This study examined the sensitivity of heart rate, skin conductance, and respiration rate as measures of mental workload in a simulated driving environment. Workload was systematically manipulated by using increasingly difficult levels of a secondary cognitive task. In a sample of 121 young adults, heart rate increased incrementally with increasing task demand. Significant elevations in skin conductance and respiration rate were also observed. At the lower levels of added workload, secondary task performance was nearly perfect and changes in indices of driving performance were negligible. At the highest level of workload, all three physiological measures appeared to plateau, and a subtle drop in simulated driving performance became detectable. Taken together, the pattern of results indicates that physiological measures can be sensitive to changes in workload before the appearance of clear decrements in driving performance. These findings further highlight a role for physiological monitoring as a means to measure mental workload in product design and functionality research. They also support work exploring the potential for incorporating physiological measures of driver workload and attentional state in future safety systems.

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.

Physiological measures provide a continuous and relatively non-invasive method of characterising workload. The extent to which such measures provide sensitivity beyond that provided by driving performance metrics is more open to question. Heart rate and skin conductance were monitored during actual highway driving in response to systematically increased levels of cognitive demand using an auditory delayed digit recall task. The protocol was consistent with an earlier simulator study, providing an opportunity to assess the validity of physiological measures recorded during driving simulation. The pattern of change in heart rate with increased cognitive demand was highly consistent between field and simulator. The findings meet statistical criteria for both relative and absolute validity, although there was a trend for absolute levels to be higher under actual driving conditions. For skin conductance level, the pattern in both environments was also quite similar and a reasonable case for overall relative validity can be made. Statement of Relevance: Growing complexity and multiple demands on modern drivers’ attention highlight the significance of determining whether physiological measures provide increased sensitivity in workload detection. Better understanding, including whether simulator assessments provide valid measures of real-world response patterns, has implications in evaluating and refining interface designs and for developing advanced workload managers.

Data from on-road and simulation studies were compared to assess the validity of measures generated in the simulator. In the on-road study, driver interaction with three manual address entry methods (keypad, touch screen and rotational controller) was assessed in an instrumented vehicle to evaluate relative usability and safety implications. A separate group of participants drove a similar protocol in a medium fidelity, fixed-base driving simulator to assess the extent to which simulator measures mirrored those obtained in the field. Visual attention and task measures mapped very closely between the two environments. In general, however, driving performance measures did not differentiate among devices at the level of demand employed in this study. The findings obtained for visual attention and task engagement suggest that medium fidelity simulation provides a safe and effective means to evaluate the effects of in-vehicle information systems (IVIS) designs on these categories of driver behaviour. Statement of Relevance: Realistic evaluation of the user interface of IVIS has significant implications for both user acceptance and safety. This study addresses the validity of driving simulation for accurately modelling differences between interface methodologies by comparing results from the field with those from a medium fidelity, fixed-base simulator.

Objective: The aim of this study was to assess sensitivity of visual attention and driving performance for detecting changes in driver cognitive workload across different age groups. Background: The literature shows mixed results concerning the sensitivity of gaze concentration metrics to variations in cognitive demand. No studies appear showing how age affects gaze allocation during cognitive demand. Method: Recordings of drivers’ gaze and driving performance by individuals in their 20s, 40s, and 60s were captured in actual driving conditions during three levels of cognitive demand. Results: Gaze concentration increased with task difficulty through the low and moderate levels of demand and then appeared to level out at the high demand level. At the moderate difficulty level, gaze concentration increased by 2.4 cm (≈2°) from the reference period. The degree of gaze concentration with added cognitive demand is not related to age in the relatively healthy drivers studied. Driving performance measures did not show a consistent relationship with the objective demand level. Conclusion: Gaze concentration appears at low levels of cognitive demand prior to the appearance of marked decrements in driving control. There is no compelling evidence from this study that driving performance measures can be used to index differences in workload prior to capacity saturation. Application: Drivers’ awareness of vehicle surroundings is incrementally affected by increases in cognitive demand. Developers of more advanced driver support systems should consider gaze concentration as a measure of driver cognitive workload. This recommendation is particularly relevant in light of the added benefits of gaze measurements for detecting visual demand. Keywords: mental workload, cognitive distraction, eye movements, visual tunneling, driving safety, situational awareness, voice interfaces, cellular telephones