Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia

Author:

Kirschner Hans1ORCID,Nassar Matthew R23ORCID,Fischer Adrian G4,Frodl Thomas5678,Meyer-Lotz Gabriela5,Froböse Sören5,Seidenbecher Stephanie5,Klein Tilmann A19,Ullsperger Markus1789

Affiliation:

1. Institute of Psychology, Otto-von-Guericke University , D-39106 Magdeburg , Germany

2. Robert J. and Nancy D. Carney Institute for Brain Science, Brown University , Providence, RI 02912-1821 , USA

3. Department of Neuroscience, Brown University , Providence, RI 02912-1821 , USA

4. Department of Education and Psychology, Freie Universität Berlin , D-14195 Berlin , Germany

5. Department of Psychiatry and Psychotherapy, Otto-von-Guericke University , D-39106 Magdeburg , Germany

6. Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University , Aachen 52074 , Germany

7. German Center for Mental Health (DZPG) , D-39106 Magdeburg , Germany

8. Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle , D-39106 Magdeburg , Germany

9. Center for Behavioral Brain Sciences , D-39106 Magdeburg , Germany

Abstract

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.

Publisher

Oxford University Press (OUP)

Subject

Neurology (clinical)

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