Building on our previous neurocomputational models of basal ganglia and hippocampal region function
(and their modulation by dopamine and acetylcholine, respectively), we show here how an integration of
these models can inform our understanding of the interaction between the basal ganglia and hippocampal
region in associative learning and transfer generalization across various patient populations. As a
common test bed for exploring interactions between these brain regions and neuromodulators, we focus
on the acquired equivalence task, an associative learning paradigm in which stimuli that have been associated
with the same outcome acquire a functional similarity such that subsequent generalization
between these stimuli increases. This task has been used to test cognitive dysfunction in various patient
populations with damages to the hippocampal region and basal ganglia, including studies of patients
with Parkinson’s disease (PD), schizophrenia, basal forebrain amnesia, and hippocampal atrophy. Simulation
results show that damage to the hippocampal region—as in patients with hippocampal atrophy
(HA), hypoxia, mild Alzheimer’s (AD), or schizophrenia—leads to intact associative learning but impaired
transfer generalization performance. Moreover, the model demonstrates how PD and anterior communicating
artery (ACoA) aneurysm—two very different brain disorders that affect different neural mechanisms—
can have similar effects on acquired equivalence performance. In particular, the model shows
that simulating a loss of dopamine function in the basal ganglia module (as in PD) leads to slow acquisition
learning but intact transfer generalization. Similarly, the model shows that simulating the loss
of acetylcholine in the hippocampal region (as in ACoA aneurysm) also results in slower acquisition learning.
We argue from this that changes in associative learning of stimulus–action pathways (in the basal
ganglia) or changes in the learning of stimulus representations (in the hippocampal region) can have similar
functional effects.