Abstract
It has been reported that decision making is impaired in suicide attempters. Decision making is a complex process and little is known about its different components. Yet, this information would help to understand the functioning of suicidal minds. In this study, the Prospect Valence-Learning (PVL) computational model was applied to the Iowa Gambling Task (IGT) to investigate and compare decision-making components in patients with affective disorder and with/without history of suicide attempts and in healthy controls. To this aim, 116 inpatients with current major depressive episode (among whom 62 suicide attempters) and 38 healthy controls were recruited. Decision-making performance was measured using the IGT. The Bayesian computational PVL model was applied to compare the feedback sensitivity, loss aversion, learning/memory, and choice consistency components of decision making in the different groups. Depressive symptomatology was assessed using the Beck Depression Inventory short form (BDI-SF). The total IGT net score and the loss aversion and learning/memory scores were lower in suicide attempters than in healthy controls. The choice consistency score was low in all patients (with/without suicide history) compared with healthy controls. Moreover, patients with high BDI score showed a positive relationship between the choice consistency score and suicide attempt. These findings suggest that decision-making impairment in depressed patients with and without suicidal history might be the result of underlying problems in feedback processing and task learning, which influence the building of long-term strategies. All these impairments should be targeted in therapeutic strategies for suicidal patients.