Literature DB >> 23527499

A diffusion model account of the relationship between the emotional flanker task and rumination and depression.

Madeline Lee Pe1, Joachim Vandekerckhove, Peter Kuppens.   

Abstract

Although there exists a consensus that depression is characterized by preferential processing of negative information, empirical findings to support the association between depression and rumination on the one hand and selective attention for negative stimuli on the other hand have been elusive. We argue that one of the reasons for the inconsistent findings may be the use of aggregate measures of response times and accuracies to measure attentional bias. Diffusion model analysis allows to partial out the information processing component from other components that comprise the decision-making process. In this study, we applied a diffusion model to an emotional flanker task. Results revealed that when focusing on a negative target, both rumination and depression were associated with facilitated processing due to negative distracters, whereas only rumination was associated with less interference by positive distracters. After controlling for depression scores, rumination still predicted attentional bias for negative information, but depression scores were no longer predictive after controlling for rumination. Consistent with elusive findings in the literature, we did not find this pattern of results when using accuracy scores or mean response times. Our results suggest that rumination accounts for the attentional bias for negative information found in depression. PsycINFO Database Record (c) 2013 APA, all rights reserved.

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Year:  2013        PMID: 23527499     DOI: 10.1037/a0031628

Source DB:  PubMed          Journal:  Emotion        ISSN: 1528-3542


  31 in total

1.  A computational analysis of flanker interference in depression.

Authors:  D G Dillon; T Wiecki; P Pechtel; C Webb; F Goer; L Murray; M Trivedi; M Fava; P J McGrath; M Weissman; R Parsey; B Kurian; P Adams; T Carmody; S Weyandt; K Shores-Wilson; M Toups; M McInnis; M A Oquendo; C Cusin; P Deldin; G Bruder; D A Pizzagalli
Journal:  Psychol Med       Date:  2015-03-02       Impact factor: 7.723

2.  Electrophysiological correlates of the drift diffusion model in visual word recognition.

Authors:  Christina J Mueller; Corey N White; Lars Kuchinke
Journal:  Hum Brain Mapp       Date:  2017-07-31       Impact factor: 5.038

3.  Thou Shalt Not Bear False Witness Against Null Hypothesis Significance Testing.

Authors:  Miguel A García-Pérez
Journal:  Educ Psychol Meas       Date:  2016-10-05       Impact factor: 2.821

4.  Decision making on spatially continuous scales.

Authors:  Roger Ratcliff
Journal:  Psychol Rev       Date:  2018-11       Impact factor: 8.934

5.  Retest reliability of the parameters of the Ratcliff diffusion model.

Authors:  Veronika Lerche; Andreas Voss
Journal:  Psychol Res       Date:  2016-04-23

Review 6.  Affect and Decision Making: Insights and Predictions from Computational Models.

Authors:  Ian D Roberts; Cendri A Hutcherson
Journal:  Trends Cogn Sci       Date:  2019-05-16       Impact factor: 20.229

7.  Model-based assessment and neural correlates of spatial memory deficits in mild cognitive impairment.

Authors:  Alexander S Weigard; K Sathian; Benjamin M Hampstead
Journal:  Neuropsychologia       Date:  2019-11-05       Impact factor: 3.139

8.  Individual differences in emotion processing: how similar are diffusion model parameters across tasks?

Authors:  Christina J Mueller; Corey N White; Lars Kuchinke
Journal:  Psychol Res       Date:  2017-11-27

9.  Processing of face identity in the affective flanker task: a diffusion model analysis.

Authors:  Christina J Mueller; Lars Kuchinke
Journal:  Psychol Res       Date:  2015-08-08

10.  Affective updating ability and stressful events interact to prospectively predict increases in depressive symptoms over time.

Authors:  Madeline L Pe; Annette Brose; Ian H Gotlib; Peter Kuppens
Journal:  Emotion       Date:  2015-08-31
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