Understanding the effects of peripheral vision and muscle memory on in-vehicle touchscreen interactions

Eren, Ayse L.; Burnett, Gary; Large, David R.; Harvey, Catherine

Abstract

It is important to gain a better understanding of how drivers interact with in-vehicle touchscreens to help design interfaces to minimise “eyes off road” time. The study aimed to investigate the relative effects of two interaction mechanisms (peripheral vision - PV and muscle memory - MM) shown to be relevant to visual behaviour when driving, on the time to press different sized buttons (small 6x6cm, medium 10x10cm, large 14x14cm) on an in-vehicle touchscreen. Twenty-five participants took part in a driving simulator study. They were presented with a single, white, square button on the touchscreen on 24 successive trials. For MM conditions, participants wore a pair of glasses that blocked their peripheral vision and for PV conditions they were asked to keep their focus on the vehicle in front throughout. Results showed that task time gradually decreased for the trials when participants could only use MM. However, overall task time for MM conditions were significantly higher than for those in which PV was utilised, and participants rated the use of MM to be more difficult than PV. In contrast, results suggest that for interfaces that utilise peripheral visual processing the learning effect is not evident and operation times are constant over time. These findings indicate that in-vehicle touch screens should be designed to utilise peripheral vision for making simple button selections with reduced visual demand.

Citation

Eren, A. L., Burnett, G., Large, D. R., & Harvey, C. (in press). Understanding the effects of peripheral vision and muscle memory on in-vehicle touchscreen interactions. IET Intelligent Transport Systems, https://doi.org/10.1049/iet-its.2017.0229

• Journal Article Type: Article
• Acceptance Date: Mar 14, 2018
• Online Publication Date: Mar 16, 2018
• Deposit Date: Mar 19, 2018
• Publicly Available Date: Mar 19, 2018
• Journal: IET Intelligent Transport Systems
• Print ISSN: 1751-956X
• Electronic ISSN: 1751-9578
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1049/iet-its.2017.0229
• Public URL: https://nottingham-repository.worktribe.com/output/920162
• Publisher URL: https://doi.org/10.1049/iet-its.2017.0229
• Additional Information: This paper is a postprint of a paper submitted to and accepted for publication in IET Intelligent Transport Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library