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Cognitive Load Measurement Using Arithmetic and Graphical Tasks and Galvanic Skin Response

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Computational Collective Intelligence (ICCCI 2022)

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Abstract

The results of an experiment to measure cognitive load using arithmetic and graphical tasks and galvanic skin response (GSR) biometric technique are presented in this paper. 62 volunteers were recruited to take part in a laboratory experiment conducted with the integrated iMotions biometric platform. Data were collected using observations, Single Ease Question (SEQ) and NASA Task Load Index (NASA-TLX) self-report questionnaires, and GSR measurements. The 18 performance, subjective, and psychophysiological indicators were calculated from the collected data to measure the cognitive load associated with arithmetic and graphical tasks. Nonparametric tests of statistical significance of differences between individual metrics were made for the easy, medium, and hard arithmetic and graphical tasks. The conducted research proved the usefulness of most measures in the analysis of the cognitive load associated with arithmetic and graphical tasks.

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Author information

Authors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Patient Zihisire Muke, Zbigniew Telec & Bogdan Trawiński

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  1. Patient Zihisire Muke
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  2. Zbigniew Telec
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  3. Bogdan Trawiński
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Corresponding author

Correspondence to Bogdan Trawiński .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Zihisire Muke, P., Telec, Z., Trawiński, B. (2022). Cognitive Load Measurement Using Arithmetic and Graphical Tasks and Galvanic Skin Response. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_66

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_66

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