Motivation and cognitive control in depression. (July 2019)

• Record Type: Journal Article
• Authors: Grahek, Ivan; Shenhav, Amitai; Musslick, Sebastian; Krebs, Ruth M.; Koster, Ernst H.W.
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 102(2019)
• Page Start: 371
• Page End: 381
• Publication Date: 2019-07
• Subjects: Depression -- Cognitive control -- Anhedonia -- Motivation -- Reward anticipation -- Effort -- Controllability of environment; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2019.04.011
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: Review of motivational and cognitive impairments in depression. Impairments in key components of motivation contribute to cognitive control deficits in depression. Proposed framework focuses on the reduced value of control in depression. Simulations provide predictions about the influence of motivational impairments on cognitive control. 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.