Cognition and control in schizophrenia: A computational model of dopamine and prefrontal function

Todd S. Braver, Deanna M. Barch, Jonathan D. Cohen

Research output: Contribution to journalArticlepeer-review

382 Scopus citations


Behavioral deficits suffered by patients with schizophrenia in a wide array of cognitive domains can be conceptualized as failures of cognitive control, due to an impaired ability to internally represent, maintain, and update context information. A theory is described that postulates a single neurobiological mechanism for these disturbances, involving dysfunctional interactions between the dopamine neurotransmitter system and the prefrontal cortex. Specifically, it is hypothesized that in schizophrenia, there is increased noise in the activity of the dopamine system, leading to abnormal 'gating' of information into prefrontal cortex. The theory is implemented as an explicit connectionist computational model that incorporates the roles of both dopamine and prefrontal cortex in cognitive control. A simulation is presented of behavioral performance in a version of the Continuous Performance Test specifically adapted to measure critical aspects of cognitive control function. Schizophrenia patients exhibit clear behavioral deficits on this task that reflect impairments in both the maintenance and updating of context information. The simulation results suggest that the model can successfully account for these impairments in terms of abnormal dopamine activity. This theory provides a potential point of contact between research on the neurobiological and psychological aspects of schizophrenia, by illustrating how a particular physiological disturbance might lead to precise and quantifiable consequences for behavior. Copyright (C) 1999 Society of Biological Psychiatry.

Original languageEnglish (US)
Pages (from-to)312-328
Number of pages17
JournalBiological Psychiatry
Issue number3
StatePublished - Aug 1 1999

All Science Journal Classification (ASJC) codes

  • Biological Psychiatry


  • Computational model
  • Inhibition
  • Neural network
  • Neuromodulation
  • Reinforcement learning
  • Working memory


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