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EXAMINING THE BOUNDARY BETWEEN NEAR AND FAR DESIGN STIMULI

Published online by Cambridge University Press:  19 June 2023

Elisa Kwon  and
Kosa Goucher-Lambert
Elisa Kwon*
Affiliation:
University of California, Berkeley
Kosa Goucher-Lambert
Affiliation:
University of California, Berkeley
*
Kwon, Elisa, University of California, Berkeley United States of America, [email protected]

Abstract

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External sources of inspiration can promote the discovery of new ideas as designers ideate on a design task. Data-driven techniques can increasingly enable the retrieval of inspirational stimuli based on nontext-based representations, beyond semantic features of stimuli. However, there is a lack of fundamental understanding regarding how humans evaluate similarity between non-semantic design stimuli (e.g., visual). Toward this aim, this work examines human-evaluated and computationally derived representations of visual and functional similarities of 3D-model parts. A study was conducted where participants (n=36) assessed triplet ratings of parts and categorized these parts into groups. Similarity is defined by distances within embedding spaces constructed using triplet ratings and deep-learning methods, representing human and computational representations. Distances between stimuli that are grouped together (or not) are determined to understand how various methods and criteria used to define non-text-based similarity align with perceptions of 'near' and 'far'. Distinct boundaries in computed distances separating stimuli that are 'too far' were observed, which include farther stimuli when modeling visual vs. functional attributes.

Keywords

CreativityHuman behaviour in designDesign theory
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1725 - 1734
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

References

Ahmed, F., Ramachandran, S., Fuge, M., Hunter, S. and Miller, S. (2019), “Interpreting Idea Maps: Pairwise Comparisons Reveal What Makes Ideas Novel”, J. Mech. Des., Vol. 141 No. 2, p. 021102, http://doi.org/10.1115/1.4041856.CrossRefGoogle Scholar
Chan, J., Dow, S.P. and Schunn, C. (2015), “Do the Best Design Ideas (Really) Come from Conceptually Distant Sources of Inspiration?”, Des. Stud., Vol. 36, pp. 31–58, http://doi.org/10.1016Zj.destud.2014.08.001.CrossRefGoogle Scholar
Chaudhari, A.M., Bilionis, I. and Panchal, J.H. (2019), “Similarity in Engineering Design: A Knowledge-Based Approach”, in: Proc. of ASMEIDETC/CIE, Anaheim, CA, USA, p. V007T06A045, http://doi.org/10.1115/DETC2019-98272.CrossRefGoogle Scholar
Cooke, T., Jakel, F., Wallraven, C. and Bulthoff, H.H. (2007), “Multimodal similarity and categorization of novel, three-dimensional objects”, Neuropsychologia, Vol. 45 No. 3, pp. 484–495, http://doi.org/10.1016/j.neuropsychologia.2006.02.009.CrossRefGoogle ScholarPubMed
Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C. and Wood, K. (2013), “The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output”, J. Mech. Des., Vol. 135 No. 2, p. 021007, http://doi.org/10.1115/L4023158.CrossRefGoogle Scholar
Goucher-Lambert, K. and Cagan, J. (2019), “Crowdsourcing Inspiration: Using Crowd Generated Inspirational Stimuli to Support Designer Ideation”, Des. Stud., Vol. 61, pp. 1–29, http://doi.org/10.1016/jj.destud.2019.01.001.CrossRefGoogle Scholar
Jansson, D. and Smith, S. (1991), “Design fixation”, Des. Stud., Vol. 12 No. 21, pp. 3–11, http://doi.org/10.1016/0142-694X(91)90003-F.CrossRefGoogle Scholar
Jiang, S., Hu, J., Wood, K.L. and Luo, J. (2022), “Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions”, J. Mech. Des., Vol. 144 No. 2, p. 020801, http://doi.org/10.1115/L4051681.CrossRefGoogle Scholar
Jiang, S., Luo, J., Ruiz-Pava, G., Hu, J. and Magee, C.L. (2021), “Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks”, J. Mech. Des, Vol. 143 No. 6, p. 061405, http://doi.org/10.1115/1.4049214.CrossRefGoogle Scholar
Kim, J. and Maher, M.L. (2023), “The effect of AI-based inspiration on human design ideation”, Int. J. Des. Creat. Innov., pp. 1–18, http://doi.org/10.1080/21650349.2023.2167124.CrossRefGoogle Scholar
Kwon, E., Huang, F. and Goucher-Lambert, K. (2022a), “Enabling multi-modal search for inspirational design stimuli using deep learning”, AIEDAM, Vol. 36, p. e22, http://doi.org/10.1017/S0890060422000130.CrossRefGoogle Scholar
Kwon, E., Rao, V. and Goucher-Lambert, K. (2022b), “Investigating the roles of expertise and modality in designers' search for inspirational stimuli”, in: Proc. of ASME IDETC/CIE, St. Louis, MO, USA, p. V006T06A015, http://doi.org/10.1115/DETC2022-90638.CrossRefGoogle Scholar
Kwon, E., Rao, V. and Goucher-Lambert, K. (2023), “Exploring designers' encounters with unexpected inspirational stimuli”, in: Gero, J.S. (Editor), Design Computing and Cognition'22, Springer International Publishing, Cham, pp. 397–408, http://doi.org/10.1007/978-3-031-20418-0_24.CrossRefGoogle Scholar
Mo, K., Zhu, S., Chang, A.X., Yi, L., Tripathi, S., Guibas, L.J. and Su, H. (2019), “Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding”, in: 2019IEEE/CVF Conference on CVPR, p. 909–918, http://doi.org/10.1109/CVPR.2019.00100.CrossRefGoogle Scholar
Nandy, A. and Goucher-Lambert, K. (2022), “Do Human and Computational Evaluations of Similarity Align? An Empirical Study of Product Function”, J. Mech. Des., Vol. 144 No. 4, p. 041404, http://doi.org/10.1115/1.4053858.CrossRefGoogle Scholar
Roads, B. and Mozer, M. (2019), “Obtaining psychological embeddings through joint kernel and metric learning”, Behav. Res., Vol. 51, pp. 2180–2193, http://doi.org/10.3758/s13428-019-01285-3.CrossRefGoogle ScholarPubMed
Sarica, S., Song, B., Luo, J. and Wood, K. (2021), “Idea generation with technology semantic network”, AIEDAM, Vol. 35, pp. 265–283, http://doi.org/10.1017/S0890060421000020.CrossRefGoogle Scholar
Sio, U.N., Kotovsky, K. and Cagan, J. (2015), “Fixation or inspiration? a meta-analytic review of the role of examples on design processes”, Des. Stud., Vol. 39, pp. 70–99, http://doi.org/10.1016/jj.destud.2015.04.004.CrossRefGoogle Scholar
Tangelder, J. and Veltkamp, R. (2004), “A survey of content based 3d shape retrieval methods”, in: Proceedings Shape Modeling Applications, 2004., pp. 145–156, http://doi.org/10.1109/SMI.2004.1314502.CrossRefGoogle Scholar
Zhang, Z. and Jin, Y. (2021), “Toward Computer Aided Visual Analogy Support (CAVAS): Augment Designers through Deep Learning”, in: Proc. of ASME IDETC/CIE, Virtual, Online, p. V006T06A057, http://doi.org/10.1115/detc2021-70961.CrossRefGoogle Scholar