Distributing Attention Between Environment and Navigation System to Increase Spatial Knowledge Acquisition During Assisted Wayfinding

  • Annina Brügger
  • Kai-Florian Richter
  • Sara Irina Fabrikant
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Travelers happily follow the route instructions of their devices when navigating in an unknown environment. Navigation systems focus on route instructions to allow the user to efficiently reach a destination, but their increased use also has negative consequences. We argue that the limitation for spatial knowledge acquisition is grounded in the system’s design, primarily aimed at increasing navigation efficiency. Therefore, we empirically investigate how navigation systems could guide users’ attention to support spatial knowledge acquisition during efficient route following tasks.


Attention guidance Decision making Environmental learning Mobile eye-tracking Pedestrian navigation Spatial cognition 

1 Introduction

Navigation systems have become an integral part of our everyday lives. They support us in reaching a destination more quickly, and help to reduce mental effort during wayfinding. Despite their popularity, concerns have been raised in the literature about the negative effects on spatial awareness and spatial knowledge acquisition due to their extensive use. Due to significant technological advancement in positional accuracy or in route calculation (e.g. speed), navigation systems impact human cognitive abilities such as allocation of attention negatively (Gardony et al. 2013). Automated navigation guidance splits a navigators’ attention between the navigation system and the environment (Gardony et al. 2013; Ishikawa et al. 2008). Attentional split can be induced by a GPS positioning signal, drawing a navigator’s continued attention to the display, as it is constantly updated during navigation (Ishikawa et al. 2008). Continued reliance on this kind of positional updates further leads to memory loss of environmental information processing, and respective navigation skill training (Parush et al. 2007). As the engagement of the user with the environment is supposed to strengthen spatial memory (Klippel et al. 2010), navigation systems should (I) draw navigators’ attention back to the environment and (II) ensure efficient navigation. They should also pro-actively support a navigator’s increased mental effort for this task. This in turn, could mitigate potential limitations to spatial knowledge acquisition (Parush et al. 2007). Navigation systems should not only enable user interaction with the environment (Hirtle and Raubal 2013), but also increase visual attention to relevant attention-grabbing features in the environment, and their respective depiction on the navigation system (e.g. landmarks (Richter 2017)) to increase spatial knowledge acquisition (Kiefer et al. 2013). This is the research problem we aim to tackle with our research program (also see Brügger et al. 2016).

2 Empirical User Study in Outdoor Environments

We empirically investigate how navigation systems could guide users’ attention to support spatial knowledge acquisition during efficient outdoor route following tasks. Our user study features two phases (Fig. 1) similar to a previous virtual environment study by Karimpur et al. (2016). First, participants execute a route following task in an unknown urban environment while being assisted by a navigation system. Second, participants are asked to find the same route back from memory, without any navigation assistance. In doing so, we directly assess their spatial knowledge acquired during the first (assisted) phase.
Fig. 1

Empirical user study. Task 1: assisted route following (left). Task 2: unassisted route reversal (right)

Fig. 2

Participant’s gaze on a navigation system (left) and on the environment (right). Data collection and visualization (yellowred circle) with mobile eye-tracking technology

We implemented four different ways with varying levels of user engagement in which the navigation system actively engages the navigator, with both, the traversed environment, and the navigation system during navigation. These four levels of user engagement were deployed in different combinations during the first (learning) phase. Accordingly, participants (N = 64) were divided into four groups (between-subject design) and randomly assigned to four variations of navigation system behaviour. Performance in the second (recall) phase reveals how the acquisition of spatial knowledge varies as a result of the different levels of user engagement with the system or the environment, respectively.

Furthermore, mobile eye-tracking data collected during participants’ trials will support understanding how the four assessed ways of user engagement might lead to differences in perceiving both, the environment, and the navigation system (Fig. 2).

3 Results and Future Work

Our results show that participants who used the navigation system with a higher level of user engagement acquired better spatial knowledge without harming navigation efficiency. Participants who collected their own environmental information simultaneously improved their spatial mental representation during the learning phase. Conversely, those participants whose attention was guided by the navigation system (lower level of user engagement) made significantly more errors in the recall phase.

These findings have significant implications for the understanding of how people should be guided by navigation systems during wayfinding, in terms of balancing efficiency of route following with potential loss of spatial knowledge. Further experiments will have to focus on the distribution of attention between the environment and the navigation system, as to develop a more coherent picture of the trade-offs between efficient navigation performance and effective spatial knowledge acquisition.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Annina Brügger
    • 1
  • Kai-Florian Richter
    • 2
  • Sara Irina Fabrikant
    • 1
  1. 1.Geographic Information Visualization and Analysis (GIVA) Department of GeographyUniversity of ZurichZurichSwitzerland
  2. 2.Department of Computing ScienceUmeå UniversityUmeåSweden

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