revealed that later rise times were significantly associated with higher weekly PHQ-9 scores in participant #7 (\r{ho} = 0.74, P=.0003) and participant #4 (\r{ho} = 0.78, P=.022), as well as higher weekly GAD-7 scores in participant #7 (\r{ho} = 0.59, P=.026). Inter-individual analyses showed that weeks with later average rise time correlated with higher PHQ-9 (r = 0.48, P=.0003) and GAD-7 scores (r = 0.38, P=.032). Increased light physical activity was linked to lower PHQ-9 scores weekly (r = -0.44, P=.001) and monthly (r = -0.53, P=.014). Conclusion: Consistent associations between actigraphy features and symptoms across temporal scales and diagnostic groups underscore their potential utility for scalable, real-world clinical monitoring.

q-bio.QM↗