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.
