Towards a Cognitive Load Aware Multimodal Dialogue Framework for the Automotive Domain

Neßelrath, Robert

doi:10.1109/ie.2013.22

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Identifying such areas wherein users experience significant levels of mental workload, and trying to regulate it by system re-design, could also minimize human error, and in turn, increase user satisfaction, learning, and other operational advantages (Davenport and Beck, 2001 ). The significance of mental workload measurement is frequently expressed in the desirability of optimizing human-machine interactions (Ogden et al, 1979 ; De Waard and te Groningen, 1996 ; Young and Stanton, 1997 ; Hankins and Wilson, 1998 ; Neßelrath, 2013 ; Kajiwara, 2014 ; Paxion et al, 2014 ; Zhang et al, 2015 ). The key reason for measuring mental workload is to quantify the mental cost of performing tasks in order to predict operator and system responses.…”

Section: Introductionmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

View full text Add to dashboard Cite

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

show abstract