Density Guides Visual Search: Sparse Groups are First even when Slower

How do people efficiently locate content in a display? We investigate the effect of text layout on how people decide which area of a display to search first. Using a visual search paradigm, participants were required to locate a known target within a two-column display, in which items were grouped into semantic clusters, and the physical distance between items varied. For ‘mixed’ trials, the distance between items in each column was varied. Results showed that participants preferred to search the sparser of the two columns first, even though they were faster at locating the target in the denser column. This finding suggests that participants were adopting an inefficient search strategy for locating the target item. Discussion focuses on the implications for models that assume people rationally adapt their search strategy to maximize the gain of task-relevant information over time.

[1]  Michael D. Byrne,et al.  Unintended effects: varying icon spacing changes users' visual search strategy , 2004, CHI.

[2]  Anthony J. Hornof,et al.  High-cost banner blindness: Ads increase perceived workload, hinder visual search, and are forgotten , 2005, TCHI.

[3]  Anthony J. Hornof,et al.  The effects of semantic grouping on visual search , 2008, CHI Extended Abstracts.

[4]  Edward Cutrell,et al.  What are you looking for?: an eye-tracking study of information usage in web search , 2007, CHI.

[5]  David E. Kieras,et al.  The persistent visual store as the locus of fixation memory in visual search tasks , 2011, Cognitive Systems Research.

[6]  David E. Kieras,et al.  An Overview of the EPIC Architecture for Cognition and Performance With Application to Human-Computer Interaction , 1997, Hum. Comput. Interact..

[7]  Richard M. Young,et al.  A Rational Model of the Effect of Information Scent on the Exploration of Menus , 2004, ICCM.

[8]  Andrew Howes,et al.  The adaptation of visual search strategy to expected information gain , 2008, CHI.

[9]  Anthony J. Hornof,et al.  An active vision computational model of visual search for human-computer interaction , 2008 .

[10]  Andrew Howes,et al.  Strategies for Guiding Interactive Search: An Empirical Investigation Into the Consequences of Label Relevance for Assessment and Selection , 2008, Hum. Comput. Interact..

[11]  Trey Hedden,et al.  Category norms as a function of culture and age: comparisons of item responses to 105 categories by american and chinese adults. , 2004, Psychology and aging.

[12]  Anthony J. Hornof,et al.  Visual search and mouse-pointing in labeled versus unlabeled two-dimensional visual hierarchies , 2001, TCHI.

[13]  Peter Pirolli,et al.  Information Foraging , 2009, Encyclopedia of Database Systems.

[14]  Meredith Ringel Morris,et al.  What do you see when you're surfing?: using eye tracking to predict salient regions of web pages , 2009, CHI.

[15]  J. H. Bertera,et al.  Eye movements and the span of the effective stimulus in visual search , 2000, Perception & psychophysics.

[16]  R. Näsänen,et al.  Eye movements in the visual search of word lists , 2002, Vision Research.

[17]  Wai-Tat Fu,et al.  SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web , 2007, Hum. Comput. Interact..

[18]  Anthony J. Hornof,et al.  Local Density Guides Visual Search: Sparse Groups are First and Faster , 2004 .