Literature DB >> 34047824

Implementation of the diffusion model on dot-probe task performance in children with behavioral inhibition.

Shane Wise1, Cynthia Huang-Pollock2, Koraly Pérez-Edgar2.   

Abstract

Attentional bias to threat, the process of preferentially attending to potentially threatening environmental stimuli over neutral stimuli, is positively associated with behavioral inhibition (BI) and trait anxiety. However, the most used measure of attentional bias to threat, the dot-probe task, has been criticized for demonstrating poor reliability. The present study aimed to assess whether utilizing a sequential sampling model to describe performance could detect adequate test-retest reliability for the dot-probe task, demonstrate stronger cueing effects, and improve the association with neural signals of early attention. One hundred and twenty children aged 9-12 years completed the dot-probe task twice. During the second administration, event-related potentials (ERPs) were obtained as time-sensitive neural markers of attention. BI was not associated with traditional or diffusion model measures of performance. Traditional and diffusion model measures of performance were also not associated with N1, P2, or N2 ERP amplitude. There were main effects of Visit, in which RTs were faster and standard deviation of RT smaller during the second administration due to an increase in drift rate and a decrease in non-decision time. The traditional RT bias score (r = 0.06) and bias scores formed via diffusion model parameters (all r's < 0.40) all demonstrated poor reliability. Results confirm recommendations to move away from using the dot-probe task as the primary or sole index of attentional bias.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Mesh:

Year:  2021        PMID: 34047824      PMCID: PMC8627521          DOI: 10.1007/s00426-021-01532-3

Source DB:  PubMed          Journal:  Psychol Res        ISSN: 0340-0727


  52 in total

1.  Contextual cueing effects despite spatially cued target locations.

Authors:  Andrea Schankin; Anna Schubö
Journal:  Psychophysiology       Date:  2010-03-04       Impact factor: 4.016

2.  Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study.

Authors:  Yair Bar-Haim; Dominique Lamy; Lee Pergamin; Marian J Bakermans-Kranenburg; Marinus H van IJzendoorn
Journal:  Psychol Bull       Date:  2007-01       Impact factor: 17.737

3.  Using decision models to decompose anxiety-related bias in threat classification.

Authors:  Corey N White; Kimberly Skokin; Brandon Carlos; Alexandria Weaver
Journal:  Emotion       Date:  2015-10-12

4.  Response time modeling reveals multiple contextual cuing mechanisms.

Authors:  David K Sewell; Ben Colagiuri; Evan J Livesey
Journal:  Psychon Bull Rev       Date:  2018-10

5.  Bias in the brain: a diffusion model analysis of prior probability and potential payoff.

Authors:  Martijn J Mulder; Eric-Jan Wagenmakers; Roger Ratcliff; Wouter Boekel; Birte U Forstmann
Journal:  J Neurosci       Date:  2012-02-15       Impact factor: 6.167

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

Authors:  Rebecca B Price; Vanessa Brown; Greg J Siegle
Journal:  Biol Psychiatry       Date:  2018-10-05       Impact factor: 13.382

7.  Target absent trials in configural contextual cuing.

Authors:  Melina A Kunar; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2011-10       Impact factor: 2.199

8.  Electrophysiological evidence of attentional biases in social anxiety disorder.

Authors:  E M Mueller; S G Hofmann; D L Santesso; A E Meuret; S Bitran; D A Pizzagalli
Journal:  Psychol Med       Date:  2008-12-15       Impact factor: 7.723

9.  Cognitive processes facilitated by contextual cueing: evidence from event-related brain potentials.

Authors:  Andrea Schankin; Anna Schubö
Journal:  Psychophysiology       Date:  2009-05       Impact factor: 4.016

10.  From anxious youth to depressed adolescents: Prospective prediction of 2-year depression symptoms via attentional bias measures.

Authors:  Rebecca B Price; Dana Rosen; Greg J Siegle; Cecile D Ladouceur; Kevin Tang; Kristy Benoit Allen; Neal D Ryan; Ronald E Dahl; Erika E Forbes; Jennifer S Silk
Journal:  J Abnorm Psychol       Date:  2015-11-23
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