Computational modelling of the speed–accuracy tradeoff: No evidence for an association with depression symptomatology

Abstract

Successful decision making often requires finding the right balance between the speed and accuracy of responding: Emphasising speed can lead to error-prone performance, yet emphasising accuracy leads to a slowing of performance. Such speed–accuracy tradeoffs (SATs) therefore require establishing appropriate response settings to optimise performance in response to changing environmental demands. Such strategic adaptation of response settings relies on the striatum component of the basal ganglia, an area implicated in depression. The current study explored the association between depression symptomatology and SAT performance. Two experiments presented participants with an SAT paradigm embedded within a simple decision-making task, together with measures of depression symptomatology. Experiment 1 (N = 349) was correlational, whereas Experiment 2 was a two-phase experiment where participants (N = 501) were first pre-screened on depression symptomatology and extreme-low and extreme-high responders (total N = 91) were invited to Phase 2. Behavioural data were modelled with a drift diffusion model. Behavioural data and associated diffusion modelling showed large and robust SAT effects. Emphasising accuracy led to an increase in boundary separation, an increase in drift rate, and an increase in non-decision time. However, the magnitude of the changes of these parameters with SAT instructions was not associated with measures of depression symptomatology. The results suggest that the strategic adaptation of response settings in response to environmental changes in speed–accuracy instructions does not appear to be associated with depression symptomatology.