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Robust multimodal cognitive load measurement
Chen F., Zhou J., Wang Y., Yu K., Arshad S., Khawaji A., Conway D., Springer International Publishing, New York, NY, 2016. 254 pp. Type: Book (978-3-319316-98-7)
Date Reviewed: Mar 9 2017

The content of this volume is a fair reference source for researchers and stakeholders interested in the investigation and application of the construct of cognitive load. It reviews the current state-of-the-art techniques for cognitive load measurement and its applications. The book is divided into six parts, and it covers areas such as behavioral and social analysis in human-computer interaction.

The first part, structured over three chapters, is devoted to the theoretical concept of cognitive load, its link with working memory, and the multimodal aspects of the brain for processing information. It then provides a high-level overview of the different ways to measure cognitive load including subjective, performance, physiological, and behavioral measures as well as some of the factors that influence this construct. The book then focuses on linking cognitive load to the notion of performance, to cognitive load theory and learning, and to multimodal interactions and measures.

The second part is built over two chapters, and it has a practical perspective describing, with a higher degree of detail, studies that employed physiological measurement to assess cognitive load using eye-based and galvanic skin response-based measures.

The third part, developed over four chapters, provides readers with details on behavioral measurement, exploring studies that adopted linguistic feature-based measures, speech signal, pen input, and mouse-based measures.

The fourth part, structured over four chapters, is devoted to a review of the multimodal measures and a description of some of the factors believed to influence cognitive load. In particular, through a set of examples taken from published peer-reviewed articles, the book explores the links between cognitive load and emotion, stress, and trust.

The fifth part, built upon three chapters, aims at providing readers with a set of accessible techniques to measure cognitive load as well as to dynamically adjust the design of tasks in order to improve human performance and maximize user satisfaction and productivity. It then describes some of the real-time cognitive load measurement techniques and provides readers with a set of applications in areas such as interface design, emergency management, transportation, and education.

The last part briefly puts cognitive load measurement into perspective, mainly summarizing the contribution of the book.

The pro of this volume is that it provides readers with useful information about cognitive load from a theoretical and practical perspective. It links together the construct of cognitive load with existing theories, mainly developed in the disciplines of ergonomics and educational psychology. It also puts theoretical notions into practice, describing a number of user studies that show the impact of cognitive load measurement on human performance. The con is the organization of the book, which might cause readers to get off track, especially novices. Also, readers are left with many pieces of knowledge, definitions, and techniques that are not always placed in a higher framework.

Overall, this is a good reference for those who are interested in the application of cognitive load and its measurement, with the aim of designing multimodal human-computer interactive systems better aligned with limited mental capacities of humans. However, the volume is not always an ideal reference for researchers and scientists interested in the theoretical refinement of the construct of cognitive load and its modeling. On one hand, the book has plenty of material for researchers, providing them with descriptions of those factors believed to influence mental workload and reviewing the relevant techniques to measure these factors. On the other hand, sometimes it looks like a collection of individual conference and scientific journal papers that are difficult to be linked together in a unified framework. In other words, the investigation of the relationship and interactions of all these factors in a wider model of cognitive load has not been covered. However, this is also an open problem in the literature of cognitive load modeling.

Reviewer:  Luca Longo Review #: CR145111 (1705-0254)
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