How Do In-Car Navigation Aids Impair Expert Navigators' Spatial Learning Ability?

Abstract

Reliance on digital navigation aids has already shown negative impacts on navigators' innate spatial abilities. How this happens is still an open research question. We report on an empirical study with twenty-four experienced (male) taxi drivers to evaluate the long-term impacts of in-car navigation system use on the spatial learning ability of these navigation experts. Specifically, we measured cognitive load by means of electroencephalography (EEG) coupled with eye tracking to assess their visuospatial attention allocation during a video-based route-following task while driving through an unknown urban environment. We found that long-term reliance on in-car navigation aids did not affect participants' visual attention allocation during spatial learning but rather limited their ability to encode viewed geographic information into memory, which, in turn, led to greater cognitive load, especially along route segments between intersections. Participants with greater dependence on in-car navigation aids performed worse on the spatial knowledge tests. Our combined behavioral and neuropsychological findings provide evidence for the impairment of expert navigators' spatial learning ability when exposed to long-term use of digital in-car navigation aids.

Keywords: EEG; eye tracking; navigation systems; spatial learning.

Figure 1.

Experimental design, procedure, and data analysis. (A) Materials and apparatus used in the experiment. (B) The experimental procedure. (C) The data analysis procedure of this study. Note: SBSOD = Santa Barbara Sense of Direction Scale; EEG = electroencephalography; ICA = independent component analysis; PSD = power spectral density.

Figure 2.

Comparison of the behavioral performance of the low-dependence (LD) and high-dependence (HD) groups. (A) Accuracy in the scene recognition task. (B) Accuracy in the route recognition task. (C) r² of the sketch map. (D) Scaling bias of the sketch map. (E) Rotational bias of the sketch map. Note: ns = p > 0.05. *p < 0.05. **p < 0.01.

Figure 3.

Results of eye movement metrics. (A) Fixation count. (B) Average fixation duration (ms). (C) Saccade count.

Figure 4.

Electroencephalography (EEG) results for Route 1. (A) Relative theta power spectral density (PSD) of EEG data from five frontal electrodes (Fz, F3, F4, AF3, and AF4) were significantly different between the low-dependence (LD) group and high-dependence (HD) group in several trials. (B) The relative theta PSD of the Fz electrode is mapped along the navigated route, indicating the spatial context of the recorded cognitive load. The locations of intersections and route segments are also labeled on the map. (C) Statistical analysis of the relative theta PSD for each intersection and route segment. Note: *p < 0.05. **p < 0.01.

Figure A.1.

Electroencephalography (EEG) results for Route 2. (A) Relative theta power spectral density (PSD) of EEG data from five frontal electrodes (Fz, F3, F4, AF3, and AF4) were significantly different between the low-dependence (LD) group and high-dependence (HD) group in several trials. (B) The relative theta PSD of the Fz electrode is mapped along the navigated route, indicating the spatial context of the recorded cognitive load. The locations of intersections and route segments are also labeled on the map. (C) Statistical analysis of the relative theta PSD for each intersection and route segment. *p < 0.05. **p < 0.01. ***p < 0.001.