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How does navigation system behavior influence human behavior?

Annina Brügger1, Kai-Florian Richter2, Sara Irina Fabrikant3

  • 1Department of Geography, University of Zurich, Winterthurerstr.190, 8057, Zurich, Switzerland. annina.bruegger@geo.uzh.ch.

Cognitive Research: Principles and Implications
|February 14, 2019
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Summary
This summary is machine-generated.

Automated navigation systems can hinder spatial knowledge acquisition. Designing systems with lower automation levels can improve learning without sacrificing navigation performance, benefiting pedestrian navigation.

Keywords:
AttentionAutomationEcological validityEmpirical user studyHuman–computer interaction (HCI)Incidental learningLocation-based services (LBS)Spatial cognition

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Urban Navigation

Background:

  • Automated navigation systems aid wayfinding but may impair spatial knowledge acquisition and environmental awareness.
  • Optimal design for pedestrian navigation systems balancing performance and learning remains underexplored.

Purpose of the Study:

  • To investigate how different levels of automation in navigation systems affect both navigation performance and spatial knowledge acquisition.
  • To identify design principles for intelligent navigation systems that enhance learning without compromising efficiency.

Main Methods:

  • Empirical testing of 64 participants using four distinct navigation system behaviors with varying automation levels for self-localization and attention allocation.
  • A novel two-phase framework in a real-world urban environment: assisted route following for incidental learning, followed by unassisted route reversal.
  • Analysis of mobile eye-tracking data to reveal gaze behavior patterns and correlate them with navigation strategies and learning outcomes.

Main Results:

  • No significant differences in navigation performance were observed across different automation levels during the assisted route-following phase.
  • Participants using highly automated systems demonstrated reduced spatial knowledge, leading to navigation errors during the unassisted reversal phase.
  • Distinct patterns in eye-tracking data indicated how navigation system behavior influences attention and spatial information processing.

Conclusions:

  • Navigation system automation levels critically impact spatial knowledge acquisition, with higher automation potentially hindering learning.
  • Designing navigation systems with adjustable or lower automation can enhance spatial learning in pedestrians without negatively affecting navigation performance.
  • Findings provide crucial insights for developing intelligent automated navigation systems optimized for real-world urban environments.