Cognitive Research (Sep 2024)

Reliable but multi-dimensional cognitive demand in operating partially automated vehicles: implications for real-world automation research

  • Monika Lohani,
  • Joel M. Cooper,
  • Amy S. McDonnell,
  • Gus G. Erickson,
  • Trent G. Simmons,
  • Amanda E. Carriero,
  • Kaedyn W. Crabtree,
  • David L. Strayer

DOI
https://doi.org/10.1186/s41235-024-00591-5
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 9

Abstract

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Abstract The reliability of cognitive demand measures in controlled laboratory settings is well-documented; however, limited research has directly established their stability under real-life and high-stakes conditions, such as operating automated technology on actual highways. Partially automated vehicles have advanced to become an everyday mode of transportation, and research on driving these advanced vehicles requires reliable tools for evaluating the cognitive demand on motorists to sustain optimal engagement in the driving process. This study examined the reliability of five cognitive demand measures, while participants operated partially automated vehicles on real roads across four occasions. Seventy-one participants (aged 18–64 years) drove on actual highways while their heart rate, heart rate variability, electroencephalogram (EEG) alpha power, and behavioral performance on the Detection Response Task were measured simultaneously. Findings revealed that EEG alpha power had excellent test–retest reliability, heart rate and its variability were good, and Detection Response Task reaction time and hit-rate had moderate reliabilities. Thus, the current study addresses concerns regarding the reliability of these measures in assessing cognitive demand in real-world automation research, as acceptable test–retest reliabilities were found across all measures for drivers across occasions. Despite the high reliability of each measure, low intercorrelations among measures were observed, and internal consistency was better when cognitive demand was estimated as a multi-factorial construct. This suggests that they tap into different aspects of cognitive demand while operating automation in real life. The findings highlight that a combination of psychophysiological and behavioral methods can reliably capture multi-faceted cognitive demand in real-world automation research.

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