How does navigation system behavior influence human behavior?

Brügger, Annina; Richter, Kai-Florian; Fabrikant, Sara Irina · 2019 · DOAJ

DOI: 10.1186/s41235-019-0156-5

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates how varying levels of automation in pedestrian navigation systems influence human navigation behavior, specifically focusing on the trade-off between navigation performance and spatial knowledge acquisition. While automated systems improve efficiency, prior research indicates they often degrade users' ability to form mental maps of their environment. The authors aim to determine if specific system behaviors can enhance spatial learning without compromising navigational success, addressing a gap in understanding how to design intelligent systems that complement human cognition in real-world settings. The researchers conducted an empirical user study with 64 participants in an outdoor urban environment in Zurich, Switzerland. Using a between-subject design, participants were assigned to one of four navigation system behaviors defined by two cognitive processes: "allocation of attention" (noticing landmarks) and "self-localization" (determining current position). Each process was implemented at either a high level of automation (system executes automatically) or a low level of automation (user decides when to engage). Participants first followed an 800-meter route using the assigned system, incidentally acquiring spatial knowledge. They then reversed the same route without assistance to test their retained spatial knowledge. The study also utilized mobile eye-tracking to analyze gaze behavior and attention allocation during the assisted phase. The results indicated no significant differences in navigation performance across the four groups during the initial route-following phase. However, spatial knowledge acquisition varied significantly based on automation levels. Participants using systems with higher levels of automation failed to acquire sufficient spatial knowledge to reverse the route without errors. Conversely, systems requiring more active human participation facilitated better spatial learning. Eye-tracking data revealed distinct patterns of gaze behavior over time and space, demonstrating how different system behaviors direct user attention either toward the device or the surrounding environment. The findings suggest that it is possible to design navigation systems that increase spatial knowledge acquisition without harming navigation performance by adjusting the level of automation. High automation leads to passive navigation and poor spatial learning, while lower automation encourages active engagement with the environment. These results provide critical insights for human-computer interaction and the design of location-based services, emphasizing the need for systems that balance efficiency with the preservation of users' spatial cognition and environmental awareness.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• nav systems