research-article

Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks

Authors:

Nargess Nourbakhsh,

Rafael A. CalvoAuthors Info & Claims

OzCHI '12: Proceedings of the 24th Australian Computer-Human Interaction Conference

Pages 420 - 423

https://doi.org/10.1145/2414536.2414602

Published: 26 November 2012 Publication History

Abstract

Galvanic Skin Response (GSR) has recently attracted researchers' attention as a prospective physiological indicator of cognitive load and emotions. However, it has commonly been investigated through single or few measures and in one experimental scenario. In this research, aiming to perform a comprehensive study, we have assessed GSR data captured from two different experiments, one including text reading tasks and the other using arithmetic tasks, each imposing multiple cognitive load levels. We have examined temporal and spectral features of GSR against different task difficulty levels. ANOVA test was applied for the statistical evaluation. Obtained results show the strong significance of the explored features, especially the spectral ones, in cognitive workload measurement in the two studied experiments.

References

[1]

Berka, C., Levendowski, D. J., Lumicao, M. N., Yau, A., Davis, G., Zivkovic, V. T., Olmstead, R. E., Tremoulet, P. D., Craven, P. L. EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks. Aviation, Space, and Environmental Medicine, 78, 5 (2007), B231--44.

[2]

Chandler, P., Sweller, J. Cognitive Load While Learning to Use a Computer Program. Applied Cognitive Psychology, 10, 2 (1996), 151--170.

[3]

Engstrom, J., Johansson, E., Ostlund, J. Effects of Visual and Cognitive Load in Real and Simulated Motorway Driving. Transportation Research Part F, 8, 2 (2005), 97--120.

[4]

Haapalainen, E., Seungjun, K., Forlizzi, J. F., Dey, A. K. Psycho-Physiological Measures for Assessing Cognitive Load, In Proc. Ubicomp 2010, ACM Press (2010).

Digital Library

[5]

Huttunen, K., Keränen, H., Väyrynen, E., Pääkkönen, R., Leino, T. Effect of Cognitive Load on Speech Prosody in Aviation: Evidence from Military Simulator Flights. Applied Ergonomics, 42 (2011), 348--357.

[6]

Ikehara, C. S., Crosby, M. E. Assessing Cognitive Load with Physiological Sensors. In Proc. HICSS 38 (2005), 295a -- 295a.

Digital Library

[7]

Leyman, E., Mirka, G., Kaber, D., Sommerich, C., (2004). Cervicobrachial Muscle Response to Cognitive Load in a Dual-Task Scenario. Ergonomics, 47(6), 625--45.

[8]

Nakasone, A.; Prendinger, H. & Ishizuka, M. Emotion Recognition from Electromyography and Skin Conductance, In Proc. BSI 2005 (2005), 219--222.

[9]

Paas, F. G. W. C. Training Strategies for Attaining Transfer of Problem-Solving Skill in Statistics: A Cognitive-Load Approach. Journal of Educational Psychology, 84, 4 (1992), 429--434.

[10]

Paas, F. G. W. C., Van Merriënboer, J. G. Instructional Control of Cognitive Load in the Training of Complex Cognitive Tasks. Educational Psychology Review, 6, 4 (1994), 351--371.

[11]

Saadé, R. G. & Otrakji, C. A. First impressions last a lifetime: effect of interface type on disorientation and cognitive load. Computers in Human Behaviour, 23, 1 (2007), 525--535.

[12]

Setz, C., Arnrich, B., Schumm, J., La Marca, R., Tröster, G. Discriminating Stress from Cognitive Load Using a Wearable EDA Device. Technology, 14, 2 (2010), 410--417.

Digital Library

[13]

Shi, Y., Ruiz, N., Taib, R., Choi, E. H., Chen, F. Galvanic Skin Response (GSR) as an Index of Cognitive Load. In Proc. CHI 2007 Work-in-Progress, (2007), 2651--2656.

Digital Library

[14]

Sweller, J., Cognitive Load Theory, Learning Difficulty, and Instructional Design. Learning and Instruction, 4, 4 (1994), 295--312.

[15]

Wilson, G. F. An Analysis of Mental Workload in Pilots During Flight Using Multiple Psychophysiological Measures. The International Journal of Aviation 12, 1 (2009), 3--18.

