Visual search and mouse-pointing in labeled versus unlabeled two-dimensional visual hierarchies

Hornof, Anthony J.

doi:10.1145/502907.502908

Cited by 49 publications

(32 citation statements)

References 34 publications

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“…The experiment was a study on visual search and mouse pointing in two-dimensional visual hierarchies and replicated the experiment reported by Hornof (2001), with two exceptions: (1) The experimental procedure was modified to accommodate eye tracking, and (2) a different computer monitor was used (the pixels per horizontal or vertical centimeter increased from 33 to 39).…”

Section: The Experimental Design With the Implicit Rflmentioning

confidence: 99%

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Cleaning up systematic error in eye-tracking data by using required fixation locations

Hornof

Halverson

2002

Behavior Research Methods, Instruments, & Computers

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159

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In the course of running an eye-tracking experiment, one computer system or subsystem typically presents the stimuli to the participant and records manual responses, and another collects the eye movement data, with little interaction between the two during the course of the experiment. This article demonstrates how the two systems can interact with each other to facilitate a richer set of experimental designs and applications and to produce more accurate eye tracking data. In an eye-tracking study, a participant is periodically instructed to look at specific screen locations, or explicit required fixation locations (RFLs), in order to calibrate the eye tracker to the participant. The design of an experimental procedure will also often produce a number of implicit RFLs-screen locations that the participant must look at within a certain window of time or at a certain moment in order to successfully and correctly accomplish a task, but without explicit instructions to fixate those locations. In these windows of time or at these moments, the disparity between the fixations recorded by the eye tracker and the screen locations corresponding to implicit RFLs can be examined, and the results of the comparison can be used for a variety of purposes. This article shows how the disparity can be used to monitor the deterioration in the accuracy of the eye tracker calibration and to automatically invoke a recalibration procedure when necessary.This article also demonstrates how the disparity will vary across screen regions and participants and how each participant's unique error signature can be used to reduce the systematic error in the eye movement data collected for that participant.

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Section: The Experimental Design With the Implicit Rflmentioning

confidence: 99%

“…The experiment was designed to separate visual search time from target selection time by imposing a pointcompletion deadline (Hornof, 2001). Once the participant started moving the mouse from the precue position, he or she had a limited amount of time to click on the target before the trial was interrupted.…”

Section: The Experimental Design With the Implicit Rflmentioning

confidence: 99%

Cleaning up systematic error in eye-tracking data by using required fixation locations

Hornof

Halverson

2002

Behavior Research Methods, Instruments, & Computers

Self Cite

159

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“…That would enable us to create applications that allow users to see an overview of a large set of information, but grouped in some meaningful way. Some research in human-computer interaction shows that this kind of grouping of search results based on meaningful categories, for instance, can help users understand those search results [4].…”

Section: Algorithms For Quantum Treemapsmentioning

confidence: 99%

Ordered and quantum treemaps

2002

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Treemaps, a space-filling me thod of visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, fail to preserve order of the underlying data, and create layouts that are difficult to visually search. In addition, continuous treemap algorithms are not suitable for displaying quantum-sized objects within them, such as images. This paper introduces several new treemap algorithms, which address these shortcomings. In addition, we show a new application of these treemaps, using them to present groups of images. The ordered treemap algorithms ensure that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials, we show that compared to other layout algorithms ordered treemaps are more stable while maintaining relatively favorable aspect ratios of the constituent rectangles. A second test set uses stock market data. The quantum treemap algorithms modify the layout of the continuous treemap algorithms to generate rectangles that are integral multiples of an input object size. The quantum treemap algorithm has been applied to PhotoMesa, an application that supports browsing of large numbers of images.

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“…Search time was separated from pointing-and-clicking time (hereafter referred to simply as pointing time) by means of a "point-completion deadline" (Hornof, 2001). Participants were instructed not to move the mouse until they found the target.…”

Section: Visual Search Taskmentioning

confidence: 99%

Cognitive Strategies for the Visual Search of Hierarchical Computer Displays

Hornof

2004

Human–Computer Interaction

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This article investigates the cognitive strategies that people use to search computer displays. Several different visual layouts are examined: unlabeled layouts that contain multiple groups of items but no group headings, labeled layouts in which items are grouped and each group has a useful heading, and a target-only layout that contains just one item. A number of plausible strategies were proposed for each layout. Each strategy was programmed into the EPIC cognitive architecture, producing models that simulate the human visual-perceptual, oculomotor, and cognitive processing required for the task. The models generate search time predictions. For unlabeled layouts, the mean layout search times are predicted by a purely random search strategy, and the more detailed positional search times are predicted by a noisy systematic strategy. The labeled layout search times are predicted by a hierarchical strategy in which first the group labels are systematically searched, and then the contents of the target group. The target-only layout search times are predicted by a strategy in which the eyes move directly to the sudden appearance of the target. The models demonstrate that human visual search performance can be explained largely in terms of the cognitive strategy

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Visual search and mouse-pointing in labeled versus unlabeled two-dimensional visual hierarchies

Cited by 49 publications

References 34 publications

Cleaning up systematic error in eye-tracking data by using required fixation locations

Cleaning up systematic error in eye-tracking data by using required fixation locations

Ordered and quantum treemaps

Cognitive Strategies for the Visual Search of Hierarchical Computer Displays

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