Schizophrenia is a highly disabling illness that impacts 0.5-1% of Americans. The disability of the illness is more strongly associated with cognitive deficits and negative symptoms than it is with the positive symptoms of the illness including hallucinations and delusions. Negative symptoms are generally defined as the absence of normal function, but the actual mechanisms involved in generating this absence have remained unknown, thereby stifling rational treatment development. The overarching goal of this application is to fundamentally alter the understanding of negative symptoms by rigorously testing a highly specific hypothesis about the origins of avolition/anhedonia in people with schizophrenia. This hypothesis has been formalized in a computational model that suggests that people with schizophrenia have a deficit in the ability to represent the positive expected value of stimuli and response alternatives, coupled with an intact ability to learn from aversive outcomes. This deficit in representing value is also thought to lead to reduced exploration of behavioral alternatives when uncertain about the likely payoffs of different choices. The project uses a program of behavioral experiments to test this hypothesis in the areas of learning from outcomes and decision making. In addition, we will explore the relationship between this deficit and current cognitive psychological models of the causes of negative symptoms, as well as the importance of this deficit for the prediction of successful outcome from a behavioral treatment approach that uses reinforcement to shape behavior. This computational approach also leads to a highly specific hypothesis about the neural mechanisms that are implicated in a deficit in representing expected value. To address this hypothesis, we will take advantage of the temporal resolution of EEG to test whether abnormalities in neural activity occur at the time of decision, as predicted, or instead occur at the time of feedback delivery, as would be expected if patients were unable to use the dopamine system to signal positive prediction errors when outcomes are better than expected. The goal of both the behavioral and neurophysiological studies is to provide an explicit, mechanistic understanding of negative symptoms and evaluate the application of this approach to current treatment approaches. Because reward circuitry is highly conserved across mammalian species, it should be possible to """"""""back translate"""""""" to the animal models needed to guide drug development research.

Public Health Relevance

Many people with schizophrenia suffer from impairments in motivation and drive that result in substantial vocational and social disability. The cause of thes motivational impairments remains largely unknown, slowing the process of treatment development. The goal of this proposal is to test a highly specific hypothesis about the causes of motivational impairment, and determine if this impairment impacts current treatment approaches for negative symptoms.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BBBP-V (03))
Program Officer
Meinecke, Douglas L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Maryland Baltimore
Schools of Medicine
United States
Zip Code
Catalano, Lauren T; Heerey, Erin A; Gold, James M (2018) The valuation of social rewards in schizophrenia. J Abnorm Psychol 127:602-611
Westbrook, Andrew; Frank, Michael (2018) Dopamine and Proximity in Motivation and Cognitive Control. Curr Opin Behav Sci 22:28-34
Nassar, Matthew R; Helmers, Julie C; Frank, Michael J (2018) Chunking as a rational strategy for lossy data compression in visual working memory. Psychol Rev 125:486-511
Collins, Anne G E; Albrecht, Matthew A; Waltz, James A et al. (2017) Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia. Biol Psychiatry 82:431-439
Maia, Tiago V; Frank, Michael J (2017) An Integrative Perspective on the Role of Dopamine in Schizophrenia. Biol Psychiatry 81:52-66
Pedersen, Mads Lund; Frank, Michael J; Biele, Guido (2017) The drift diffusion model as the choice rule in reinforcement learning. Psychon Bull Rev 24:1234-1251
Waltz, James A (2017) The neural underpinnings of cognitive flexibility and their disruption in psychotic illness. Neuroscience 345:203-217
Swart, Jennifer C; Froböse, Monja I; Cook, Jennifer L et al. (2017) Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action. Elife 6:
Collins, Anne G E; Ciullo, Brittany; Frank, Michael J et al. (2017) Working Memory Load Strengthens Reward Prediction Errors. J Neurosci 37:4332-4342
Maia, Tiago V; Huys, Quentin J M; Frank, Michael J (2017) Theory-Based Computational Psychiatry. Biol Psychiatry 82:382-384

Showing the most recent 10 out of 85 publications