Adaptive feature guidance: Modelling visual search with graphical layouts. Issue 136 (April 2020)

• Record Type: Journal Article
• Authors: Jokinen, Jussi P.P.; Wang, Zhenxin; Sarcar, Sayan; Oulasvirta, Antti; Ren, Xiangshi
• Is Part Of: International journal of human-computer studies. Issue 136(2020)
• Publication Date: 2020-04
• Subjects: Visual search -- Computational modelling -- Learning; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2019.102376
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: A computational model predicts how users visually search layouts, and how their search develops over time. The adaptive feature guidance model suggests that people adapt their search strategy optimally according to the design and their memory of it. The model accurately predicted human search behaviour over various layouts. A number of practical uses and design guidelines are proposed. Abstract: We present a computational model of visual search on graphical layouts. It assumes that the visual system is maximising expected utility when choosing where to fixate next. Three utility estimates are available for each visual search target: one by unguided perception only, and two, where perception is guided by long-term memory (location or visual feature). The system is adaptive, starting to rely more upon long-term memory when its estimates improve with experience. However, it needs to relapse back to perception-guided search if the layout changes. The model provides a tool for practitioners to evaluate how easy it is to find an item for a novice or an expert, and what happens if a layout is changed. The model suggests, for example, that (1) layouts that are visually homogeneous are harder to learn and more vulnerable to changes, (2) elements that are visually salient are easier to search and more robust to changes, and (3) moving a non-salient element far away from original location is particularly damaging. The model provided a good match with human data in a study with realistic graphical layouts.