How does the design of landmarks on a mobile map influence wayfinding experts’ spatial learning during a real-world navigation task?

ABSTRACT

Humans increasingly rely on GPS-enabled mobile maps to navigate novel environments. However, this reliance can negatively affect spatial learning, which can be detrimental even for expert navigators such as search and rescue personnel. Landmark visualization has been shown to improve spatial learning in general populations by facilitating object identification between the map and the environment. How landmark visualization supports expert users’ spatial learning during map-assisted navigation is still an open research question. We thus conducted a real-world study with wayfinding experts in an unknown residential neighborhood. We aimed to assess how two different landmark visualization styles (abstract 2D vs. realistic 3D buildings) would affect experts’ spatial learning in a map-assisted navigation task during an emergency scenario. Using a between-subjects design, we asked Swiss military personnel to follow a given route using a mobile map, and to identify five task-relevant landmarks along the route. We recorded experts’ gaze behavior while navigating and examined their spatial learning after the navigation task. We found that experts’ spatial learning improved when they focused their visual attention on the environment, but the direction of attention between the map and the environment was not affected by the landmark visualization style. Further, there was no difference in spatial learning between the 2D and 3D groups. Contrary to previous research with general populations, this study suggests that the landmark visualization style does not enhance expert navigators’ navigation or spatial learning abilities, thus highlighting the need for population-specific mobile map design solutions.

KEYWORDS:

• Landmark visualization style
• mobile map design
• real-world navigation study
• expert users
• spatial learning
• eye-tracking
• visual attention

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2183525.

Acknowledgments

The authors thank Stefano Villa for his help recruiting the participants and organizing the necessary logistics to run the experiment, and all the experts for participating in our study. The authors thank Dr. Tyler Thrash and Dr. Ian T. Ruginski for their invaluable comments on our evolving research program and this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and statistical analyses (R-code) that support the findings of this study are openly available in OSF at https://doi.org/10.17605/OSF.IO/QDTVW

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the H2020 - EXCELLENT SCIENCE - European Research Council (ERC) Advanced Grant GeoViSense (No. 740426). https://cordis.europa.eu/project/id/740426.