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The effects of information scent on visual search in the hyperbolic tree browser

 

The Hyperbolic Tree is a focus + context information visualization that has been developed to amplify users' ability to navigate large tree-structured information systems. Information scent is a theoretical construct that captures one kind of interaction between task and display. Information scent is provided by task-relevant display cues, such as node labels on a tree that influence a user's visual search behavior and navigation decisions. An empirical Accuracy of Scent (AOS) score was developed to characterize a set of tasks that required users to find (Retrieval Tasks) or compare (Comparison Tasks) information in tree structures. Two experiments investigated the effect of information scent (tasks with different AOS scores) on performance with the Hyperbolic Tree Browser and the Microsoft Windows File Browser, which is a widely available conventional browser. Experiment 1 found no overall difference in performance time between the two browsers, but did reveal a marginal interaction of information scent with browser performance on Retrieval Tasks. On high AOS tasks the Hyperbolic showed faster performance, but on low AOS tasks the Windows File Browser showed faster performance. Experiment 2 focused only on the Retrieval tasks and revealed that Hyperbolic Tree users examined more tree nodes at a faster rate and visually searched through the tree hierarchy at a faster rate than users of a Windows File Browser lookalike, however, visual search paths were shortened in dense areas of the Hyperbolic Tree display when information scent was low. Two processes appear to affect visual search in the Hyperbolic display: strong information scent improves visual search, and the crowding of targets in a compressed region degrades visual search especially when there is weak information scent.

 
citation

Pirolli, P. L. ; Card, S. K. ; Van Der Wege, M. The effects of information scent on visual search in the hyperbolic tree browser. ACM Transactions on Computer Human Interaction. 2003 March; 10 (1): 20-53.