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Queueing network modeling of elementary mental processes

Y Liu1

  • 1Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor 48109-2117, USA. yililiu@umich.edu

Psychological Review
|January 1, 1996
PubMed
Summary
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This study introduces queueing network models to understand mental processes by analyzing reaction time (RT). These models offer a flexible framework for exploring information transmission and system architecture in cognition.

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Mathematical Psychology

Background:

  • Reaction time (RT) is a key metric for inferring mental system configurations.
  • Existing models often focus on discrete information transmission or serial processing architectures.

Purpose of the Study:

  • To present a class of queueing network models for elementary mental processes.
  • To integrate temporal (discrete vs. continuous transmission) and architectural (serial vs. network) factors.

Main Methods:

  • Development of five types of queueing networks modeling elementary mental processes.
  • Analysis of predictions for reaction time (RT) behavior under different network configurations.
  • Re-examination of existing psychological process models using the new framework.

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Main Results:

  • Queueing network models encompass existing discrete and continuous serial models, as well as discrete network models.
  • The proposed models offer a broader range of temporal and architectural structures for mental processes.
  • The models provide a unified framework for empirical testing of cognitive architectures.

Conclusions:

  • Queueing network models provide a powerful and flexible tool for studying mental processes.
  • This approach allows for a more comprehensive understanding of the interplay between information transmission and system architecture.
  • The models are empirically testable and can advance theories in cognitive psychology.