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Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy

Authors:

Leanne M. Hirshfield,

Erin Treacy Solovey,

Audrey Girouard,

James Kebinger,

Robert J.K. Jacob,

Angelo Sassaroli,

Sergio FantiniAuthors Info & Claims

CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 2185 - 2194

https://doi.org/10.1145/1518701.1519035

Published: 04 April 2009 Publication History

Abstract

A well designed user interface (UI) should be transparent, allowing users to focus their mental workload on the task at hand. We hypothesize that the overall mental workload required to perform a task using a computer system is composed of a portion attributable to the difficulty of the underlying task plus a portion attributable to the complexity of operating the user interface. In this regard, we follow Shneiderman's theory of syntactic and semantic components of a UI. We present an experiment protocol that can be used to measure the workload experienced by users in their various cognitive resources while working with a computer. We then describe an experiment where we used the protocol to quantify the syntactic workload of two user interfaces. We use functional near infrared spectroscopy, a new brain imaging technology that is beginning to be used in HCI. We also discuss extensions of our techniques to adaptive interfaces.

References

[1]

Baddeley, A. and Della Sala, S. Working memory and executive control. Philosophical Transactions of the Royal Society of London, 351, (1996), 1397--403.

[2]

Boechler, P. How Spatial Is Hyperspace? Interacting with Hypertext Documents: Cognitive Processes and Concepts. CyberPsychology and Behavior, 4,1,(2001) 23--46.

[3]

Chance, B., Anday, E., Nioka, S., Zhou, S., Hong, L., Worden, K., Li, C. Murray, T., Ovetsky, Y., Thomas, R. A novel method for fast imaging of brain function, non-invasively, with light. Optics Express, 10, 2, (1988), 411--423.

[4]

Czerwinski, M. and Larson, K. Cognition and the Web: Moving from Theory to Web Design. in Human Factors and Web Development, Ratner, J. (Ed.), Erlbaum: NJ, (2002), 147--165.

[5]

Eckstrom, R., French, J., Harman, H. and Derman, D. Kit of factor-referenced cognitive tests. (1976).

[6]

Gevins, A., Smith, M., McEvoy, L. and Yu, D. High-Resolution EEG Mapping of Cortical Activation Related to Working Memory: Effects of Task Difficulty, Type of Processing, and Practice. Cerebral Cortex, (1997), 374--385.

[7]

Gratton, G., Fabiani, M., Friedman, D., Franceschini, M., Fantini, S., Corballis, P. and Gratton, E. Rapid Changes of Optical Parameters in the Human Brain During a Tapping Task. Journal of Cognitive Neuroscience, 7. (1995), 446--456.

Digital Library

[8]

Grimes, D., Tan, D., Hudson, S., Shenoy, P. and Rao, R., Feasibility and Pragmatics of Classifying Working Memory Load with an Electroencephalograph. in Proc CHI 2008. ACM Press (2008).

Digital Library

[9]

Hart, S.G. and Staveland, L.E. Development of NASA-TLX : Results of empirical and theoretical research. in Hancock, P., Meshkati, N. ed. Human Mental Workload, Amsterdam, (1988), pp 139 -- 183.

[10]

Izzetoglu, K., Bunce, S., Onaral, B., Pourrezaei, K. and Chance, B. Functional Optical Brain Imaging Using Near-Infrared During Cognitive Tasks. Int. Journal of Human-Computer Interaction, 17, 2. (2004). 211--231.

[11]

John, M.S., Kobus, D., Morrison, J. and Schmorrow, D. Overview of the DARPA Augmented Cognition Technical Integration Experiment. Int. Journal of Human-Computer Interaction, 17, 2. (2004), 131--149.

[12]

Keogh, E. and Pazzani, M., Scaling up dynamic time warping for datamining applications. in Proc. of the Sixth ACM SIGKDD, (2000).

Digital Library

[13]

Kohlmorgen, J., Dornhege, G., Braun, M, Blankertz, B., Muller, K., Curio, G., Hagemann, K., Bruns, A., Sharuf, M.,Kincses, W. Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection. Toward Brain Computer Interfacing, MIT Press, (2007), 409--422.

[14]

Larson, K. and Czerwinski, M., Web Page Design: Implications of Memory, Structure and Scent for Information Retrieval. in Proc.CHI 1998, ACM Press, (1998).

Digital Library

[15]

Lee, J.C. and Tan, D.S., Using a Low-Cost Electroencephalograph for Task Classification in HCI Research. in Proc. UIST 2006. ACM Press, (2006).

Digital Library

[16]

Leung, H., Oh, H., Ferri, J. and Yi, Y. Load Response Functions in the Human Spatial Working Memory Circuit During Location Memory Updating. NeuroImage, 35, (2007), 368--377.

[17]

Muller, K.T., M., Dornhege, G., Krauledat, M., Curio, G. and Blankertz, B. Machine learning for real-time single-trial EEG-analysis: From Brain-computer interfacing to mental state monitoring. Journal of Neuroscience Methods, 167, 1, (2008), 82--90.

