Abstract:
Temporal difference (TD) prediction error signal
models are instrumental in simulating brain function during
reinforcement learning (RL). Recent evidence suggests a
significant role of TD prediction error signals in the actionselection and action-execution brain networks. We introduce a
neurocomputational model that explores TD prediction error
signal variations for action-selection and action-execution. The
TD prediction error signal represents the dopamine
neurotransmitter the basal ganglia and prefrontal cortex brain
regions. The model incorporates dopamine genetic parameters
in the two networks (COMT gene for action-selection; DAT1
gene for action-execution) to generate four different parameter
combinations. The model simulation showed that TD signaling
in both networks plays a significant role in RL under optimal
conditions of medium, not high, TD signals. Moreover, each
parameter combination showed a unique pattern of RL,
corresponding with experimental data obtained using a
computer-based RL task
