A computational analysis of flanker interference in depression

Abstract

Background Depression is characterized by poor executive function, but—counterintuitively—it is associated with highly accurate performance on certain cognitively demanding tasks. The psychological mechanisms responsible for this paradoxical finding are unclear. To address this issue, we applied a drift diffusion model (DDM) to flanker task data from depressed and healthy adults participating in the multi-site Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study. Methods One hundred unmedicated, depressed adults and forty healthy controls completed a flanker task. We investigated the effect of flanker interference on accuracy and response time, and used the DDM to examine group differences in three cognitive processes: prepotent response bias (tendency to respond to the distracting flankers), response inhibition (necessary to resist prepotency), and executive control (required for execution of correct response on incongruent trials). Results Consistent with prior reports, depressed participants responded more slowly and accurately than controls on incongruent trials. The DDM indicated that although executive control was sluggish in depressed participants, this was more than offset by decreased prepotent response bias. Among the depressed participants, anhedonia was negatively correlated with a parameter indexing the speed of executive control (r = -0.28, p = 0.007). Conclusions Executive control was delayed in depression but this was counterbalanced by reduced prepotent response bias, demonstrating how participants with executive function deficits can nevertheless perform accurately in a cognitive control task. Drawing on data from neural network simulations, we speculate that these results may reflect tonically reduced striatal dopamine in depression.

An LBA account of decisions in the multiple object tracking task

“…Here we show that sequential sampling models can be fit to decision data from the MOT to reveal underlying decision strategies used by different groups. Further, since prior research has tended to focus on investigating cognitive deficits within certain populations, rather than proficiencies (e.g., Dillon et al, 2015;Forstmann et al, 2011;Heathcote et al, 2015;van Ravenzwaaij et al, 2012), it was opportunistic to model the decision strategies and processes from a tightly selected group of RAAF personnel.…”

“…With sequential sampling methods often enabling a deeper understanding of the decision making process and underlying strategy, we proposed an experiment which tested two divergent groups on varying levels of MOT. A large proportion of modelling studies that compare across groups have tended to focus on cognitive deficits associated with certain groups (e.g., aging populations; Forstmann et al, 2011;, schizophrenia;Heathcote et al, 2015;and depression;Dillon et al, 2015) rather than a "proficiency". However, our study aimed to compare a student control group to a group of highly trained Royal Australian Air Force (RAAF) personnel as a means of understanding potential differences in cognitive decision making processes.…”

“…Parameter shifts include the strategies undertaken by participants, such as being more fast or cautious in responding (Rae, Heathcote, Donkin, Averell, & Brown, 2014;Evans, Rae, Bushmakin, Rubin, & Brown, 2017), the speed in which different groups process information (Dutilh, Vandekerckhove, Tuerlinckx, & Wagenmakers, 2009;Ratcliff, Thapar, & McKoon, 2010), or a combination of these factors. Further examples of sequential sampling models have accounted for a range of decision-making data across age groups (Forstmann et al, 2011), personality groups (Evans et al, 2017), clinical groups (e.g., depression and schizophrenia;Dillon et al, 2015;Heathcote et al, 2015), and groups differing in blood alcohol levels (van Ravenzwaaij, Dutilh, & Wagenmakers, 2012).…”

Modeling the Effects of Attentional Cueing on Meditators

“…Some previous studies have suggested that drift rate is the DDM parameter that reflects stimulus congruency (Dillon et al 2015; Pe et al 2013; White et al 2011). However, in those studies, the decision threshold was not allowed to vary with stimulus congruency, and hence, it could not predict stimulus congruency.…”

“…Some previous studies showed that incongruent stimuli were associated with a lower drift rate than congruent stimuli (Dillon et al 2015; Pe et al 2013; White et al 2011), but in none of these studies the threshold was allowed to account for congruency effects. In another study where the decision threshold was allowed to vary between conditions (King et al 2012), this parameter accounted for congruency effects.…”