Understanding visual search in graphical user interfaces

How do we find items within graphical user interfaces (GUIs)? Current understanding of this issue relies on studies using symbol matrices, natural scenes, and other non-GUI stimuli. To understand whether the effects discovered in those environments extend to mobile, desktop, and web interfaces, this paper reports on visual search performance and eye movements with 900 real-world GUIs. In an eye-tracking study, participants ( N = 84 ) were given a cue (textual or image) describing a target to find within a GUI. The study found that the type of GUI, the absence/presence of the target, and cue type affected search time more than visual complexity did. We also compared visual search to free-viewing in GUIs, concluding that these two tasks are distinctly different. Synthesis of the results points to a Guess-Scan-Confirm pattern in visual search: in the first few fixations, gaze is frequently directed toward the top-left corner of the screen, a pattern possibly related to the top left being a statistically likely location of the target or of information that could aid in finding it; attention then gets more selectively guided, in line with the GUI’s structure and the features of the target; and, finally, the user must confirm whether the target has been identified or, instead, that no target is visible. The VSGUI10K eye-tracking dataset (10,282 trials) is released for study and modeling of visual search. • We present a large-scale study ( N = 84 ) of visual search in naturalistic GUIs. • Our results quantify the effects of several factors on search times. • Target absence, cue and GUI type have the largest effect on search times. • We propose that search can be divided into three main stages: Guess, Scan, Confirm. • We release an eye-tracking dataset VSGUI10K for study of visual search.

Authors

Institutions

Publication Details

Journal
International Journal of Human-Computer Studies
Published
2025-03-01
DOI
https://doi.org/10.1016/j.ijhcs.2025.103483
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding visual search in graphical user interfaces

Antti Oulasvirta, Yue Jiang, Aini Putkonen, Jussi Jokinen et al.
International Journal of Human-Computer Studies
Visual Attention and Saliency Detection
article

Understanding visual search in graphical user interfaces

Antti Oulasvirta, Yue Jiang, Aini Putkonen, Jussi Jokinen, Jingchun Zeng, Olli Tammilehto
article en

Abstract

How do we find items within graphical user interfaces (GUIs)? Current understanding of this issue relies on studies using symbol matrices, natural scenes, and other non-GUI stimuli. To understand whether the effects discovered in those environments extend to mobile, desktop, and web interfaces, this paper reports on visual search performance and eye movements with 900 real-world GUIs. In an eye-tracking study, participants ( N = 84 ) were given a cue (textual or image) describing a target to find within a GUI. The study found that the type of GUI, the absence/presence of the target, and cue type affected search time more than visual complexity did. We also compared visual search to free-viewing in GUIs, concluding that these two tasks are distinctly different. Synthesis of the results points to a Guess-Scan-Confirm pattern in visual search: in the first few fixations, gaze is frequently directed toward the top-left corner of the screen, a pattern possibly related to the top left being a statistically likely location of the target or of information that could aid in finding it; attention then gets more selectively guided, in line with the GUI’s structure and the features of the target; and, finally, the user must confirm whether the target has been identified or, instead, that no target is visible. The VSGUI10K eye-tracking dataset (10,282 trials) is released for study and modeling of visual search. • We present a large-scale study ( N = 84 ) of visual search in naturalistic GUIs. • Our results quantify the effects of several factors on search times. • Target absence, cue and GUI type have the largest effect on search times. • We propose that search can be divided into three main stages: Guess, Scan, Confirm. • We release an eye-tracking dataset VSGUI10K for study of visual search.

International Journal of Human-Computer StudiesVol. 199
University of Jyväskylä (FI), Aalto University (FI)
Academy of Finland, Aalto-Yliopisto, Finnish Center for Artificial Intelligence
Openalex Percentile: Top 97%
Visual Attention and Saliency Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.