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

Abstract

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.

Trial registration: ClinicalTrials.gov NCT02303691.

Keywords: Anxiety; Attention bias; Attention bias modification; Computational modeling; Computational psychiatry; Drift-diffusion model.

Conflict of interest statement

Financial Disclosures. Dr. Price, Ms. Brown, and Dr. Siegle reported no biomedical financial interests or potential conflicts of interest.

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

Figures

Figure 1.

Representative empirical and model-predicted cumulative distribution functions (CDFs) of trial-level behavioral performance on the dot-probe task, constructed from a single individual at baseline. For graphical purposes only, error trials (present in the top panel only) are represented as negative values reflecting the inverse of observed reaction time.

Figure 2.

Hierarchical linear regression equation plots depicting changes (slope) in extradecisional bias as a function of training group.

Figure 3:

Clusters where incongruent-minus-congruent fMRI contrast values negatively correlate with DDM-derived extradecisional bias scores across individuals (from whole-brain analysis with map-wise p<.05; voxel-wise p<.005). From left to right, panels display the following clusters from Results text: L temperoparietal junction (TPJ; n=121 voxels; peak voxel: x=−46, y=−69, z=38; R2=.28); dorsomedial prefrontal cortex (DMPFC; n=93 voxels; peak voxel: x=−1, y=45, z=14; R2=.20); ventral anterior cingulate cortex (vACC; n=96 voxels; peak voxel: x=5, y=15, z=−3; R2=.21). No significant clusters exhibiting positive correlations were found.