Computational Modeling Applied to the Dot-Probe Task Yields Improved Reliability and Mechanistic Insights.

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.
(Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)