Computational Modeling Applied to the Dot-Probe Task Yields Improved Reliability and Mechanistic Insights
Open accessAbstract
Background Biased patterns of attention are implicated as key mechanisms across many forms of psychopathology and have given rise to automated mechanistic interventions designed to modify such attentional preferences. However, progress is substantially hindered by limitations in widely used methods to quantify attention, bias leading to imprecision of measurement.Methods In a sample of patients who were clinically anxious (n = 70), we applied a well-validated form of computational modeling (drift-diffusion model) to trial-level reaction time data from a two-choice "dot-probe task"-the dominant paradigm used in hundreds of attention bias studies to date-in order to model distinct components of task performance.Results While drift-diffusion model-derived attention bias indices exhibited convergent validity with previous approaches (e.g., conventional bias scores, eye tracking), our novel analytic approach yielded substantially improved split-half reliability, modestly improved test-retest reliability, and revealed novel mechanistic insights regarding neural substrates of attention bias and the impact of an automated attention retraining procedure.Conclusions Computational modeling of attention bias task data may represent a new way forward to improve precision.
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The dot-probe task has notoriously poor psychometric properties that may have impeded the ability to sensitively detect changes over time.
“Although in a separate study, ketamine failed to illustrate a similar impact on attentional bias towards negative cues 128 , the use of a reaction time measure (the dot-probe task) with notoriously poor psychometric properties 136-138 may have impeded the ability to sensitively detect changes over time.”
From Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model (2020) · cites this paper as [137]
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Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model
Price, R. B., & Duman, R. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0615-x · Cited by 721 · see citing works
Self-citation
- Authors
- Price, R. B., Brown, V., & Siegle, G. J.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Price, Rebecca B.
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 85
- Issue
- 7
- Pages
- 606-612
- ISSN
- 0006-3223 (ISSN-L)
- PMC
- PMC6420394
- OpenAlex
- W2894852748
- Cited by
- 84
- References
- 45
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 13 · 2023: 12 · 2022: 13 · 2021: 11 · 2020: 11 · 2019: 7
- FWCI
- 9.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (K23MH100259)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- attentional bias, dot-probe task, drift-diffusion model, computational modeling, anxiety, reaction time, reliability statistics, convergent validity, automated intervention
- MeSH terms
- Attentional Bias, Adult, Anxiety Disorders, Behavior Therapy, Cerebral Cortex, Follow-Up Studies, Humans, Magnetic Resonance Imaging, Models, Theoretical, Neuropsychological Tests, Psychomotor Performance, Reproducibility of Results
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