Review of Eye-related Measures of Drivers’ Mental Workload

Marquart, Gerhard; Cabrall, Christopher; Winter, Joost de

doi:10.1016/j.promfg.2015.07.783

Cited by 179 publications

(112 citation statements)

References 26 publications

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“…10) indicated that people blinked particularly at those moments when the visual demand was reduced, such as right after the start of the task and right after the presentation of the multiplier. In summary, consistent with prior research, the relationship between mental workload and blink rate is complex, and it appears that blink rate is governed not only by mental demands, but also by visual demands (see also Marquart, Cabrall & De Winter, 2015).…”

Section: Blink Ratesupporting

confidence: 86%

“…Second, we assessed the mean blink rate (MBR). The relation between mental workload and blink rate has been unclear (Kramer, 1990;Recarte et al, 2008;Marquart, Cabrall & De Winter, 2015), and our aim was to clarify this relationship.…”

Section: Introductionmentioning

confidence: 91%

“…The relation between mental workload and blink rate has been unclear in the literature (e.g., Kramer, 1990;Marquart, Cabrall & De Winter, 2015;Recarte et al, 2008). The results in the present study show that the MBR was slightly higher for Level 3 than for Levels 1 and 2.…”

Section: Blink Ratementioning

confidence: 99%

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Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Marquart

Winter

2015

PeerJ Computer Science

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Pupillometry is a promising method for assessing mental workload and could be helpful in the optimization of systems that involve human-computer interaction. The present study focuses on replicating the studies by Ahern (1978) and Klingner (2010), which found that for three levels of difficulty of mental multiplications, the more difficult multiplications yielded larger dilations of the pupil. Using a remote eye tracker, our research expands upon these two previous studies by statistically testing for each 1.5 s interval of the calculation period (1) the mean absolute pupil diameter (MPD), (2) the mean pupil diameter change (MPDC) with respect to the pupil diameter during the pre-stimulus accommodation period, and (3) the mean pupil diameter change rate (MPDCR). An additional novelty of our research is that we compared the pupil diameter measures with a self-report measure of workload, the NASA Task Load Index (NASA-TLX), and with the mean blink rate (MBR). The results showed that the findings of Ahern and Klingner were replicated, and that the MPD and MPDC discriminated just as well between the lowest and highest difficulty levels as did the NASA-TLX. The MBR, on the other hand, did not differentiate between the difficulty levels. Moderate to strong correlations were found between the MPDC and the proportion of incorrect responses, indicating that the MPDC was higher for participants with a poorer performance. For practical applications, validity could be improved by combining pupillometry with other physiological techniques.

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Section: Blink Ratesupporting

confidence: 86%

Section: Introductionmentioning

confidence: 91%

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Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Marquart

Winter

2015

PeerJ Computer Science

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“…Gerhard et al and Minin et al believe that an increase in task visual requirements increases cognitive workload. Accordingly, everything that increases the need for visual perception increases mental workload (6,29).…”

Section: Visual Attention and Processing In Drivingmentioning

confidence: 99%

Developing a Checklist for Cognitive Characteristics of Driving Scenarios in Dual-Task Studies: The Case of Cell Phone Use While Driving

et al. 2019

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Context: Recently, driving simulators have been widely used in various studies in the traffic safety domain, especially to investigate the effects of a mobile phone conversation on driving performance. As the characteristics of simulators and scenarios designed in previous studies were quite diverse, the cognitive workload resulting from scenarios in each study and their effects on the results varied significantly so that it made it difficult to compare the results. Therefore, the present study was conducted aiming at providing a checklist of cognitive specification control in this category of studies by investigating the methodological characteristics of previous studies. Evidence Acquisition: The articles were searched in Springer, Elsevier, PubMed, and Google Scholar databases during 2002-2017. The author used mobile phone, cell phone, driving simulator, distraction, and mental workload keywords with the "and" and "or" operators. Based on certain criteria, 14 articles were selected among the retrieved articles. Results: Methods and purposes of articles were evaluated in terms of factors affecting cognitive function. Based on reliable and valid scientific documents, a checklist of the cognitive profile of scenarios and simulators was designed and presented in three macro domains. Conclusions: Considering various simulator designs the scenario design characteristics effective in cognitive workload, the studies were not convergent so that different aspects of the main variables were reported in various studies. Inattention to reporting these variables led to the incorrect estimation of the effects of a mobile phone conversation on driving performance.

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“…Physiological measures appear to be an attractive workload assessment approach as these can be performed without mediation by subjective response or transformation through performance manifestation (Marquart et al, 2015). In physiological measurement, electroencephalographs (EEGs) and electrocardiographs (ECGs) are widely used.…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Driving Workload and Gamma Waves on Curved Sections in Expressway

Kang¹,

Namgung²,

Kim³

et al. 2016

Journal of Korean Society of Transportation

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Previous studies show that driver mental workload plays a significant role in the occurrence of traffic accidents. This study also analyzes driving workload under different road conditions especially with the brain wave data collected by a driving simulator. We use a logistic regression model to explain the relationship between driving workload and gamma brain waves. The results show that beta band of brain waves becomes broader with more curved sections while alpha band and gamma band become narrower. Since driving workload is negatively correlated with gamma band, it can be concluded that driving condition with less curved section is beneficial for reducing driving stress and increasing driving comfort.

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Review of Eye-related Measures of Drivers’ Mental Workload

Cited by 179 publications

References 26 publications

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Developing a Checklist for Cognitive Characteristics of Driving Scenarios in Dual-Task Studies: The Case of Cell Phone Use While Driving

The Analysis of Driving Workload and Gamma Waves on Curved Sections in Expressway

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