Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia

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.

Haste or Speed? Alterations in the Impact of Incentive Cues on Task Performance in Remitted and Depressed Patients With Bipolar Disorder

“…The present study was adequately powered to detect effects of similar magnitude to those reported in previous studies of psychopathology with this task [d = 0.77–1.29: ( 13 , 14 )]. These findings contribute to a growing literature describing areas of intact reward processing in MDD ( 33 ), and provide contrast with paradigms which appear to be more reliably sensitive ( 34 , 35 ). Although details of the experimental design within the reinforcement learning framework may be relevant to understanding these discrepancies, it may also be that theoretical accounts of motivation that extend beyond a focus on the arrangement of stimuli, responses requirements and reinforcement contingencies may be insightful in determining why some paradigms are sensitive to differences related to depression and some are not ( 36 ).…”

Translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders

“…Computational models can make specific predictions about the underlying mechanisms that drive behaviour and enable a more fine-grained view of decision-making and how it changes in pathological states (Robinson and Chase, 2017). One such model – the drift diffusion model (DDM) – has been applied to rodent data on this task (Hales et al ., 2016).…”

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

“…Recent studies have leveraged computational modelling to capture associations between learning processes and psychiatrically relevant dimensions in non-clinical samples [5][6][7][8] , as well as in clinical conditions ranging from anxiety and depression to psychosis [9][10][11][12] . A common finding across studies is that of altered learning rates, where psychopathology is linked to inappropriate weighting of evidence when updating value estimates 7,13,14 . Notably, there is evidence suggesting that people with clinically significant symptoms of anxiety and depression show biased learning as a function of the valence of information, updating faster in response to negative than positive outcomes presented as monetary losses and gains 12 , a bias that might engender a negative view of the environment.…”