Computational modelling of attentional selectivity in depression reveals perceptual deficits

Abstract

Depression is associated with broad deficits in cognitive control, including in visual selective attention tasks such as the flanker task. Previous computational modelling of depression and flanker task performance showed reduced preprotent response bias and reduced executive control efficiency in depression. However, this modelling did not account for accuracy performance, and could not therefore account for the full dynamics of attentional selectivity. Across 3 large-scale online experiments (one exploratory experiment followed by two confirmatory—and pre-registered experiments; Total N= 923) we measured attentional selectivity via the flanker task and obtained measures of depression symptomology as well as anhedonia. We then fit two computational models that account for the dynamics of attentional selectivity: The Dual-Stage Two Phase model, and the Shrinking Spotlight model. No behavioural measures were related to depression symptomology or anhedonia. However, a parameter of the Shrinking Spotlight model that indexes the strength of perceptual input was consistently negatively associated with the magnitude of depression symtomology. The findings provide evidence for deficits in perceptual representations in depression. We discuss the implications of this in relation to the hypothesis that perceptual deficits potentially exacerbate control deficits in depression.

Publication
Psychological Medicine, in press
Date
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