This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Melloy, B. J. Articles by Duchowski, A. T. Search for Related Content PubMed Articles by Melloy, B. J. Articles by Duchowski, A. T. Social Bookmarking                         What's this?

A Model of Extended, Semisystematic Visual Search

Clemson University, Clemson, South Carolina

Sourav Das

Clemson University, Clemson, South Carolina

Anand K. Gramopadhye

Clemson University, Clemson, South Carolina

Andrew T. Duchowski

Clemson University, Clemson, South Carolina

Objective: A model of semisystematic search was sought that could account for both memory retrieval and other performance-shaping factors. Background: Visual search is an important aspect of many examination and monitoring tasks. As a result, visual search performance has been the topic of many empirical investigations. These investigations have reported that individual search performance depends on participant factors such as search behavior, which has motivated the development of models of visual search that incorporate this behavior. Search behavior ranges from random to strictly systematic; variation in behavior is commonly assumed to be caused by differences in memory retrieval and search strategy. Methods: This model ultimately took the form of a discrete-time nonstationary Markov process. Results: It yields both performance and process measures that include accuracy, time to perception, task time, and coverage while avoiding the statistical difficulties inherent to simulations. In particular, it was seen that as the search behavior becomes more systematic, expected coverage and accuracy increase while expected task time decreases. Conclusion: In addition to explaining these outcomes and their interrelationships from a theoretical standpoint, the model can predict these outcomes in practice to a certain extent as it can create an envelope defined by best- and worst-case search performance. Application: The model also has the capability of supporting assessment. That is, it can be used to assess the effectiveness of an individual's search performance, and to provide possible explanations for this performance, through the use of one or more of the output measures.

Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 48, No. 3, 540-554 (2006)
DOI: 10.1518/001872006778606840


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?