Towards testing auditory–vocal interfaces and detecting distraction while driving: A comparison of eye-movement measures in the assessment of cognitive workload

Niezgoda, M.; Tarnowski, Adam; Kruszewski, Mikołaj; Kamiński, T.

doi:10.1016/j.trf.2015.04.012

Cited by 58 publications

(27 citation statements)

References 36 publications

(35 reference statements)

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“…Multiple studies have shown that pupil dilation increases with increasing n (Belayachi et al, 2015 ; Brouwer, Hogervorst, Holewijn, & van Erp, 2014 ; Hopstaken, van der Linden, Bakker, & Kompier, 2015 ; Karatekin, Marcus, & Couperus, 2007 ; Niezgoda et al, 2015 ; Pehlivanoglu, Jain, Ariel, & Verhaeghen, 2014 ; Scharinger, Soutschek, Schubert, & Gerjets, 2015 ). Notably, a recent study using classification algorithms has shown that pupil dilation alone can be sufficient to distinguish high from low cognitive load up to an accuracy of 75 %.…”

Section: Pupil Studies On the Updating Component Of Cognitive Controlmentioning

confidence: 99%

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Pupil dilation as an index of effort in cognitive control tasks: A review

2018

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Pupillometry research has experienced an enormous revival in the last two decades. Here we briefly review the surge of recent studies on task-evoked pupil dilation in the context of cognitive control tasks with the primary aim being to evaluate the feasibility of using pupil dilation as an index of effort exertion, rather than task demand or difficulty. Our review shows that across the three cognitive control domains of updating, switching, and inhibition, increases in task demands typically leads to increases in pupil dilation. Studies show a diverging pattern with respect to the relationship between pupil dilation and performance and we show how an effort account of pupil dilation can provide an explanation of these findings. We also discuss future directions to further corroborate this account in the context of recent theories on cognitive control and effort and their potential neurobiological substrates.

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Section: Pupil Studies On the Updating Component Of Cognitive Controlmentioning

confidence: 99%

“…failing an important math test as a student, to disastrous, e.g,. a traffic accident (Niezgoda, Tarnowski, Kruszewski, & Kamiński, 2015 ). Numerous recent studies have started to investigate the dilation of the human pupil under such conditions.…”

Section: Introductionmentioning

confidence: 99%

Pupil dilation as an index of effort in cognitive control tasks: A review

2018

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“…Now we introduce how to estimate the importance of each input variable to the variation of the system output. Initially, set values , and calculate (11) For , set and then calculate (12) where . Next calculate (13) and 14The ERR value for each team , as a criterion to select the model structure, is defined as…”

Section: Identification Of Important Variables Using Errcmentioning

confidence: 99%

“…However, unresolved problems do arise due to strong uncertainties surrounding driving experience and complex driver-vehicle interactions. Before transitioning to fully autonomous driving, driver behaviour should be better understood and integrated to enhance vehicle performance and traffic efficiency [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

et al. 2018

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Measuring driver workload is of great significance for improving the understanding of driver behaviours and supporting the improvement of advanced driver assistance systems technologies. In this paper, a novel hybrid method for measuring driver workload estimation for real-world driving data is proposed. Error reduction ratio causality, a new nonlinear causality detection approach, is being proposed in order to assess the correlation of each measured variable to the variation of workload. A full model describing the relationship between the workload and the selected important measurements is then trained via a support vector regression model. Real driving data of 10 participants, comprising 15 measured physiological and vehicle-state variables are used for the purpose of validation. Test results show that the developed error reduction ratio causality method can effectively identify the important variables that relate to the variation of driver workload, and the support vector regression based model can successfully and robustly estimate workload.

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“…However, pupil size is very sensitive to the lighting conditions, which can change markedly during driving (Palinko & Kun, 2012). A change in pupil size is the most sensitive measure of cognitive workload (Niezgoda, Tarnowski, Kruszewski, & Kamiński, 2015). Fixation duration is also sensitive to workload and increases with increasing visual and mental processing demands (Recarte & Nunes, 2000).…”

Section: Introductionmentioning

confidence: 99%

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Niu

Sun

et al. 2020

Hum Ftrs & Erg Mfg Svc

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A high degree of automated driving distracts drivers more easily, resulting in slow recognition of critical events during driving and slow responses to emergencies. Automated driving and manual switching processes are also prone to erroneous decisions. We conducted a simulated automated driving experiment to study participants' visual perception characteristics during the takeover of vehicle control. The present study used dynamic videos to imitate the driving situations when drivers returned their gaze from the distractive source to the road. We collected the drivers' eye movement data to analyze the search strategy and physiological characteristics of the drivers after the takeover reminder. The results showed that the instant information search method of the drivers was scanning of the driving scene. When the degree of distraction deepened and the hazard level of scenes increased, the pupil diameter of the drivers increased and the fixation duration became longer. These findings can help to design takeover warnings and support more intelligent automated driving systems to judge whether measures should be taken to interfere with the driver's operation to avoid collisions. Furthermore, the drivers' fixation point distribution focused on the left side and the lower side of the scene. We suggest that the takeover warning is displayed in the head-up display. This study provides a better understanding of drivers' visual perception characteristics when drivers' eyesight returns from other distractors to the driving scene and a good theoretical basis for the design of hazard warning information for automated driving.

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Towards testing auditory–vocal interfaces and detecting distraction while driving: A comparison of eye-movement measures in the assessment of cognitive workload

Cited by 58 publications

References 36 publications

Pupil dilation as an index of effort in cognitive control tasks: A review

Pupil dilation as an index of effort in cognitive control tasks: A review

Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

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