The Impact of Dynamic Icons on Mobile APP Interfaces: Evidence from EEG and Eye-tracking Signals
This study investigates the cognitive impact of dynamic icons in mobile interfaces by integrating electroencephalography (EEG) and eye-tracking technologies. Traditional research on mobile app interface design has relied mainly on questionnaire and eye-tracking methods for behavioral analysis. This research adds a new dimension by examining the neural mechanisms associated with dynamic icons. We employed EEG to analyze channel-wise power spectrum density (PSD), focusing on the alpha and theta frequency bands related to attention and working memory. Concurrently, eye-tracking data were analyzed through Areas of Interest (AOIs) and fixation metrics to assess visual attention patterns. The results indicate that dynamic icons significantly enhance neural activity, with a 15% increase in alpha band power and a 20% increase in theta band power compared to static icons. Additionally, eye-tracking data show a 30% increase in total fixation duration on AOIs containing dynamic icons, particularly in the left-top quarter of the mobile interface. This effect was observed without changes in the first fixation duration, suggesting that dynamic icons have a stronger impact on sustained attention rather than on initial capture. These findings highlight that dynamic icons not only attract and maintain visual attention more effectively but also enhance cognitive processing efficiency. This study provides valuable insights for optimizing mobile app interface design, emphasizing the benefits of incorporating dynamic elements to improve user engagement and interface effectiveness.
Authors
- Wanzeng Kong (ORCID: https://orcid.org/0000-0002-0113-6968)
- Lu Fang (ORCID: https://orcid.org/0000-0002-1176-4255)
- Ruizhe Yang
- Haojie Tao (ORCID: https://orcid.org/0009-0001-6607-5860)
- Xuanyu Jin (ORCID: https://orcid.org/0000-0001-5542-3340)
- Zhu Li (ORCID: https://orcid.org/0000-0002-8246-177X)
- Yan Li (ORCID: https://orcid.org/0000-0002-9274-2516)
- Jiangtao Qin (ORCID: https://orcid.org/0009-0003-8430-3286)
- Wenjie Hu (ORCID: https://orcid.org/0000-0002-1099-3080)
Institutions
- Suzhou University of Technology (CN)
- Hangzhou Dianzi University (CN)
Publication Details
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1109/bibm62325.2024.10822366
- Citations
- 1
- Primary Topic
- Gaze Tracking and Assistive Technology
- Type
- article
- Field-Weighted Citation Impact
- 0.37
Funders
- National Natural Science Foundation of China