title: Queueing network modeling of elementary mental processes.
abstract: This article examines the use of reaction time (RT) to infer the possible configurations of mental systems and presents a class of queueing network models of elementary mental processes. The models consider the temporal issue of discrete versus continuous information transmission in conjunction with the architectural issue of serial versus network arrangement of mental processes. Five elementary but important types of queueing networks are described in detail with regard to their predictions for RT behavior, and they are used to re-examine existing models for psychological processes. As continuous-transmission networks in the general form, queueing network models include the existing discrete and continuous serial models and discrete network models as special cases, cover a broader range of temporal and architectural structures that mental processes might assume, and can be subjected to empirical tests.
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor 48109-2117, USA. yililiu@umich.edu
authors.forename: Y
authors.initials: Y
authors.lastname: Liu
mesh_terms: D005246:Feedback; D006801:Humans; D008606:Mental Processes; D016571:Neural Networks, Computer; D011930:Reaction Time