Optimization in Brain? - Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms

Here we present a novel approach to model brain and behavioral phenomena of multitask performance, which integrates queuing networks with reinforcement learning algorithms. Using the queuing network as the static platform of brain structure and reinforcement learning as the dynamic algorithm to quantify the learning process, this model successfully accounts for several behavioral 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 outcomes of the natural operations of the human information processing queuing network.