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Model of visual search and selection time in linear menus

Published: 26 April 2014 Publication History

Abstract

This paper presents a novel mathematical model for visual search and selection time in linear menus. Assuming two visual search strategies, serial and directed, and a pointing sub-task, it captures the change of performance with five fac- tors: 1) menu length, 2) menu organization, 3) target position, 4) absence/presence of target, and 5) practice. The novel aspect is that the model is expressed as probability density distribution of gaze, which allows for deriving total selection time. We present novel data that replicates and extends the Nielsen menu selection paradigm and uses eye-tracking and mouse tracking to confirm model predictions. The same parametrization yielded a high fit to both menu selection time and gaze distributions. The model has the potential to improve menu designs by helping designers identify more effective solutions without conducting empirical studies.

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ZIP File (pn0750-file4.zip)

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    cover image ACM Conferences
    CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2014
    4206 pages
    ISBN:9781450324731
    DOI:10.1145/2556288
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 26 April 2014

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    Author Tags

    1. eye-tracking
    2. linear menus
    3. mathematical predictive models
    4. user performance
    5. visual search

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    April 26 - May 1, 2014
    Ontario, Toronto, Canada

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    CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;
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