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LOOKING BEYOND SELF-REPORTED COGNITIVE LOAD: INVESTIGATING THE USE OF EYE TRACKING IN THE STUDY OF DESIGN REPRESENTATIONS IN ENGINEERING DESIGN

Published online by Cambridge University Press:  19 June 2023

Madison Cass  and
Rohan Prabhu
Madison Cass
Affiliation:
Neuroscience Program, Lafayette College;
Rohan Prabhu*
Affiliation:
Department of Mechanical Engineering, Lafayette College
*
Prabhu, Rohan, Lafayette College, United States of America, prabhur@lafayette.edu

Abstract

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Designers are experiencing greater mental demands given the complexity of design tools, necessitating the study of cognitive load in design. Researchers have identified task- and designer-related factors that affect cognitive load; however, these studies primarily use self-reported measures that could be inaccurate and incomplete. Little research has tested the accuracy and completeness of self-reported measures and we aim to explore this gap. Towards this aim, we seek to answer the question: How does cognitive load vary based on the different design representations used, and do these differences depend on the measure of cognitive load? From our results, we see that the design representations vary in the range of cognitive load experienced by designers when using them. Moreover, this role of the range of cognitive load variance was observed given our use of pupil diameter. These findings call for the use of a multi-modal approach for measuring cognitive load with the combined use of subjective (e.g., self-report) and objective measures (e.g., physiological measures), as well as the use of both retrospective (e.g., self-report) and concurrent measures (e.g., physiological measures).

Keywords

Design for Additive Manufacturing (DfAM)Design cognitionIndustry 4.0cognitive loaddesign representation
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2475 - 2484
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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