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The Neurocognition of Three Engineering Concept Generation Techniques

Part of: Human Behaviour in Design and Design Cognition

Published online by Cambridge University Press:  26 July 2019

Tripp Shealy  and
John Gero
Tripp Shealy*
Affiliation:
Virginia Tech;
John Gero
Affiliation:
University of North Carolina, Charlotte
*
Contact: Shealy, Tripp, Virginia Tech, United States of America, tshealy@vt.edu

Abstract

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Techniques and processes used for concept generation rely on composing new concepts and analysis given situational context. Composition and analysis require distinct neurocognitive function. For instance, jazz composition relies heavily on the right brain, while math relies on the left. Similar to music and math, is concept generation hemisphere dominant? What differences exist when using varying techniques? Twelve graduate engineering students were given three design tasks and instructed to use brainstorming, morphological analysis and TRIZ. A device called fNIRS measured cognitive activation. The results find left hemisphere dominance. More specifically, the left dorsolateral PFC (dlPFC), which is central to spatial working memory and filtering information. Temporal differences do exist. Morphological analysis and TRIZ reinforced the use of the left dlPFC, while brainstorming increased the use of the right dlPFC and medial PFC (mPFC) late during concept generation. The right dlPFC contributes to divergent thinking and mPFC facilitates memory retrieval. One explanation is designers relaxed rule constraints and more deeply searched for associations during brainstorming.

Keywords

Design cognitionConceptual designDesign theoryMulti- / Cross- / Trans-disciplinary processes
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1833 - 1842
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

