Computational modelling of attentional selectivity in depression reveals perceptual deficits

Grange

Abstract

Background
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 pre-potent response bias and reduced executive control efficiency in depression. In the current study, we applied two computational models that account for the full dynamics of attentional selectivity.

Method
Across three 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 (SSP) model.

Results
No behavioural measures were related to depression symptomology or anhedonia. However, a parameter of the SSP model that indexes the strength of perceptual input was consistently negatively associated with the magnitude of depression symptomatology.

Conclusions
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.

Citation

Grange. (2020). Computational modelling of attentional selectivity in depression reveals perceptual deficits. Psychological Medicine, 904-913. https://doi.org/10.1017/S0033291720002652

• Acceptance Date: Jul 2, 2020
• Publication Date: Jul 27, 2020
• Journal: Psychological Medicine
• Print ISSN: 0033-2917
• Publisher: Cambridge University Press
• Pages: 904-913
• DOI: https://doi.org/10.1017/S0033291720002652
• Keywords: Depression; Cognitive Control; Cognitive Modelling
• Publisher URL: https://doi.org/10.1017/S0033291720002652