Cognitive Neurodynamics

, 2:207 | Cite as

Impaired associative learning in schizophrenia: behavioral and computational studies

  • Vaibhav A. Diwadkar
  • Brad Flaugher
  • Trevor Jones
  • László Zalányi
  • Balázs Ujfalussy
  • Matcheri S. Keshavan
  • Péter Érdi
Research Article


Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.


Learning dynamics Schizophrenia Computational models 



This work was supported by MH68680 (VAD), the Children’s Research Center of Michigan (CRCM) and the Joe Young Sr. Fund to the Dept of Psychiatry & Behavioral Neuroscience. We thank R. Marciano and C. Zajac-Benitez for assistance in patient recruitment and assessment, E. Murphy, S. Chakraborty, M. Benton and D. Khatib for assistance in conducting the experiments, N. Seraji-Borzorgzad and S. Fedorov for programming assistance, and J. Stanley for helpful discussions. PÉ thanks the Henry Luce Foundation for general support.


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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Vaibhav A. Diwadkar
    • 1
    • 2
  • Brad Flaugher
    • 3
    • 4
  • Trevor Jones
    • 3
    • 4
  • László Zalányi
    • 3
    • 4
    • 5
  • Balázs Ujfalussy
    • 5
  • Matcheri S. Keshavan
    • 1
    • 2
  • Péter Érdi
    • 3
    • 4
    • 5
  1. 1.Department of Psychiatry & Behavioral NeuroscienceWayne State University SOMDetroitUSA
  2. 2.Department of PsychiatryUniversity of Pittsburgh SOMPittsburghUSA
  3. 3.Department of Physics, Center for Complex SystemsKalamazoo CollegeKalamazooUSA
  4. 4.Department of Psychology, Center for Complex Systems StudiesKalamazoo CollegeKalamazooUSA
  5. 5.Computational Neuroscience Group, Department of BiophysicsKFKIBudapestHungary

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