Information Technology Reference
In-Depth Information
Chapter 9
Optimization in Brain? - Modeling Human
Behavior and Brain Activation Patterns with
Queuing Network and Reinforcement Learning
Algorithms
Changxu Wu, Marc Berman, and Yili Liu
Abstract
Here we present a novel approach to model brain and behavioral phe-
nomena of multitask performance, which integrates queuing networks with re-
inforcement learning algorithms. Using the queuing network as the static plat-
form of brain structure and reinforcement learning as the dynamic algorithm to
quantify the learning process, this model successfully accounts for several behav-
ioral phenomena related to the learning process of transcription typing and the
psychological refractory period (PRP). This model also proposes brain changes
that may accompany the typing and PRP practice effects that could be tested
empirically with neuroimaging. All of the modeled phenomena emerged as out-
comes of the natural operations of the human information processing queuing
network.
9.1 Introduction
Elucidating the psychological and physiological processes that mediate cognitive
and behavioral performance has been an important topic for a long period of
time. This topic for many years was studied exclusively with behavioral tech-
niques, and models of behavioral performance had to be inferred exclusively from
behavioral data [13, 45]. Current researchers are now endowed with two addi-
