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Hum Factors. 2012 Jun;54(3):396-412.

Sensitivity of physiological measures for detecting systematic variations in cognitive demand from a working memory task: an on-road study across three age groups.

Author information

1
AgeLab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E40-278, Cambridge, MA 02139, USA. bmehler@mit.edu

Abstract

OBJECTIVE:

To assess the sensitivity of two physiological measures for discriminating between levels of cognitive demand under driving conditions across different age groups.

BACKGROUND:

Previous driving research presents a mixed picture concerning the sensitivity of physiological measures for differentiating tasks with presumed differences in mental workload.

METHOD:

A total of 108 relatively healthy drivers balanced by gender and across three age groups (20-29, 40-49, 60-69) engaged in three difficulty levels of an auditory presentation-verbal response working memory task.

RESULTS:

Heart rate and skin conductance level (SCL) both increased in a statistically significant fashion with each incremental increase in cognitive demand, whereas driving performance measures did not provide incremental discrimination. SCL was lower in the 40s and 60s age groups; however, the pattern of incremental increase with higher demand was consistent for heart rate and SCL across all age groups. Although each measure was quite sensitive at the group level, considering both SCL and heart rate improved detection of periods of heightened cognitive demand at the individual level.

CONCLUSION:

The data provide clear evidence that two basic physiological measures can be utilized under field conditions to differentiate multiple levels of objectively defined changes in cognitive demand. Methodological considerations, including task engagement, may account for some of the inconsistencies in previous research.

APPLICATION:

These findings increase the confidence with which these measures may be applied to assess relative differences in mental workload when developing and optimizing human machine interface (HMI) designs and in exploring their potential role in advanced workload detection and augmented cognition systems.

PMID:
22768642
DOI:
10.1177/0018720812442086
[Indexed for MEDLINE]

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