Affiliation:
1. Arizona State University, Mesa, AZ
2. Michigan Technological University, Houghton, MI
Abstract
The SEEV model of visual scanning offers a quick and easy way of evaluating the attentional demands of various tasks and displays. A SEEV model can be developed without relying on complicated mathematical software or background, making the conceptual model highly accessible. Implementation of SEEV modeling can further be improved by easing the process of running simulations and providing actionable information. In this paper, we showcase the SEEV Modeler, a GUI-based prototype of the computational SEEV model that lowers the technical barriers for human factors practitioners. Furthermore, the prototype’s ability to predict eye movements in dynamic driving scenarios was tested, with an emphasis on the impacts of the attention shifting effort and inhibition of return (IOR) on the model’s prediction performance. The SEEV Modeler produced model fits comparable to those of previous mathematical modeling approaches but also revealed limitations and practical issues to be addressed in the final version.
Subject
General Medicine,General Chemistry
Cited by
2 articles.
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