Understanding Visual Saliency in Mobile User Interfaces

TL;DR

This study investigates what attracts visual attention in mobile UIs, revealing a top-left bias and limited effectiveness of classic saliency models, while providing a new annotated dataset for future research.

Contribution

It offers the first annotated dataset for mobile UI saliency and shows that data-driven models outperform classic saliency models when trained on this data.

Findings

Strong top-left bias in user gaze patterns

Classic saliency models perform poorly on mobile UIs

Data-driven models improve significantly with dataset-specific training

Abstract

For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on desktop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the top-left corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.