Abstract
Navigation systems are ubiquitous tools to assist wayfinders of the mobile information society with various navigational tasks. Whenever such systems assist with self-localization and path planning, they reduce human effort for navigating. Automated navigation assistance benefits navigation performance, but research seems to show that it negatively affects attention to environment properties, spatial knowledge acquisition, and retention of spatial information. Very little is known about how to design navigation systems for pedestrian navigation that increase both navigation performance and spatial knowledge acquisition. To this end, we empirically tested participants (N = 64) using four different navigation system behaviors (between-subject design). Two cognitive processes with varying levels of automation, self-localization and allocation of attention, define navigation system behaviors: either the system automatically executes one of the processes (high level of automation), or the system leaves the decision of when and where to execute the process to the navigator (low level of automation). In two experimental phases, we applied a novel empirical framework for evaluating spatial knowledge acquisition in a real-world outdoor urban environment. First, participants followed a route assisted by a navigation system and, simultaneously, incidentally acquired spatial knowledge. Second, participants reversed the route using the spatial knowledge acquired during the assisted phase, this time without the aid of the navigation system. Results of the route-following phase did not reveal differences in navigation performance across groups using different navigation system behaviors. However, participants using systems with higher levels of automation seemed not to acquire enough spatial knowledge to reverse the route without navigation errors. Furthermore, employing novel methods to analyze mobile eye tracking data revealed distinct patterns of human gaze behavior over time and space. We thus can demonstrate how to increase spatial knowledge acquisition without harming navigation performance when using navigation systems, and how to influence human navigation behavior with varying navigation system behavior. Thus, we provide key findings for the design of intelligent automated navigation systems in real-world scenarios.
Highlights
The cognitive process of “navigation” consists of two major components: locomotion and wayfinding
The study presented in this paper empirically investigates human behavior during navigation tasks when facing different system behaviors according to the levels of automation as introduced by Parasuraman et al (2000), using a novel empirical framework for efficiently testing pedestrians’ spatial knowledge in real-world environments (Brügger, Richter, & Fabrikant, 2016)
We contend that the additional challenges of running studies in the real world are outweighed by the high ecological validity these settings offer (e.g., Kiefer et al, 2013)
Summary
The cognitive process of “navigation” consists of two major components: locomotion and wayfinding. Despite considerable research demonstrating detrimental spatial knowledge acquisition with navigation systems (e.g., Bertel, Dressel, Kohlberg, & von Jan, 2017; Parush et al, 2007; Willis, Hölscher, Wilbertz, & Li, 2009), surprisingly few empirical investigations have been conducted about ways to balance navigation performance and spatial knowledge acquisition during assisted navigation. Achieving such a balance seems feasible because navigation systems can feature varying levels of automation and, with this, vary the level of human involvement in decision-making. Several researchers have compared different kinds of navigation aids
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