The sensitivity of different methodologies for characterizing drivers’ gaze concentration under increased cognitive demand

By Ying Wang, Bryan Reimer, Jonathan Dobres, Bruce Mehler. Transportation Research Part F: Traffic Psychology and Behaviour, 2014

153 citations recorded by the source corpus.

An observer's visual scanning behavior tends to narrow during periods of increased cognitive demand. Thus, measures of gaze concentration have become a popular method of gauging cognitive demand, but the consensus on the best method for computing gaze concentration is still evolving. This analysis considers measures of gaze concentration while driving an on-road vehicle, with and without two types of secondary cognitive demand (auditory and visuospatial working memory). We compare the advantages and disadvantages of several different methods for measuring gaze concentration, as well as a direct statistical comparison of their relative sensitivities. We find that several methods produce similar effect sizes, and that these are consistent across task types. Horizontal gaze dispersion, as measured from the standard deviation of horizontal gaze position, attained the largest effect size, indicating that it is the most sensitive to changes in gaze concentration under cognitive demand, while also being one of the simpler metrics to calculate. Our results show that complex eye tracking data sets from applied, ecologically valid situations such as on-road driving can be analyzed effectively with maximal sensitivity and minimal analytical burden to produce a robust measure of a driver's general allocation of attention.

Subfields: Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/d9fbc72e34182843eefb26b984c88ef3abc779b1

How researchers see this paper

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In Social Psychology

  1. J. D. de Winter
  2. Gerhard Marquart
  3. Christopher D. D. Cabrall
  4. Jack Stilgoe

In Human-Computer Interaction

  1. J. D. de Winter
  2. Moritz Körber

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