Aziz-Zadeh, L., Liew, S.-L. and Dandekar, F. (2013), “Exploring the neural correlates of visual creativity”. Social Cognitive and Affective Neuroscience, Vol. 8 No. 4, pp. 475–480. https://doi.org/10.1093/scan/nss021Google Scholar
Ball, L.J. and Christensen, B.T. (2009), “Analogical reasoning and mental simulation in design: two strategies linked to uncertainty resolution”. Design Studies, Vol. 30 No. 2, pp. 169–186. https://doi.org/10.1016/j.destud.2008.12.005Google Scholar
Bengtsson, S.L., Csíkszentmihályi, M. and Ullén, F. (2007), “Cortical regions involved in the generation of musical structures during improvisation in pianists”. Journal of Cognitive Neuroscience, Vol. 19 No. 5, pp. 830–842. https://doi.org/10.1162/jocn.2007.19.5.830Google Scholar
Bhattacharya, J. and Petsche, H. (2002), “Shadows of artistry: cortical synchrony during perception and imagery of visual art”. Brain Research. Cognitive Brain Research, Vol. 13 No. 2, pp. 179–186.Google Scholar
Blumenfeld, R.S., Parks, C.M., Yonelinas, A.P. and Ranganath, C. (2011), “Putting the pieces together: The role of dorsolateral prefrontal cortex in relational memory encoding. Journal of Cognitive Neuroscience, Vol. 23 No. 1, pp. 257–265. https://doi.org/10.1162/jocn.2010.21459Google Scholar
Bryant, R.B.S.C.R. (2005), “CONCEPT GENERATION FROM THE FUNCTIONAL BASIS OF DESIGN”. DS 35: Proceedings ICED 05, the 15th International Conference on Engineering Design, Melbourne, Australia, 15.-18.08.2005.Google Scholar
Camarda, A., Salvia, É., Vidal, J., Weil, B., Poirel, N., Houdé, O., … Cassotti, M. (2018), “Neural basis of functional fixedness during creative idea generation: An EEG study”. Neuropsychologia, Vol. 118, pp. 4–12. https://doi.org/10.1016/j.neuropsychologia.2018.03.009Google Scholar
Cerqueira, J. J., Almeida, O. F. X. and Sousa, N. (2008), “The stressed prefrontal cortex. Left?” Right! Brain, Behavior, and Immunity, Vol. 22 No. 5, pp. 630–638. https://doi.org/10.1016/j.bbi.2008.01.005Google Scholar
Coley, F., Houseman, O. and Roy, R. (2007), “An introduction to capturing and understanding the cognitive behaviour of design engineers”. Journal of Engineering Design, Vol. 18 No. 4, pp. 311–325. https://doi.org/10.1080/09544820600963412Google Scholar
Cross, N. (2001), “Design cognition: results from protocol and other empirical studies of design activity”. In Design knowing and learning: cognition in design education (pp. 79–103). Elsevier.Google Scholar
Cross, N. (1989), Engineering design methods. Wiley.Google Scholar
Cross, N. (2006), Designerly Ways of Knowing. Retrieved from //www.springer.com/us/book/9781846283000Google Scholar
Euston, D. R., Gruber, A. J. and McNaughton, B. L. (2012), “The Role of Medial Prefrontal Cortex in Memory and Decision Making”. Neuron, Vol. 76 No. 6, pp. 1057–1070. https://doi.org/10.1016/j.neuron.2012.12.002Google Scholar
Finkelstein, Y., Vardi, J. and Hod, I. (1991), “Impulsive artistic creativity as a presentation of transient cognitive alterations”. Behavioral Medicine (Washington, D.C.), Vol. 17 No. 2, pp. 91–94. https://doi.org/10.1080/08964289.1991.9935164Google Scholar
French, J. M. (1999), Conceptual Design for Engineers | Michael J. French | Springer (3rd ed). Retrieved from //www.springer.com/us/book/9783662113646Google Scholar
Gero, J. S. (2010), “Generalizing design cognition research”. In Dorst, K., et al. (Eds), DTRS8: Interpreting Design Thinking, DAB documents, Sydney, pp. 187–198.Google Scholar
Gero, J. S., Jiang, H. and Williams, C. B. (2013), “Design cognition differences when using unstructured, partially structured, and structured concept generation creativity techniques”. International Journal of Design Creativity and Innovation, Vol. 1 No. 4, pp. 196–214. https://doi.org/10.1080/21650349.2013.801760Google Scholar
Gibson, C., Folley, B. S. and Park, S. (2009), “Enhanced divergent thinking and creativity in musicians: A behavioral and near-infrared spectroscopy study”. Brain and Cognition, Vol. 69 No. 1, pp. 162–169. https://doi.org/10.1016/j.bandc.2008.07.009Google Scholar
Halpern, M. E., Güntürkün, O., Hopkins, W. D. and Rogers, L. J. (2005), “Lateralization of the Vertebrate Brain: Taking the Side of Model Systems”. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, Vol. 25 No. 45, pp. 10351–10357. https://doi.org/10.1523/JNEUROSCI.3439-05.2005Google Scholar
Hatchuel, A. and Weil, B. (2008), “C-K design theory: an advanced formulation”. Research in Engineering Design, Vol. 19 No. 4, p. 181. https://doi.org/10.1007/s00163-008-0043-4Google Scholar
