Computational Link Between Motivational Factors and Cognitive Deficits in Depression

Abstract Background Cognitive deficits are a common symptom of depression and contribute significantly to the disabling effects of the disorder. Experimentally, they are observed as increased reaction times, increased error rates, and deficient performance adaptation after making errors or receiving adverse feedback, in multiple cognitive paradigms. In the current theoretical study we aimed to address the cause of these cognitive deficits. Methods We constructed computational models of optimal resource allocation in two cognitive tasks – Delayed Match to Sample (DMS), and Eriksen Flanker (EF). The models explicitly link performance feedback values and beliefs about task controllability with measures of cognitive performance including accuracy, reaction times, and post-error improvement in accuracy (PIA). We then introduced depression-related motivational changes – altered control belief and feedback values (representing learned helplessness, anhedonic valuation and negative bias) – to see if these factors can account for deficits in cognitive performance. Results In the DMS task, altered control belief and lower valuation of correct performance accounted for decreased accuracy and decreased PIA. In the EF task, altered control belief and lower correct performance valuation could explain increased response times, decreased accuracy and decreased error-related negativity (ERN) signal. Increased valuation of adverse feedback, on the other hand, was linked to increased accuracy and the ERN signal. Furthermore, in the EF task, different combinations of depression-related motivational factors led to different patterns of cognitive performance, which could offer a basis for stratification. Conclusions Our models offer an explicit computational and algorithmic bridge between the known depression-related motivation factors (learned helplessness, anhedonic valuation) and commonly observed cognitive deficits (increased reaction times, decreased performance accuracy, worse post-error adaptation), which contributes towards a better understanding of depression.