2013
DOI: 10.1037/a0031628
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A diffusion model account of the relationship between the emotional flanker task and rumination and depression.

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 infor… Show more

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Cited by 83 publications
(75 citation statements)
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“…Second, drift diffusion modelling is a general, neurobiologically plausible modelling approach (e.g., Shadlen & Kiani, 2013) that is not restricted to the modelling of time perception data, specifically. Finally, drift diffusion modelling has already proven useful in emotion research in helping to improve sensitive to detect effects in anxiety (White et al, 2010) and depression (Pe, Vandekerckhove, & Kuppens, 2013). In short, the future application Drift Diffusion Modelling promises rich insights into the effects of emotion on perceptual decision making.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Second, drift diffusion modelling is a general, neurobiologically plausible modelling approach (e.g., Shadlen & Kiani, 2013) that is not restricted to the modelling of time perception data, specifically. Finally, drift diffusion modelling has already proven useful in emotion research in helping to improve sensitive to detect effects in anxiety (White et al, 2010) and depression (Pe, Vandekerckhove, & Kuppens, 2013). In short, the future application Drift Diffusion Modelling promises rich insights into the effects of emotion on perceptual decision making.…”
Section: Discussionmentioning
confidence: 99%
“…The advantage of drift diffusion modelling over standard RT measures has recently been demonstrated for research into the 6 effects of emotional stimuli on basic cognitive processes. Specifically, diffusion modelling has been used to uncover: 1) a processing advantage for threat-related words in anxiety (White, Ratcliff, Vasey, & McKoon, 2010) and 2) facilitated processing due to negative distracters in depression (Pe, Vandekerckhove, & Kuppens, 2013). Here, I used diffusion modelling to test for increased temporal accumulation speed due to fear.…”
mentioning
confidence: 99%
“…There was no effect of sad expressions on the standard measures of RT and accuracy (although for the larger sample there was a small effect on d') and therefore, this research adds to the list of studies that have found that Diffusion Model can help identify process that are hard to detect using standard statistical methods (Krypotos, Beckers, Kindt, & Wagenmakers, 2015;Pe, Vandekerckhove, & Kuppens, 2013;White, Ratcliff, Vasey, & McKoon, 2010). For example, one study (White et al, 2010) recorded a consistent processing advantage for threatening words in high-anxious individuals despite the fact such effects were absent when the data were analysed using traditional analyses of RTs and accuracy.…”
Section: A 4bmentioning
confidence: 99%
“…For example, one study (White et al, 2010) recorded a consistent processing advantage for threatening words in high-anxious individuals despite the fact such effects were absent when the data were analysed using traditional analyses of RTs and accuracy. Another study (Pe et al, 2013) found that rumination and depression scores were associated with facilitated processing due to negative distracters but failed to find any effect using either accuracy or RTs as separate measures. Combined with the current results finding these studies demonstrate the usefulness of the Diffusion Modelling to study emotion and perhaps emotion disorders (White, Skokin, Carlos, & Weaver, 2016).…”
Section: A 4bmentioning
confidence: 99%
“…Examples indicating the wide range of applications for the diffusion model include analyses of cognitive processes in such typical experimental paradigms as the lexical decision task (e.g., Yap, Balota, & Tan, 2013), sequential priming paradigms (e.g., Voss, Rothermund, Gast, & Wentura, 2013), task switching (Schmitz & Voss, 2012, or prospective memory paradigms (e.g., Boywitt & Rummel, 2012). Other applications encompass social cognitive research (e.g., Germar, Schlemmer, Krug, Voss, & Mojzisch, 2014;Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007;Voss, Rothermund, & Brandtstädter, 2008), cognitive aging (e.g., McKoon & Ratcliff, 2013;Spaniol, Madden, & Voss, 2006), cognitive processes related to psychological disorders (e.g., Metin et al, 2013;Pe, Vandekerckhove, & Kuppens, 2013;White, Ratcliff, Vasey, & McKoon, 2010b), and other fields of psychology.…”
mentioning
confidence: 99%