[18]

Parasuraman, R. and Caggiano, D. Neural and Genetic Assays of Human Mental Workload. in Quantifying Human Information Processing, Lexington Books, (2005), 123--149.

[19]

Sassaroli, A., Zheng, F., Hirshfield, L.M., Girouard, A., Solovey, E.T., Jacob, R.J.K. and Fantini, S. Discrimination of mental workload levels in human subjects with functional near-infrared spectroscopy. in the Journal of Innovative Optical Health Sciences, (2009), 227--237.

[20]

Shneiderman, B. and Plaisant, C. Designing the User Interface: Strategies for Effective Human-Computer Interaction, Fourth Edition, Addison-Wesley, Reading, Mass. (2005), 86--88.

Digital Library

[21]

Smith, E. and Jonides, J. Storage and Executive Processes n the Frontal Lobes. Science, 283, (1999), 1657--1661.

[22]

Son, I.-Y., Guhe, M., Gray, W., Yazici, B. and Schoelles, M. Human performance assessment using fNIR. Proc. of SPIE The International Society for Optical Engineering, 5797. (2005), 158--169.

[23]

Wickens, C., Lee, J., Liu, Y., Becker, S. Introduction to Human Factors Engineering. Pearson, (2004).

Digital Library

[24]

Witten, I.H. and Frank, E. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, (2005), 196--197.

Digital Library

[25]

Zhang, Q., Brown, E. and Strangman, G. Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study. Journal of biomedical optics, 12, (2007), 044014-01 - 044014-12.

Cited By

De Santis AFantinelli S(2024)Digital Well-Being as a New Kind of Adaptation to the New Millennium Needs: A State-of-the-Art AnalysisElementa. Intersections between Philosophy, Epistemology and Empirical Perspectives10.7358/elementa-2023-0102-safa3:1-2Online publication date: 29-Jan-2024
https://doi.org/10.7358/elementa-2023-0102-safa
Landowska AWilson MCraven MHarrington K(2024)Adaptative computerized cognitive training decreases mental workload during working memory precision task - A preliminary fNIRS studyInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103206184:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103206
Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Show More Cited By

Index Terms

Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces

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Published In

cover image ACM Conferences

CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2009

2426 pages

ISBN:9781605582467

DOI:10.1145/1518701

General Chairs:
Dan R. Olsen
Brigham Young University
,
Richard B. Arthur
Brigham Young University
,
Program Chairs:
Ken Hinckley
Microsoft Research
,
Meredith Ringel Morris
Microsoft Research
,
Scott Hudson
Carnegie Mellon University
,
Saul Greenberg
University of Calgary

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 April 2009

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CHI '09: CHI Conference on Human Factors in Computing Systems

April 4 - 9, 2009

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CHI '09 Paper Acceptance Rate 277 of 1,130 submissions, 25%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

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https://doi.org/10.7358/elementa-2023-0102-safa
Landowska AWilson MCraven MHarrington K(2024)Adaptative computerized cognitive training decreases mental workload during working memory precision task - A preliminary fNIRS studyInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103206184:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103206
Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Cakar SYavuz F(2023)Hybrid statistical and machine learning modeling of cognitive neuroscience dataJournal of Applied Statistics10.1080/02664763.2023.217683451:6(1076-1097)Online publication date: 16-Feb-2023
https://doi.org/10.1080/02664763.2023.2176834
Patel SDuggirala MGhose ASinha A(2023)mHealth for Well-Being: Case Studies in Physiological, Cognitive, and Affective SensingmHealth and Human-Centered Design Towards Enhanced Health, Care, and Well-being10.1007/978-981-99-3989-3_5(79-100)Online publication date: 19-Jul-2023
https://doi.org/10.1007/978-981-99-3989-3_5
Putze FPutze SSagehorn MMicek CSolovey E(2022)Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and ReuseACM Transactions on Computer-Human Interaction10.1145/349055429:4(1-43)Online publication date: 31-Mar-2022
https://dl.acm.org/doi/10.1145/3490554
Kortum PChaparro B(2021)Preface to the Special Section on the Science Behind Usability and UXHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720821102690663:5(733-735)Online publication date: 9-Jul-2021
https://doi.org/10.1177/00187208211026906
Hazer-Rau DMeudt SDaucher ASpohrs JHoffmann HSchwenker FTraue H(2020)The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer InteractionSensors10.3390/s2008230820:8(2308)Online publication date: 17-Apr-2020
https://doi.org/10.3390/s20082308
Bandara DGrant THirshfield LVelipasalar S(2020)Identification of Potential Task Shedding Events Using Brain Activity DataAugmented Human Research10.1007/s41133-020-00034-y5:1Online publication date: 30-Mar-2020
https://doi.org/10.1007/s41133-020-00034-y
Liu RWalker EFriedman LArrington CSolovey E(2020)fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfacesJournal on Multimodal User Interfaces10.1007/s12193-020-00325-zOnline publication date: 2-Jun-2020
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