Computing the Uncontrollable: Insights from Computational Modelling of Learning and Choice in Depression

Abstract

The learned helplessness (LH) paradigm, developed in experimental animals, has had great influence on the development of models of mood and anxiety disorders. However, the insights from this paradigm have not always translated straightforwardly into human experimental work. In particular, instrumental contingency learning experiments yielded the contradictory finding of more accurate contingency knowledge in depressed individuals (“depressive realism”: DR). A growing literature involving the application of computational modelling applied to human learning studies suggest that possible sequelae of LH in humans may be manifest in one or more potential candidate reinforcement learning-based mechanisms. This analysis suggests that the DR effect is theoretically and empirically independent of LH, despite both LH and DR being related to depression. Overall, the review highlights recent developments within the complex literature of learning and choice in depression and proposes a variety of testable predictions which may serve to delineate more precisely the presence, and neural basis, of LH-like sequelae across social and non-social learning and choice paradigms in depressed patients.