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Environmental factors and features that influence visual search in a 3D WIMP interface
Affiliation:1. School of Psychology and Clinical Language Sciences, University of Reading, Whiteknights Road, RG6 6AL, UK;2. Henley Business School, Department of Informatics, University of Reading, Whiteknights Road, RG6 6UD, UK;1. School of Electronics and Information, Northwestern Polytechnical University, 710129 Shaanxi, China;2. Visual Computing Department, Institute for Infocomm Research, 138632, Singapore;3. Department of Computer Science and Engineering, University of Oulu, 90014 Oulu, Finland;1. School of Electronic Information Engineering, Tianjin University, Tianjin, China;2. Department of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China;1. Cedars-Sinai Heart Institute, Los Angeles, California;2. Heart Center, Tokyo Bay Urayasu/Ichikawa Medical Center, Urayasu, Japan;3. University of California, Los Angeles, Los Angles, California;1. McGill University, Montreal, QC, Canada;2. The Lady Davis Institute for Medical Research, Montreal, QC, Canada
Abstract:The challenge of moving past the classic Window Icons Menus Pointer (WIMP) interface, i.e. by turning it ‘3D’, has resulted in much research and development. To evaluate the impact of 3D on the ‘finding a target picture in a folder’ task, we built a 3D WIMP interface that allowed the systematic manipulation of visual depth, visual aides, semantic category distribution of targets versus non-targets; and the detailed measurement of lower-level stimuli features. Across two separate experiments, one large sample web-based experiment, to understand associations, and one controlled lab environment, using eye tracking to understand user focus, we investigated how visual depth, use of visual aides, use of semantic categories, and lower-level stimuli features (i.e. contrast, colour and luminance) impact how successfully participants are able to search for, and detect, the target image. Moreover in the lab-based experiment, we captured pupillometry measurements to allow consideration of the influence of increasing cognitive load as a result of either an increasing number of items on the screen, or due to the inclusion of visual depth.Our findings showed that increasing the visible layers of depth, and inclusion of converging lines, did not impact target detection times, errors, or failure rates. Low-level features, including colour, luminance, and number of edges, did correlate with differences in target detection times, errors, and failure rates. Our results also revealed that semantic sorting algorithms significantly decreased target detection times. Increased semantic contrasts between a target and its neighbours correlated with an increase in detection errors. Finally, pupillometric data did not provide evidence of any correlation between the number of visible layers of depth and pupil size, however, using structural equation modelling, we demonstrated that cognitive load does influence detection failure rates when there is luminance contrasts between the target and its surrounding neighbours. Results suggest that WIMP interaction designers should consider stimulus-driven factors, which were shown to influence the efficiency with which a target icon can be found in a 3D WIMP interface.
Keywords:3D WIMP  Visual search  Cognitive load  Eye tracking  Perceptual sorting algorithms  Target detection
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