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

doi:10.1016/j.biopsych.2018.09.022

Biological Psychiatry

Volume 85, Issue 7, 1 April 2019, Pages 606-612

https://doi.org/10.1016/j.biopsych.2018.09.022 Get rights and content

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.

Section snippets

Methods and Materials

Full methods and primary clinical findings from the randomized controlled trial (clinicaltrials.gov: NCT02303691) have been reported previously (23). In brief, 70 unmedicated patients reporting clinically elevated levels of trait anxiety and associated clinician-rated disability were randomized to receive active ABM (n = 49) or a sham control variant (n = 21). See Supplemental Table S1 for sample characteristics.

Model Fit

DDM models were a good fit for every participant’s datasets (for each assessment point and trial type) according to Kolmogorov-Smirnov tests, which assess the probability that empirical and predicted data distributions differ (all p ≥ .59; mean p = .983; SD = .017). Model fits did not differ by trial type, time point, or group (ABM vs. control) (p ≥ .18). Figure 1 illustrates the model fit for the empirical data distribution in a representative subject. See Supplemental Table S3 for descriptive

Discussion

The dot-probe task has been used in many hundreds of studies of affective conditions, spanning both internalizing [e.g., anxiety (7), depression (33), trauma (34), suicidality (35)] and externalizing [e.g., substance use (36), unhealthy eating (37)] conditions. The task assesses the clinically relevant construct of attention bias—or preferential allocation of attention to disorder-relevant stimuli—which is a feature of information processing believed to have wide-reaching effects on the

Acknowledgments and Disclosures

This research was supported by National Institutes of Health Career Development Grant No. K23MH100259 (to RBP).

We gratefully acknowledge Danielle Gilchrist, Logan Cummings, Simona Graur, and the study participants for their contributions to this work.

The authors report no biomedical financial interests or potential conflicts of interest.

ClinicalTrials.gov: Attention Bias Modification for Transdiagnostic Anxiety; https://clinicaltrials.gov/ct2/show/NCT02303691?term=NCT02303691&rank=1; NCT02303691

