Sleep‐Related Attentional Bias in Insomnia: A Drift Diffusion Model Approach

Cognitive models propose that insomnia is maintained in part by selective attention to sleep-related information, yet reaction-time indices alone offer limited mechanistic specificity. We investigated sleep-related attentional bias in adolescents and young adults with insomnia disorder (n = 201; aged 15-24 years; DSM-5) using a sleep-related dot-probe task with sleep-related and neutral Cantonese Chinese word pairs. Trial-level responses were analysed with Hierarchical Drift Diffusion Modelling (HDDM) to estimate drift rate (v), the speed of evidence accumulation for probe response choices and to examine moderation by anxiety symptoms. Drift rates were higher on congruent than incongruent trials (q = 0.036), indicating faster evidence accumulation when the probe appeared in the location of sleep-related words, consistent with sleep-related attentional bias indexed indirectly via probe responses. Higher anxiety was associated with faster drift rates across both trial types (q = 0.023 and q = 0.024), consistent with generalised hyperarousal rather than selective enhancement of sleep-related bias. The congruency × anxiety interaction was not significant (95% HDI [-0.10, 0.31]). These findings provide computational evidence consistent with sleep-related attentional bias in young people with insomnia and suggest that comorbid anxiety is associated with broadly increased evidence accumulation rather than cue-specific amplification.

Authors

Institutions

Publication Details

Journal
Journal of Sleep Research
Published
2026-02-24
DOI
https://doi.org/10.1111/jsr.70315
Primary Topic
Sleep and related disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sleep‐Related Attentional Bias in Insomnia: A Drift Diffusion Model Approach

Tong Han Chung, Nazanin Biabani, D. O'Regan, V. Kumari et al.
Journal of Sleep Research
Sleep and related disorders
article

Sleep‐Related Attentional Bias in Insomnia: A Drift Diffusion Model Approach

Tong Han Chung, Nazanin Biabani, D. O'Regan, V. Kumari, Ivana Rosenzweig, Sharon L. Naismith, Toby Wise, Panagis Drakatos, Sing-Hang Cheung, Isla Tsz Kwan Hui, Romola S Bucks, Dieter Riemann, Shirley Xin Li
article en

Abstract

Cognitive models propose that insomnia is maintained in part by selective attention to sleep-related information, yet reaction-time indices alone offer limited mechanistic specificity. We investigated sleep-related attentional bias in adolescents and young adults with insomnia disorder (n = 201; aged 15-24 years; DSM-5) using a sleep-related dot-probe task with sleep-related and neutral Cantonese Chinese word pairs. Trial-level responses were analysed with Hierarchical Drift Diffusion Modelling (HDDM) to estimate drift rate (v), the speed of evidence accumulation for probe response choices and to examine moderation by anxiety symptoms. Drift rates were higher on congruent than incongruent trials (q = 0.036), indicating faster evidence accumulation when the probe appeared in the location of sleep-related words, consistent with sleep-related attentional bias indexed indirectly via probe responses. Higher anxiety was associated with faster drift rates across both trial types (q = 0.023 and q = 0.024), consistent with generalised hyperarousal rather than selective enhancement of sleep-related bias. The congruency × anxiety interaction was not significant (95% HDI [-0.10, 0.31]). These findings provide computational evidence consistent with sleep-related attentional bias in young people with insomnia and suggest that comorbid anxiety is associated with broadly increased evidence accumulation rather than cue-specific amplification.

Journal of Sleep Research
Wellcome Centre for Human Neuroimaging (GB), The University of Western Australia (AU), King's College London (GB), Guy's and St Thomas' NHS Foundation Trust (GB), University Medical Center Freiburg (DE), Department of Health and Aged Care (AU), Brunel University of London (GB), University of Hong Kong (HK)
Openalex Percentile: Top 45%
Sleep and related disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.