Applying the SEEV Model to Assess Where People Will Look: Practical Considerations

For safety-critical tasks, such as landing an airplane and driving through an intersection, operators must find critical information quickly, which depends upon where they are likely to look, specifically dwell percentages. These values can be estimated using the SEEV computational model which includes four primary factors: Salience , Effort , Expectancy , and Value . To assist the practitioner developing interfaces for these and other tasks, this paper describes each factor in detail, provides example hand calculations, and identifies computational software for complex analyses (e.g., the Automation Design Advisor Tool, the Adaptive Information Expectancy Model, the Human Efficiency Evaluator, MIDAS, Automation Design Advisor Tool, and Eijssen’s SEEV model). Getting access to this software can be challenging.

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Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2024-08-29
DOI
https://doi.org/10.1177/10711813241275074
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

Applying the SEEV Model to Assess Where People Will Look: Practical Considerations

Paul Green
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Human-Automation Interaction and Safety
article

Applying the SEEV Model to Assess Where People Will Look: Practical Considerations

Paul Green
article en

Abstract

For safety-critical tasks, such as landing an airplane and driving through an intersection, operators must find critical information quickly, which depends upon where they are likely to look, specifically dwell percentages. These values can be estimated using the SEEV computational model which includes four primary factors: Salience , Effort , Expectancy , and Value . To assist the practitioner developing interfaces for these and other tasks, this paper describes each factor in detail, provides example hand calculations, and identifies computational software for complex analyses (e.g., the Automation Design Advisor Tool, the Adaptive Information Expectancy Model, the Human Efficiency Evaluator, MIDAS, Automation Design Advisor Tool, and Eijssen’s SEEV model). Getting access to this software can be challenging.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 68(1)
University of Michigan (US)
Nissan North America
Industry, innovation and infrastructure
Openalex Percentile: Top 82%
Human-Automation Interaction and Safety
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