Adaptive learning can result in a failure to profit from good conditions: implications for understanding depression

Background and objectives: Depression is a major medical problem diagnosed in an increasing proportion of people and for which commonly prescribed psychoactive drugs are frequently ineffective. Development of treatment options may be facilitated by an evolutionary perspective; several adaptive reasons for proneness to depression have been proposed. A common feature of many explanations is that depressive behaviour is a way to avoid costly effort where benefits are small and/or unlikely. However, this viewpoint fails to explain why low mood persists when the situation improves. We investigate whether a behavioural rule that is adapted to a stochastically changing world can cause inactivity which appears similar to the effect of depression, in that it persists after the situation has improved. Methodology: We develop an adaptive learning model in which an individual has repeated choices of whether to invest costly effort that may result in a net benefit. Investing effort also provides information about the current conditions and rates of change of the conditions. Results: An individual following the optimal behavioural strategy may sometimes remain inactive when conditions are favourable (i.e. when it would be better to invest effort) when it is poorly informed about the current environmental state. Initially benign conditions can predispose an individual to inactivity after a relatively brief period of negative experiences. Conclusions and implications: Our approach suggests that the antecedent factors causing depressed behaviour could go much further back in an individual s history than is currently appreciated. The insights from our approach have implications for the ongoing debate about best treatment options for patients with depressive symptoms.