Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA.
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA.
Biol Psychiatry. 2019 Apr 1;85(7):606-612. doi: 10.1016/j.biopsych.2018.09.022. Epub 2018 Oct 5.
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.
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.
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.
Computational modeling of attention bias task data may represent a new way forward to improve precision.
在许多形式的精神病理学中，注意力的偏向模式被认为是关键机制，并由此产生了旨在改变这种注意力偏好的自动化机械干预措施。然而，由于广泛使用的注意力定量方法存在局限性，导致测量精度不高，这在很大程度上阻碍了进展。
在一组临床焦虑的患者（n=70）中，我们应用了一种经过充分验证的计算建模方法（漂移-扩散模型），对来自二选一“点探测任务”的试验水平反应时间数据进行分析，该任务是迄今为止数百项注意力偏向研究中使用的主要范式，用于对任务表现的不同成分进行建模。
虽然漂移-扩散模型得出的注意力偏向指数与之前的方法（例如，传统的偏向得分、眼动追踪）具有收敛效度，但我们新颖的分析方法大大提高了分半信度，适度提高了重测信度，并揭示了关于注意力偏向的神经基础以及自动化注意力再训练程序的影响的新的机制见解。
对注意力偏向任务数据的计算建模可能代表着提高精度的新途径。
