Motivation and cognitive control in depression

Abstract

Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression.

Keywords: Anhedonia; Cognitive control; Controllability of environment; Depression; Effort; Motivation; Reward anticipation.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Figures

Figure 1

The schematic representation of the proposed framework. Depression-related changes in the outcome value (e.g. reduced reward anticipation), outcome controllability (e.g. lowered estimates of controllability), and effort costs (e.g. reduced effort exertion), lead to the reduced value of control. This leads to the lowered amounts of control being allocated. The expected value of control (EVC) for a signal of a given intensity is calculated as the sum of the values of each possible outcome weighted by the probability of reaching that outcome for the given signal. The cost of allocating control is subtracted from that sum. The figure was adapted from Shenhav et al., 2013 with permission from the authors.

Figure 2

Effects of control cost, reward sensitivity and control efficacy on simulated behavior of the EVC model. Behavior of model was simulated in the Stroop paradigm and COGED paradigm (see supplementary Materials for details). (A-C) The amount of cognitive control allocated in a Stroop task is shown as a function of control cost, sensitivity to reward and expected control efficacy. (D-F) The error rate on incongruent and congruent trials is shown as a function of the three model parameters. (G-I) The subjective value of a task in the COGED paradigm is plotted as a function of task difficulty for different values of control costs, sensitivity to reward and control efficacy.