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Cognitive bias theories posit that generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are entwined with attention bias toward threats, commonly indexed by faster response time (RT) on threat-congruent (vs. threat-incongruent) trials on the visual dot probe. Moreover, although smartphone ecological momentary assessment (EMA) of the visual dot probe has been developed, their psychometric properties are understudied. This study thus aimed to assess the reliability of 8 smartphone-delivered visual dot probe attention bias and related indices in persons with and without GAD and SAD.
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Our visual dot probe EMA should have administered ≥320 trials to match best-practice guidelines based on similar laboratory studies.
Future research should strive to examine attention bias paradigms beyond the dot-probe task that evidenced meaningful test-retest reliability properties in laboratory and real-world naturalistic settings.
Applying a drift diffusion model to test the effect of oxytocin on attentional biases in body dysmorphic disorder
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Individuals with body dysmorphic disorder (BDD) display selective attentional biases to threat. Oxytocin is an endogenous neuropeptide proposed to modulate attentional salience in social contexts. We conducted a secondary analysis applying drift diffusion modeling (DDM) to test whether individuals with BDD would display an attentional bias to threat, and whether oxytocin would modulate this bias. Eighteen participants with BDD and 15 healthy controls received an oxytocin or placebo nasal spray during two study visits, in randomized order, and completed a modified spatial cueing paradigm. DDM successfully parsed distinct task components demonstrating a selective attentional bias to disgust versus neutral faces in BDD compared to controls in the placebo condition, and a main effect of oxytocin on exacerbating this bias across participants. There were no effects using mean reaction time measures. DDM may reveal insights about attentional biases by utilizing trial-wise information. Oxytocin may exacerbate attentional biases to threat in BDD.
Drift diffusion modeling successfully parsed distinct components of a modified spatial cueing task that assessed attentional biases in those with body dysmorphic disorder (BDD) and healthy controls that were missed in conventional analyses using mean reaction time measures. Individuals with BDD displayed an attentional preference for disgust versus neutral faces in the placebo condition, and oxytocin exacerbated this attentional bias.
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Social reticence in early childhood is characterized by shy and anxiously avoidant behavior, and it confers risk for pediatric anxiety disorders later in development. Aberrant threat processing may play a critical role in this association between early reticent behavior and later psychopathology. The goal of this longitudinal study is to characterize developmental trajectories of neural mechanisms underlying threat processing and relate these trajectories to associations between early-childhood social reticence and adolescent anxiety.
In this 16-year longitudinal study, social reticence was assessed from 2 to 7 years of age; anxiety symptoms and neural mechanisms during the dot-probe task were assessed at 10, 13, and 16 years of age. The sample included 144 participants: 71 children provided data at age 10 (43 girls, mean_age = 10.62), 85 at age 13 (46 girls, mean_age = 13.25), and 74 at age 16 (36 girls, mean_age = 16.27).
A significant interaction manifested among social reticence, anxiety symptoms, and time, on functional connectivity between the left amygdala and the left dorsolateral prefrontal cortex, voxelwise p < .001, clusterwise familywise error p < .05. Children with high social reticence showed a negative association between amygdala–dorsolateral prefrontal cortex connectivity and anxiety symptoms with age, compared to children with low social reticence, suggesting distinct neurodevelopmental pathways to anxiety.
These findings were present across all conditions, suggesting task-general effects in potential threat processing. Additionally, the timing of these neurodevelopmental pathways differed for children with high versus low social reticence, which could affect the timing of effective preventive interventions.
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An altered automatic processing of food stimuli may contribute to the maintenance of calorie restriction in patients with restrictive Anorexia Nervosa (AN-R). The present study aimed to assess whether task-irrelevant food distractors elicited a different interference effect in the motor actions of patients with AN-R compared to healthy controls (HC). 40 patients with acute AN-R and 40 HC performed an irrelevant distractor task in which they were required to perform a reaching movement from a starting point to a green dot, while an irrelevant distractor (a high-calorie food, low-calorie food, or neutral object) was presented in the middle of the screen. Mouse trajectories and response times (RT) were recorded. The analyses conducted on the kinematic variables revealed that while the trajectories of HC veered similarly toward the three categories of stimuli, AN-R patients showed an increased deviation toward low-calorie foods and a reduced deviation toward high-calorie foods compared to neutral objects. No significant results emerged as regards RT. The pattern of responses observed in patients with AN-R (deviation increased toward low-calorie and reduced toward high-calorie) is consistent with their eating habits and may thus represent an implicit mechanism sustaining calorie restriction in patients with AN-R.
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Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation—and on the best strategies to overcome these barriers—is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).
Neurocognitive Mechanisms Underlying Attention Bias Towards Pain: Evidence From a Drift-Diffusion Model and Event-Related Potentials
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Although combining computational modeling with event-related potentials (ERPs) can precisely characterize neurocognitive processes involved in attention bias, it has yet to be applied in the context of pain. Here, a hierarchical drift-diffusion model (DDM) along with ERPs was used to characterize the neurocognitive mechanisms underlying attention bias towards pain. A spatial cueing paradigm was adopted, in which the locations of targets were either validly or invalidly predicted by spatial cues related to pain or nonpain signals. DDM-derived nondecision time was shorter for targets validly cued by pain signals than by nonpain signals, thus indicating speeded attention engagement towards pain; drift rate was slower for targets invalidly cued by pain signals than by nonpain signals, reflecting slower attention disengagement from pain. The facilitated engagement towards pain was partially mediated by the enhanced lateralization of cue-evoked N1 amplitudes, which relate to the bottom-up, stimulus-driven processes of detecting threatening signals. On the other hand, the retarded disengagement from pain was partially mediated by the enhanced target-evoked anterior N2 amplitudes, which relate to the top-down, goal-driven processes of conflict monitoring and behavior regulating. These results demonstrated that engagement and disengagement components of pain-related attention bias are governed by distinct neurocognitive mechanisms. However, it remains possible that the findings are not pain-specific, but rather, are related to threat or aversiveness in general. This deserves to be further examined by adding a control stimulus modality.
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Archival ReportComputational Modeling Applied to the Dot-Probe Task Yields Improved Reliability and Mechanistic Insights

Abstract

Background

Methods

Results

Conclusions

Section snippets

Methods and Materials

Model Fit

Discussion

Acknowledgments and Disclosures

Biol Psychiatry

Vision Res

Clin Psychol Rev

J Math Psychol

J Chiropr Med

Trends Cogn Sci

Neuroimage

Computational psychiatry: A report from the 2017 NIMH workshop on opportunities and challenges

Mol Psychiatry

Computational psychiatry as a bridge from neuroscience to clinical applications

Nat Neurosci

The global burden of anxiety and mood disorders: Outting the European Study of the Epidemiology of Mental Disorders (ESEMeD) findings into perspective

J Clin Psychiatry

Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication

Arch Gen Psychiatry

Threat-related attentional bias in anxious and nonanxious individuals: A meta-analytic study

Psychol Bull

The attentional bias modification approach to anxiety intervention

Clin Psychol Sci

Empirical recommendations for improving the stability of the dot-probe task in clinical research

Psychol Assess

Unreliability as a threat to understanding psychopathology: the cautionary tale of attentional bias

J Abnorm Psychol

Capturing dynamics of biased attention: are new attention variability measures the way forward?

PLoS One

The diffusion decision model: theory and data for two-choice decision tasks

Neural Comp

Diffusion models in experimental psychology: A practical introduction

Exp Psychol

Attentional bias in emotional disorders

J Abnorm Psychol

Dysphoria and memory for emotional material: A diffusion-model analysis

Cogn Emot

Anxiety-related threat bias in recognition memory: The moderating effect of list composition and semantic-similarity effects

Cogn Emot

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