Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T14:22:12.119Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparing the Design Neurocognition of Mechanical Engineers and Architects: A Study of the Effect of Designer’s Domain

Part of: Human Behaviour in Design and Design Cognition

Published online by Cambridge University Press:  26 July 2019

Sonia Liliana da Silva Vieira ,
John S. Gero ,
Jessica Delmoral ,
Valentin Gattol ,
Carlos Fernandes  and
António A. Fernandes
Sonia Liliana da Silva Vieira*
Affiliation:
INEGI Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal;
John S. Gero
Affiliation:
Department of Computer Science and School of Architecture University of North Carolina at Charlotte, United States;
Jessica Delmoral
Affiliation:
INEGI Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal;
Valentin Gattol
Affiliation:
AIT Austrian Institute of Technology, Vienna, Austria;
Carlos Fernandes
Affiliation:
FMUP Faculty of Medicine of the University of Porto, Portugal;
António A. Fernandes
Affiliation:
FEUP Faculty of Engineering, University of Porto, Portugal
*
Contact: Vieira, Sonia Liliana da Silva, University of Porto, INEGI, Portugal, vieirasonia88@gmail.com

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

New tools from neuroscience allow design researchers to explore design neurocognition. By taking the advantage of EEG's temporal resolution we give up spatial resolution to focus on the performance of time-related design tasks. This paper presents results from an experiment using EEG to measure brain activation to study mechanical engineers and architects to compare their design neurocognition. In this study, we adopted and extended the tasks described in a previous fMRI study of design neurocognition reported in the literature. The block experiment consists of a sequence of 3 tasks: problem solving, basic design and open design using a physical interface. The block is preceded by a familiarizing pre-task using the physical interface and then extended to a fourth task using free-hand sketching. Brainwaves were collected from both mechanical engineers and architects. Results comparing 36 mechanical engineers and architects while designing were produced. These results indicate design cognition differences between the two domains in task-related power between the problem-solving task and the design tasks, in temporal resolution and transformed power.

Keywords

Design cognitionResearch methodologies and methodsHuman behaviour in designDesign domainsDesign neuroscience
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1853 - 1862
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) 2019

