Individualising graphical layouts with predictive visual search models

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Flanders Make

Abstract

In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual search, and a computational approach to restructure graphical layouts for an individual user such that features on a new, unvisited interface can be found quicker. It explores four technical principles inspired by the human visual system (HVS) to predict expected positions of features and create individualised layout templates: (I) the interface with highest frequency is chosen as the template; (II) the interface with highest predicted recall probability (serial position curve) is chosen as the template; (III) the most probable locations for features across interfaces are chosen (visual statistical learning) to generate the template; (IV) based on a generative cognitive model, the most likely visual search locations for features are chosen (visual sampling modelling) to generate the template. Given a history of previously seen interfaces, we restructure the spatial layout of a new (unseen) interface with the goal of making its features more easily findable. The four HVS principles are implemented in Familiariser, a web browser that automatically restructures webpage layouts based on the visual history of the user. Evaluation of Familiariser (using visual statistical learning) with users provides first evidence that our approach reduces visual search time by over 10%, and number of eye-gaze fixations by over 20%, during web browsing tasks.

Details

Original languageEnglish
Article number9
JournalACM Transactions on Interactive Intelligent Systems
Volume10
Issue number1
Publication statusPublished - 1 Oct 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Adaptive user interfaces, Computational design, Graphical layouts, Visual search

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