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Summary: Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009
978-1-4244-3553-1/09/$25.00 ©2009 IEEE
Abstract--Rule-guided behavior is essential in quickly
adapting to one's ever-changing environment. In particular,
learned rules can quickly be used in new contexts or applied to
new stimuli (which confers an advantage over restricting
learning to perceptual motor associations). Here, we propose
a new neurocomputational model of automaticity in rule-
guided behavior. The proposed model assumes two parallel
neural pathways corresponding to "naïve" and "expert" rule
use. The development of automaticity is characterized by a
transfer of control of rule-guided behavior from a pathway
mediated by the prefrontal cortex to a direct parietal -
premotor pathway. The model includes differential equations
that describe voltage changes in the relevant brain areas and
difference equations that describe the Hebbian learning. A
simulation shows that the model accounts for some critical
single-cell recording data from several key brain areas as well
as some important behavioral results.
I. INTRODUCTION
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