Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-06-09T09:57:28.049Z Has data issue: false hasContentIssue false
  • English
  • Français

Inspirational Stimuli Improve Idea Fluency during Ideation: A Replication and Extension Study with Eye-Tracking

Published online by Cambridge University Press:  26 May 2022

H. Dybvik ,
F. G. Abelson ,
P. Aalto ,
K. Goucher-Lambert  and
M. Steinert
H. Dybvik*
Affiliation:
Norwegian University of Science and Technology, Norway
F. G. Abelson
Affiliation:
Norwegian University of Science and Technology, Norway
P. Aalto
Affiliation:
Norwegian University of Science and Technology, Norway
K. Goucher-Lambert
Affiliation:
University of California, Berkeley, United States of America
M. Steinert
Affiliation:
Norwegian University of Science and Technology, Norway
*
henrikke.dybvik@ntnu.no

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.

We replicate a design ideation experiment (Goucher-Lambert et al., 2019) with and without inspirational stimuli and extend data collection sources to eye-tracking and a think aloud protocol to provide new insights into generated ideas. Preliminary results corroborate original findings: inspirational stimuli have an effect on idea output and questionnaire ratings. Near and far inspirational stimuli increased participants’ idea fluency over time and were rated more useful than control. We further enable experiment reproducibility and provide publicly available data.

Keywords

design cognitionconceptual designeye trackingexperimentationreplication
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 861 - 870
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), 2022.

References

Abelson, F.G., 2021. Code Repository for Design Ideation Experiment (v1.0). Zenodo. 10.5281/zenodo.5130090Google Scholar
Abelson, F.G., Dybvik, H., Steinert, M., 2021a. Dataset for Design Ideation Study. DataverseNO. 10.18710/PZQC4AGoogle Scholar
Abelson, F.G., Dybvik, H., Steinert, M., 2021b. Raw Data for Design Ideation Study. 10.21400/7KQ02WJLGoogle Scholar
Borgianni, Y., Maccioni, L., 2020. Review of the use of neurophysiological and biometric measures in experimental design research. AI EDAM 34, 248–285. 10.1017/S0890060420000062Google Scholar
Cao, J., Xiong, Y., Li, Y., Liu, L., Wang, M., 2018. Differences between beginning and advanced design students in analogical reasoning during idea generation: evidence from eye movements. Cogn Tech Work 20, 505–520. 10.1007/s10111-018-0477-zGoogle Scholar
Carter, B.T., Luke, S.G., 2020. Best practices in eye-tracking research. International Journal of Psychophysiology 155, 49–62. 10.1016/j.ijpsycho.2020.05.010Google ScholarPubMed
Colombo, S., Mazza, A., Montagna, F., Ricci, R., Monte, O.D., Cantamessa, M., 2020. NEUROPHYSIOLOGICAL EVIDENCE IN IDEA GENERATION: DIFFERENCES BETWEEN DESIGNERS AND ENGINEERS. Proceedings of the Design Society: DESIGN Conference 1, 1415–1424. 10.1017/dsd.2020.161Google Scholar
Duchowski, A.T., 2017. Eye-tracking Methodology, 3rd ed. Springer International Publishing, Cham. 10.1007/978-3-319-57883-5CrossRefGoogle Scholar
Field, A., 2018. Discovering statistics using IBM SPSS statistics, 5th edition. ed. SAGE Publications, Thousand Oaks, CA.Google Scholar
Gero, J.S., Milovanovic, J., 2020. A framework for studying design thinking through measuring designers’ minds, bodies and brains. Design Science 6. 10.1017/dsj.2020.15CrossRefGoogle Scholar
Goucher-Lambert, K., Moss, J., Cagan, J., 2019. A neuroimaging investigation of design ideation with and without inspirational stimuli—understanding the meaning of near and far stimuli. Design Studies 60, 1–38. 10.1016/j.destud.2018.07.001CrossRefGoogle Scholar
Hunter, J.D., 2007. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 9, 90–95. 10.1109/MCSE.2007.55CrossRefGoogle Scholar
Kassner, M., Patera, W., Bulling, A., 2014. Pupil: an open source platform for pervasive eye-tracking and mobile gaze-based interaction, in: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp ’14 Adjunct. Association for Computing Machinery, New York, NY, USA, pp. 1151–1160. 10.1145/2638728.2641695CrossRefGoogle Scholar
Kwon, E., Ryan, J.D., Bazylak, A., Shu, L.H., 2019. Does Visual Fixation Affect Idea Fixation? Journal of Mechanical Design 142. 10.1115/1.4045600Google Scholar
Martin, G.N., Clarke, R.M., 2017. Are Psychology Journals Anti-replication? A Snapshot of Editorial Practices. Frontiers in Psychology 8.CrossRefGoogle ScholarPubMed
McKinney, W., 2010. Data Structures for Statistical Computing in Python. Presented at the Python in Science Conference, Austin, Texas, pp. 56–61. 10.25080/Majora-92bf1922-00aGoogle Scholar
Open Science Collaboration, 2015. Estimating the reproducibility of psychological science. Science 349. 10.1126/science.aac4716Google Scholar
Peirce, J., Gray, J.R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., Lindeløv, J.K., 2019. PsychoPy2: Experiments in behavior made easy. Behav Res 51, 195–203. 10.3758/s13428-018-01193-yCrossRefGoogle ScholarPubMed
Pupil Labs, 2021. Best Practices - Tips for conducting eye-tracking experiments with the Pupil Core eye-tracking platform. [WWW Document]. Pupil Labs. URL https://docs.pupil-labs.com (accessed 4.27.21).Google Scholar
Rayner, K., 2009. Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology 62, 1457–1506. 10.1080/17470210902816461Google ScholarPubMed
Shrout, P.E., Rodgers, J.L., 2018. Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis. Annu. Rev. Psychol. 69, 487–510. 10.1146/annurev-psych-122216-011845Google ScholarPubMed
Vallat, R., 2018. Pingouin: statistics in Python. Journal of Open Source Software 3, 1026. 10.21105/joss.01026CrossRefGoogle Scholar
van Teijlingen, E.R., Hundley, V., 2001. The importance of pilot studies.Google Scholar
Wade, P. of V.P.N., Wade, N., Tatler, B.W., Tatler, L. in P.B., 2005. The Moving Tablet of the Eye: The Origins of Modern Eye Movement Research. Oxford University Press.Google Scholar
Waskom, M., 2021 . seaborn: statistical data visualization. JOSS 6, 3021. 10.21105/joss.03021Google Scholar