A Queuing Network Model for Visual Search and Menu Selection

Abstract

Random menu search is a task component involved in many human-machine interfaces and has been modeled with various cognitive models including ACT-R and EPIC. Based on a review of empirical data in menu search and strengths and limitations of existing models, this article proposes a model that is based on the queueing network approach, which has been successfully applied in some other task domains (e.g., response time, driver performance). The queueing network model for random menu selection was implemented and evaluated through model simulation. In contrast to existing models that rely on multiple task-specific strategies to account for performance and eye movement data, the queueing network model uses only one strategy already employed in an existing cognitive model to account for the same data. The value of this ”minimal task strategy” approach for modeling complex menu search tasks is discussed, based on the reported findings of the queueing network model and a comparison to existing models.

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