Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia

Abstract

Deficits in reward learning are core symptoms across many mental disorders. Recent work suggests that such learning impairments arise by a diminished ability to use reward history to guide behaviour, but the neuro-computational mechanisms through which these impairments emerge remain unclear. Moreover, limited work has taken a transdiagnostic approach to investigate whether the psychological and neural mechanisms that give rise to learning deficits are shared across forms of psychopathology. To provide insight into this issue, we explored probabilistic reward learning in patients diagnosed with major depressive disorder (n = 33) or schizophrenia (n = 24) and 33 matched healthy controls by combining computational modelling and single-trial EEG regression. In our task, participants had to integrate the reward history of a stimulus to decide whether it is worthwhile to gamble on it. Adaptive learning in this task is achieved through dynamic learning rates that are maximal on the first encounters with a given stimulus and decay with increasing stimulus repetitions. Hence, over the course of learning, choice preferences would ideally stabilize and be less susceptible to misleading information. We show evidence of reduced learning dynamics, whereby both patient groups demonstrated hypersensitive learning (i.e. less decaying learning rates), rendering their choices more susceptible to misleading feedback. Moreover, there was a schizophrenia-specific approach bias and a depression-specific heightened sensitivity to disconfirmational feedback (factual losses and counterfactual wins). The inflexible learning in both patient groups was accompanied by altered neural processing, including no tracking of expected values in either patient group. Taken together, our results thus provide evidence that reduced trial-by-trial learning dynamics reflect a convergent deficit across depression and schizophrenia. Moreover, we identified disorder distinct learning deficits.

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance

“…97 healthy young adults completed the probabilistic learning task (Figure 2) (Fischer & Ullsperger, 2013;Jocham et al, 2014;Kirschner et al, 2022;Kirschner et al, 2023) on three separate sessions, an initial drug-free session, and after PL and MA. The study followed a double-blinded cross-over design, whereby 50% of participants received MA first, and 50% of participants PL first.…”

“…To study learning dynamics participants completed a reversal variant of an established probabilistic learning task (Fischer & Ullsperger, 2013;Jocham et al, 2014;Kirschner et al, 2022;Kirschner et al, 2023). Participants completed the task three times: in a baseline session without drug, and after PL and after oral MA (20 mg) administration.…”

“…In environments that include discontinuities (changepoints) and noise (probabilistic errors), optimal learning requires increased weighting of surprising information during periods of change and ignoring surprising events during periods of stability. A burgeoning literature suggests that humans are able to calibrate learning rates according to the statistical content of new information (Behrens et al, 2007;Cook et al, 2019;Diederen et al, 2016;Nassar et al, 2019;Nassar et al, 2010;Razmi & Nassar, 2022), albeit to varying degrees (Kirschner et al, 2022;Kirschner et al, 2023;Nassar et al, 2016;Nassar et al, 2012;Nassar et al, 2021).…”

“…The task used to study adaptive learning dynamics was a reversal variant of an established probabilistic learning task (Fischer & Ullsperger, 2013;Jocham et al, 2014;Kirschner et al, 2022;Kirschner et al, 2023). On each trial, subjects made a choice to either gamble or avoid gambling on a probabilistic outcome, in response to a stimulus presented in the middle of the screen (see Figure 2A).…”

Pharmacological Enhancement of Dopamine Neurotransmission Does Not Affect Illusory Pattern Perception

“…A large body of research suggests that dopamine modulates striatal coding of prediction errors to enable learning ( Pessiglione et al, 2006 ; Glimcher, 2011 ; Schlagenhauf et al, 2013 ; Schultz, 2016 ; Basanisi et al, 2023 ). Imaging studies in human schizophrenic participants have reported striatal dysfunction during learning ( Schlagenhauf et al, 2014 ; Katthagen et al, 2020 ) and aberrant learning from feedback accompanied by altered EEG correlates ( Kirschner et al, 2024 ). In monkeys, midbrain dopamine neurons have been found to code reward size but also uncertainty or confidence during perceptual decision-making ( de Lafuente and Romo, 2011 ; Lak et al, 2017 ).…”