Abstract
Computational approaches are increasingly being used to model behavioral and neural processes in mood and anxiety disorders. Here we explore the extent to which the parameters of popular learning and decision-making models are implicated in anhedonic symptoms of major depression. We first highlight the parameters of reinforcement learning that have been implicated in anhedonia, focusing, in particular, on the role that choice variability (i.e., “temperature”) may play in explaining heterogeneity across previous findings. We then turn to neuroimaging findings implicating attenuated ventral striatum response in anhedonic responses and discuss possible causes of the heterogeneity in the literature. Taken together, the reviewed findings highlight the potential of the computational approach in teasing apart the observed heterogeneity in both behavioral and functional imaging results. Nevertheless, considerable challenges remain, and we conclude with five unresolved questions that seek to address issues highlighted by the reviewed data.
Highlights
Mood and anxiety disorders are a major worldwide health burden across individual, social, and economic levels (Beddington et al, 2008)
We focus on the application of computational modeling to anhedonia— diminished reward processing—in major depression
We review findings from two primary classes of models (Figure 1) that have been implicated in anhedonia: (a) reinforcement learning models and (b) reaction time models
Summary
Mood and anxiety disorders are a major worldwide health burden across individual, social, and economic levels (Beddington et al, 2008). Despite a number of effective therapies and growing neuroscientific understanding of disease processes, resistance to established treatment strategies remains high (Yonkers, Warshaw, Massion, & Keller, 1996) This may be, at least in part, because current clinical diagnoses of these disorders rely primarily on subjective symptoms and behaviors, while the goal of neuroscience is to understand objective (i.e., observer-independent) biological mechanisms. Mapping these two approaches onto one another is exceptionally difficult and fraught with potential bias but is critical if we want to improve our ability to develop new treatments and target current treatments more effectively. It has been suggested that computational modeling of behavior—the focus of this review—can provide a means of bridging the gap between observable symptoms and behavior to underlying neurobiological mechanisms (Huys, Maia, & Frank, 2016; Montague, Dolan, Friston, & Dayan, 2012)
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