Helm, K., Jablokow, K., McKilligan, S., Daly, S. and Silk, E. (2016, June 1), Evaluating the impacts of different interventions on quality in concept generation.Google Scholar
Henson, R. N., Shallice, T. and Dolan, R. J. (1999), “Right prefrontal cortex and episodic memory retrieval: a functional MRI test of the monitoring hypothesis”. Brain: A Journal of Neurology, Vol. 122 No. Pt 7, pp. 1367–1381.Google Scholar
Howard, T. J., Culley, S. J. and Dekoninck, E. (2008), “Describing the creative design process by the integration of engineering design and cognitive psychology literature”. Design Studies, Vol. 29 No. 2, pp. 160–180. https://doi.org/10.1016/j.destud.2008.01.001Google Scholar
Howard, T. J., Dekoninck, E. A. and Culley, S. J. (2010), “The use of creative stimuli at early stages of industrial product innovation”. Research in Engineering Design, Vol. 21 No. 4, pp. 263–274. https://doi.org/10.1007/s00163-010-0091-4Google Scholar
Huppert, T. J., Diamond, S. G., Franceschini, M. A. and Boas, D. A. (2009), “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain”. Applied Optics, Vol. 48 No. 10, pp. D280–298.Google Scholar
Kaimal, G., Ayaz, H., Herres, J., Dieterich-Hartwell, R., Makwana, B., Kaiser, D. H. and Nasser, J. A. (2017), “Functional near-infrared spectroscopy assessment of reward perception based on visual self-expression: Coloring, doodling, and free drawing”. The Arts in Psychotherapy, Vol. 55, pp. 85–92. https://doi.org/10.1016/j.aip.2017.05.004Google Scholar
Luft, C. D. B., Zioga, I., Banissy, M. J. and Bhattacharya, J. (2017), “Relaxing learned constraints through cathodal tDCS on the left dorsolateral prefrontal cortex”. Scientific Reports, Vol. 7 No. 1, p. 2916. https://doi.org/10.1038/s41598-017-03022-2Google Scholar
Moon, Sungwoo, Ha, Chideok and Yang, J. (2012), “Structured Idea Creation for Improving the Value of Construction Design”. Journal of Construction Engineering and Management, Vol. 138 No. 7, pp. 841–853. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000491Google Scholar
Pisapia, N. D., Bacci, F., Parrott, D. and Melcher, D. (2016), “Brain networks for visual creativity: a functional connectivity study of planning a visual artwork”. Scientific Reports, Vol. 6, p. 39185. https://doi.org/10.1038/srep39185Google Scholar
Poldrack, R. A., Wagner, A. D., Prull, M. W., Desmond, J. E., Glover, G. H. and Gabrieli, J. D. (1999), “Functional specialization for semantic and phonological processing in the left inferior prefrontal cortex”. NeuroImage, Vol. 10 No. 1, pp. 15–35. https://doi.org/10.1006/nimg.1999.0441Google Scholar
Rosen, D. S., Erickson, B., Kim, Y. E., Mirman, D., Hamilton, R. H. and Kounios, J. (2016), “Anodal tDCS to Right Dorsolateral Prefrontal Cortex Facilitates Performance for Novice Jazz Improvisers but Hinders Experts”. Frontiers in Human Neuroscience, Vol. 10. https://doi.org/10.3389/fnhum.2016.00579Google Scholar
Shah, J. J., Kulkarni, S. V. and Vargas-Hernandez, N. (2000), “Evaluation of Idea Generation Methods for Conceptual Design: Effectiveness Metrics and Design of Experiments”. Journal of Mechanical Design, Vol. 122 No. 4, pp. 377–384. https://doi.org/10.1115/1.1315592Google Scholar
Shai, O., Reich, Y., Subrahmanian, E. and et al. (2009), Creativity Theories and Scientific Discovery: A Study of C-K Theory and Infused Design.Google Scholar
Shealy, T., Grohs, J., Hu, M., Maczka, D. and Panneton, R. (2017), Investigating Design Cognition during Brainstorming Tasks with Freshmen and Senior Engineering Students using Functional Near Infrared Spectroscopy.Google Scholar
Taura, T. and Nagai, Y. (2013), “Perspectives on Concept Generation and Design Creativity”. In Concept Generation for Design Creativity (pp. 9–20). https://doi.org/10.1007/978-1-4471-4081-8_2Google Scholar
Vidal, R., Salmeron, J. L., Mena, A. and Chulvi, V. (2015), “Fuzzy Cognitive Map-based selection of TRIZ (Theory of Inventive Problem Solving) trends for eco-innovation of ceramic industry products”. Journal of Cleaner Production, Vol. 107 No. C, pp. 202–214. https://doi.org/10.1016/j.jclepro.2015.04.131Google Scholar
Welling, H. (2007), “Four mental operations in creative cognition: The importance of abstraction”. Creativity Research Journal, Vol. 19 No. 2–3, pp. 163–177. https://doi.org/10.1080/10400410701397214Google Scholar
Zmigrod, S., Colzato, L. S. and Hommel, B. (2015), “Stimulating Creativity: Modulation of Convergent and Divergent Thinking by Transcranial Direct Current Stimulation (tDCS)”. Creativity Research Journal, Vol. 27 No. 4, pp. 353–360. https://doi.org/10.1080/10400419.2015.1087280Google Scholar