A meta-analysis of the n-back task while driving and its effects on cognitive workload

Janczewski, Nikolai von; Wittmann, Jennifer; Engeln, Arnd; Baumann, Martin; Krauß, Lutz

doi:10.1016/j.trf.2020.11.014

Cited by 31 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solís Marcos et al also demonstrated that performance is not be impaired when there is simultaneous performing of tasks that do not use the same mental resources, such as auditory and visual tasks (Solís Marcos and Kircher 2019). Another study (von Janczewski et al 2021) has obtained conflicting results and reported that forward velocity, the standard deviation of forward velocity and steering wheel variability measures are not sensitive to cognitive load. A reason for these conflicting data could be the shorter driving period in simulation-based studies compare to a real driving test and reduce the effect of fatigue.…”

Section: Discussionmentioning

confidence: 99%

“…In terms of the relationship between subjective workload and driving performance indicators, some studies have used steering wheel angle (Brookhuis et al 2009;von Janczewski et al 2021) in the driving simulator to estimate driver performance and even to find a possible effect of this factor on MWL. Driving on a maze road causes slower mean speed, more variability in lane position, and the number of steering wheel corrections increased, which suggest a higher level of driver's workload (Hamish Jamson and Merat, 2005;Faure, Lobjois and Benguigui, 2016).…”

Section: Road Analysismentioning

confidence: 99%

“…In the design of the secondary task, it is essential to use a task that requires the same resources as the primary task to measure the MWL (Longo 2015; M. S. Young et al 2015). Measures such as the n-back, oddball task and reaction time tasks are examples used as secondary tasks (von Janczewski et al 2021;Solís Marcos and Kircher 2019). The physiological and or neurophysiology measures refer to body responses derived from the operator's physiological responses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

et al. 2021

View full text Add to dashboard Cite

Road traffic accidents are increasing worldwide and cause a high number of fatalities and injuries. Mental Work Load (MWL) is a contributing factor in road safety. The primary aim of this work was to study important MWL factors and then compare conventional and BRT (Bus Rapid Transit) drivers' MWL. This study evaluated bus drivers' MWL using the Driving Activity Load Index (DALI) questionnaire conducted with 123 bus drivers in Tehran. The results revealed significant differences between conventional and BRT drivers' mental workload. Moreover, data modelling showed that some organizational and environmental factors such as bus type, working hours per day, road maze, and route traffic volume contribute to drivers' mental workload. These findings suggest some essential customized factors that may help measure and offer practical solutions for decreasing the level of bus drivers MWL in real-world road driving.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Road Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For excitation purposes, one may design and perform experiments to help the subjects with enhancing their arousal state. In this regard, we can suggest to perform memory-related tasks [59] and analyze the effects of safe actuation helpful in elevating their arousal levels. In these experiments, subjects should fully engage with the tasks.…”

Section: Future Directionsmentioning

confidence: 99%

Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Azgomi

Faghih

2022

IEEE Open J. Eng. Med. Biol.

View full text Add to dashboard Cite

We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledgebased layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledgebased layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

show abstract

“…"Driving simulation" remains as the main method from the beginning of this field up to now [65,66]. In simulation, surrogate driving tasks such as LCT (lane change task) and BT (box task) could be adopted to mimic driving [67,68], and tools like visual occlusion and nback task could be employed for distraction imitation [69,70]. "Naturalistic driving study" (NDS) provides insight into this issue under natural driving conditions [71].…”

Section: Plos Onementioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

View full text Add to dashboard Cite

This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

show abstract

A meta-analysis of the n-back task while driving and its effects on cognitive workload

Cited by 31 publications

References 43 publications

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Understanding the domain of driving distraction with knowledge graphs

Contact Info

Product

Resources

About