Looking Beyond Self-Reported Cognitive Load: Investigating the Use of Eye Tracking in the Study of Design Representations in Engineering Design
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Cass, Madison (1); Prabhu, Rohan (2)
Series: ICED
Institution: 1: Neuroscience Program, Lafayette College;
2: Department of Mechanical Engineering, Lafayette College
Section: Design Methods
Page(s): 2475-2484
DOI number: https://doi.org/10.1017/pds.2023.248
ISBN: -
ISSN: -
Abstract
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 cognition, Industry 4.0, cognitive load, design representation