Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)
Quentin Huys, Joshua Vogelstein, Peter Dayan
Decision making lies at the very heart of many psychiatric diseases. It is also a central theoretical concern in a wide variety of ﬁelds and has undergone detailed, in-depth, analyses. We take as an example Major Depressive Disorder (MDD), applying insights from a Bayesian reinforcement learning framework. We focus on anhedonia and helplessness. Helplessness—a core element in the conceptual- izations of MDD that has lead to major advances in its treatment, pharmacolog- ical and neurobiological understanding—is formalized as a simple prior over the outcome entropy of actions in uncertain environments. Anhedonia, which is an equally fundamental aspect of the disease, is related to the effective reward size. These formulations allow for the design of speciﬁc tasks to measure anhedonia and helplessness behaviorally. We show that these behavioral measures capture explicit, questionnaire-based cognitions. We also provide evidence that these tasks may allow classiﬁcation of subjects into healthy and MDD groups based purely on a behavioural measure and avoiding any verbal reports.
There are strong ties between decision making and psychiatry, with maladaptive decisions and be- haviors being very prominent in people with psychiatric disorders. Depression is classically seen as following life events such as divorces and job losses. Longitudinal studies, however, have revealed that a signiﬁcant fraction of the stressors associated with depression do in fact follow MDD onset, and that they are likely due to maladaptive behaviors prominent in MDD (Kendler et al., 1999). Clinically effective ’talking’ therapies for MDD such as cognitive and dialectical behavior therapies (DeRubeis et al., 1999; Bortolotti et al., 2008; Gotlib and Hammen, 2002; Power, 2005) explicitly concentrate on altering patients’ maladaptive behaviors and decision making processes.
Decision making is a promising avenue into psychiatry for at least two more reasons. First, it offers powerful analytical tools. Control problems related to decision making are prevalent in a huge diversity of ﬁelds, ranging from ecology to economics, computer science and engineering. These ﬁelds have produced well-founded and thoroughly characterized frameworks within which many issues in decision making can be framed. Here, we will focus on framing issues identiﬁed in psychiatric settings within a normative decision making framework.
Its second major strength comes from its relationship to neurobiology, and particularly those neuro- modulatory systems which are powerfully affected by all major clinically effective pharmacothera- pies in psychiatry. The understanding of these systems has beneﬁted signiﬁcantly from theoretical accounts of optimal control such as reinforcement learning (Montague et al., 1996; Kapur and Rem- ington, 1996; Smith et al., 1999; Yu and Dayan, 2005; Dayan and Yu, 2006). Such accounts may be useful to identify in more speciﬁc terms the roles of the neuromodulators in psychiatry (Smith et al., 2004; Williams and Dayan, 2005; Moutoussis et al., 2008; Dayan and Huys, 2008).
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