[16]

Xu, J., Wang, Y., Chen, F., Choi, H., Li, G., Chen, S., Hussain, S. Pupillary Response Based Cognitive Workload Index Under Luminance and Emotional Changes. In Proc. CHI 2011, ACM Press (2011), 1627--1632.

Digital Library

Cited By

Page CLiu CMeltzer JGhosh Hajra S(2024)Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking EnvironmentsSensors10.3390/s2404108224:4(1082)Online publication date: 7-Feb-2024
https://doi.org/10.3390/s24041082
Lennon-Maslin MQuaiser-Pohl C(2024)“It’s Different for Girls!” The Role of Anxiety, Physiological Arousal, and Subject Preferences in Primary School Children’s Math and Mental Rotation PerformanceBehavioral Sciences10.3390/bs1409080914:9(809)Online publication date: 12-Sep-2024
https://doi.org/10.3390/bs14090809
Sazuka NKatsumata KKomoriya YOba TOhira H(2024)Association of brain–autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learningFrontiers in Human Neuroscience10.3389/fnhum.2024.127212118Online publication date: 29-Feb-2024
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Index Terms

Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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OzCHI '12: Proceedings of the 24th Australian Computer-Human Interaction Conference

November 2012

692 pages

ISBN:9781450314381

DOI:10.1145/2414536

Copyright © 2012 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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New Zealand Chapter of ACM SIGCHI
Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

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OzCHI '12

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Human Factors & Ergonomics Soc

OzCHI '12: The 24th Australian Computer-Human Interaction Conference

November 26 - 30, 2012

Melbourne, Australia

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https://doi.org/10.3390/s24041082
Lennon-Maslin MQuaiser-Pohl C(2024)“It’s Different for Girls!” The Role of Anxiety, Physiological Arousal, and Subject Preferences in Primary School Children’s Math and Mental Rotation PerformanceBehavioral Sciences10.3390/bs1409080914:9(809)Online publication date: 12-Sep-2024
https://doi.org/10.3390/bs14090809
Sazuka NKatsumata KKomoriya YOba TOhira H(2024)Association of brain–autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learningFrontiers in Human Neuroscience10.3389/fnhum.2024.127212118Online publication date: 29-Feb-2024
https://doi.org/10.3389/fnhum.2024.1272121
Uğuz Arsu Ş(2024)YÖNETİMDE YENİ BİR KAVRAM OLARAK NÖROYÖNETİM: DENEYSEL ÇALIŞMALARIN SİSTEMATİK OLARAK İNCELENMESİPamukkale University Journal of Social Sciences Institute10.30794/pausbed.1398796Online publication date: 22-Mar-2024
https://doi.org/10.30794/pausbed.1398796
Wang ZOuyang YZeng H(2024)ARFN: An Attention-Based Recurrent Fuzzy Network for EEG Mental Workload AssessmentIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.336914373(1-14)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3369143
Wang JWang AHu HWu KHe D(2024)Multi-Source Domain Generalization for ECG-Based Cognitive Load Estimation: Adversarial Invariant and Plausible Uncertainty LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447676(1631-1635)Online publication date: 14-Apr-2024
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Yang PLiu NLiu XShu YJi WRen ZSheng JYu MYi RZhang DLiu Y(2024)A Multimodal Dataset for Mixed Emotion RecognitionScientific Data10.1038/s41597-024-03676-411:1Online publication date: 5-Aug-2024
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Brillinger MManfredi SLeder DBloder MJäger MDiwold KKajmakovic AHaslgrübler MPichler RBrunner MMehr SMalisa V(2024)Physiological workload assessment for highly flexible fine-motory assembly tasks using machine learningComputers and Industrial Engineering10.1016/j.cie.2023.109859188:COnline publication date: 17-Apr-2024
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Pan HPayre WGao ZWang Y(2024)Exploring driving anger-caused impairment of takeover performance among professional taxi drivers during partially automated drivingAccident Analysis & Prevention10.1016/j.aap.2024.107686205(107686)Online publication date: Sep-2024
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Pishghadam RShayesteh SDaneshvarfard FBoustani NSeyednozadi ZZabetipour MPishghadam M(2024)Cognition-Emotion Interaction during L2 Sentence Comprehension: The Correlation of ERP and GSR Responses to Sense CombinationsJournal of Psycholinguistic Research10.1007/s10936-024-10039-y53:1Online publication date: 28-Jan-2024
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