References

Alexiou, K, Zamenopoulos, T, Johnson, J.H. and Gilbert, S.J (2009), “Exploring the neurological basis of design cognition using brain imaging: some preliminary results”, Design Studies, Vol. 30 No. 6, pp. 623–647.Google Scholar
Akin, O. (2001), “Variants in design cognition. In, Design Knowing and Learning: Cognition in Design Education”, Editors, Eastman, C. Newstetter, W. and McCracken, M. Elsevier Science.Google Scholar
Beeman, M., Bowden, E., Haberman, J., Frymiare, J. and Arambel-Liu, S. (2004), “Neural Activity When People Solve Verbal Problems with Insight,” PLoS Biol Vol. 2 No. 4, p. e97. https://doi.org/10.1371/journal.pbio.0020097Google Scholar
Cross, N., Christiaans, H. and Dorst, K. (1996), “Analysing Design Activity”, Wiley.Google Scholar
Cross, N. and Roozenburg, N. (1992), “Modelling the Design Process in Engineering and in Architecture”, Journal of Engineering Design, Vol. 3 No. 4, pp. 325–337.Google Scholar
De Clercq, W., Vergult, A., Vanrumste, B., Van Paesschen, W. and Van Huffel, S. (2006), “Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram”, IEEE Transactions on Biomedical Engineering, Vol. 53, pp. 2583–2587.Google Scholar
Dickter, C. and Kieffaber, P. (2014), “EEG Methods for the Psychological Sciences”, Sage.Google Scholar
Ericsson, K. A. and Simon, H. A. (1993), “Protocol Analysis; Verbal Reports as Data”, MIT Press.Google Scholar
Gero, J.S and Kannengiesser, U (2014), “The Function-Behaviour-Structure ontology of design”, in Amaresh, C. and Lucienne, B. (eds), An Anthology of Theories and Models of Design, Springer, pp. 263–283.Google Scholar
Goel, V. and Pirolli, P. (1992), “The structure of design problem spaces”, Cognitive Science Vol. 16, pp. 395–429.Google Scholar
Goel, V.and Vartanian, O. (2005), “Dissociating the roles of right ventral lateral and dorsal lateral prefrontal cortex in generating and maintenance of hypotheses in set-shift problems”, Cerebral Cortex Vol. 15 No. 8, pp. 1170–1177.Google Scholar
Goucher-Lambert, K., Moss, J. and Cagan, J. (2017), “Inside the mind: Using neuroimaging to understand moral product preference judgments involving sustainability”, ASME Journal of Mechanical Design. Vol. 139 No. 4, pp. 041103–041103-11.https://doi/org/10.1115/1.4035859Google Scholar
Hinterberger, T., Zlabinger, M. and Blaser, K. (2014), “Neurophysiological correlates of various mental perspectives”, Frontiers in Human Neuroscience, Vol. 8, pp. 1–16.Google Scholar
Kan, J. W. T. and Gero, J. S. (2017), “Quantitative Methods for Studying Design protocols”, Springer.Google Scholar
Kounios, J. and Beeman, M. (2014), “The Cognitive Neuroscience of Insight”, Annual Review of Psychology, Vol. 65, pp. 71–93.Google Scholar
Liang, C., Lin, C., Yao, C., Chang, W., Liu, Y. and Chen, S. (2017), “Visual attention and association: An electroencephalography study in expert designers”, Design Studies, Vol. 48, pp. 76–95.Google Scholar
Liu, L., Nguyen, T., Zeng, Y. and Ben Hamza, A. (2016), “Identification of Relationships Between Electroencephalography (EEG) Bands and Design Activities”, ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 7: 28th International Conference on Design Theory and Methodology. Charlotte, North Carolina, USA, August 21–24, 2016.Google Scholar
Liu, L., Li, Y., Xiong, Y., Cao, J. and Yuan, P. (2018), “An EEG study of the relationship between design problem statements and cognitive behaviors during conceptual design”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing Vol. 32, pp. 351–362.Google Scholar
Martindale, C. (1999), “Biological Bases of Creativity”, In Sternberg, R. (ed.), Handbook of Creativity, Cambridge University Press, Cambridge, pp. 137–151.Google Scholar
Nguyen, P., Nguyen, T., Zeng, Y. (2017), “Empirical approaches to quantifying effort, fatigue and concentration in the conceptual design process: an EEG study”, Research in Engineering Design.Google Scholar
Pidgeon, L., Grealy, M., Duffy, A., Hay, L., McTeague, C., Vuletic, T., Coyle, D. and Gilbert, S. (2016), “Functional neuroimaging of visual creativity: a systematic review and meta-analysis”, Brain and Behavior, Vol. 6 No. 10, pp. 1–26.Google Scholar
Pfurtscheller, G. and da Silva, Lopes, F. (1999), “Event-related EEG/MEG synchronization and desynchronization: basic principles”, Clin. Neurophysiol. Vol. 110, pp. 1842–1857.Google Scholar
Schön, D. (1988), “Designing: Rules, types and worlds”, Design Studies Vol. 9 No. 3, pp. 181–190.Google Scholar
Springer, S., Deutsch, G (1998), “Left brain, right brain (5th edition)”, W.H. Freeman, San Francisco, CA.Google Scholar
Stern, R. and Ray, Q.K. (2001), “Psychophysical Recording”, Oxford University Press.Google Scholar
Van-Someren, M. W., Barnard, Y. F. and Sandberg, J. A. (1994), “The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes”, Academic Press.Google Scholar
Vecchiato, G., Jelic, A., Gaetano, T., Maglione, A., Matteis, F. and Babiloni, F. (2015), “Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural”, Conference Cognitive Processing, July.Google Scholar
Vergult, A., De Clercq, W., Palmini, A., Vanrumste, B., Dupont, P., Van Huffel, S., et al. (2007), “Improving the interpretation of ictal scalp eeg: BSS-cca algorithm for muscle artifact removal”, Epilepsia, Vol. 48, pp. 950–958.Google Scholar
Visser, W. (2009), “Design: one, but in different forms”, Design Studies, Vol. 30 No. 3, pp. 187–223.Google Scholar
Vos, D., Riès, S., Vanderperren, K., Vanrumste, B., Alario, F., Huffel, V. and Burle, B. (2010), “Removal of muscle artifacts from EEG recordings of spoken language production”, Neuroinform, Vol. 8, pp. 135–150.Google Scholar
Ward, J. (2015), “The Student's Guide to Cognitive Neuroscience”, Psychology Press.Google Scholar