Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-24T02:28:55.853Z Has data issue: false hasContentIssue false
  • English
  • Français

Creative patterns and stimulation in conceptual design

Published online by Cambridge University Press:  26 April 2010

Yan Jin  and
Oren Benami
Yan Jin
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
Oren Benami
Affiliation:
Space and Airborne Systems, Raytheon Company, El Segundo, California, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Conceptual design is a creative process. Designers create functions to satisfy customer needs and behaviors and forms to fulfill their functions. Although cognitive processes are at the center in developing new ideas, they are rarely taken into account in research and development of design support methods and systems. It is conceivable that if one understands how cognitive processes are stimulated to generate design ideas, then more effective methods and tools can be developed to support conceptual design. In this article, a cognitive model of conceptual design is developed to capture the relationships among design entities, design operations, and cognitive processes. A protocol analysis is performed to evaluate the model, and a cognitive experiment carried out to study the creative patterns and stimulating relationships. The results show that designers exhibit patterns of creative design behavior, and that these patterns can be captured and instilled into the design process to promote creativity.

Keywords

AnalogyCognitive ModelCognitive ProcessConceptual DesignProtocol AnalysisStimulation
Type
Special Issue Articles
Information
AI EDAM , Volume 24 , Issue 2: Creativity: Simulation, Stimulation, and Studies , May 2010 , pp. 191 - 209
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Altshuller, G. (1984). Creativity as an Exact Science. New York: Gordon & Breach.Google Scholar
Beveridge, M., & Parkins, E. (1987). Visual representation in analogical problem solving. Memory & Cognition 15 (3), 230–237.CrossRefGoogle ScholarPubMed
Casakin, H., & Goldschmidt, G. (1999). Expertise and the use of visual analogy: implications for design education. Design Studies 20 (2), 153–175.Google Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B. (2005). A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19 (2), 113–132.Google Scholar
Chalmers, D.J., French, R.M., & Hofstadter, D.R. (1992). High-level perception, representation, and analogy: a critique of artificial intelligence methodology. Journal of Experimental and Theoretical Artificial Intelligence 4, 185–211.Google Scholar
Chiu, I., & Shu, L.H. (2007). Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21 (1), 45–59.Google Scholar
Chusilp, P., & Jin, Y. (2006). Impact of mental iteration on conceptual design performance. Journal of Mechanical Design Transactions of ASME 128, 14–25.Google Scholar
Cross, N. (2001). Designerly ways of knowing: design discipline versus design science. Design Issues 17 (3), 49–55.Google Scholar
Cross, N. (2002). Creative cognition in design: processes of exceptional designers. Proc. 4th Creativity and Cognition Conf. (C&C-02), pp. 14–19, Loughborough.CrossRefGoogle Scholar
Cross, N., & Cross, A.C. (1998). Expertise in engineering design. Research in Engineering Design 10 (3), 141–149.CrossRefGoogle Scholar
Dreistadt, R. (1969). The use of analogies and incubation in obtaining insights in creative problem solving. Journal of Psychology 71 (2), 159–175.CrossRefGoogle Scholar
Duncker, K. (1945). On Problem-Solving. Psychological Monographs 58. Washington, DC: American Psychological Association.Google Scholar
Ericsson, K.A., & Simon, A. (1993). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Falkenhainer, B., Forbus, K.D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence 41 (1), 1–63.CrossRefGoogle Scholar
Finke, R. (1990). Creative Imagery. Hillsdale, NJ: Erlbaum.Google Scholar
Finke, R.A., Ward, T.B., & Smith, S.M. (1992). Creative Cognition: Theory, Research, and Applications. Cambridge MA: MIT Press.Google Scholar
Fisher, R.A. (1935). The Design of Experiments. Edinburgh: Oliver & Boyd.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science 7 (2), 155–170.Google Scholar
Gentner, D., & Markman, A.B. (1997). Structure mapping in analogy and similarity. American Psychologist 52 (1), 45–56.CrossRefGoogle Scholar
Gero, J.S. (1996). Creativity, emergence and evolution in design. Knowledge-Based Systems 9 (7), 435–448.CrossRefGoogle Scholar
Gero, J.S. (1998). Emergence in designing. In Proc. AID'98 Workshop on Emergence in Design, pp. 9–12, Lisbon.Google Scholar
Gero, J.S., & Kannengiesser, U. (2007). A function–behaviour–structure ontology of processes. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21 (3), 295–308.CrossRefGoogle Scholar
Gero, J.S., & Kannengiesser, U. (2008). An ontology of situated design teams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21 (3), 297–310.Google Scholar
Gero, J.S., & Maher, M.L., Eds. (1993). Modeling Creativity and Knowledge-Based Creative Design. Hillsdale, NJ: Erlbaum.Google Scholar
Gero, J.S., & McNeill, T. (1998). An approach to the analysis of design protocols. Design Studies 19, 21–61.CrossRefGoogle Scholar
Gick, M.L., & Holyoak, K.J. (1980). Analogical problem solving. Cognitive Psychology 12 (3), 306–355.CrossRefGoogle Scholar
Gick, M.L., & Holyoak, K.J. (1983). Schema induction and analogical transfer. Cognitive Psychology 15 (1), 1–38.Google Scholar
Goldschmidt, G. (1991). The dialectics of sketching. Creativity Research Journal 4, 123–143.CrossRefGoogle Scholar
Goldschmidt, G. (2003). The backtalk of self-generated sketches. Design Issues 19 (1), 72–88.Google Scholar
Goldschmidt, G., & Smolkov, M. (2006). Variances in the impact of visual stimuli on design problem solving performance. Design Studies 27 (5), 549–569.CrossRefGoogle Scholar
Gordon, W.J.J. (1961). Synectics, the Development of Creative Capacity. New York: Harpers.Google Scholar
Gross, M.D., Ervin, S.M., Anderson, J.A., & Fleischer, A. (1998). Constraints: knowledge representation in design. Design Studies 9 (3), 133–143.Google Scholar
Hofstadter, D. (1995). A review of mental leaps: analogy in creative thought. AI Magazine 16 (3), 75–80.Google Scholar
Hofstadter, D. (2001). Analogy as the core of cognition. In The Analogical Mind: Perspectives from Cognitive Science (Gentner, D., Holyoak, K.J., & Kokinov, B.N., Eds.), pp. 499–538. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Holyoak, K.J. (1985). The pragmatics of analogical transfer. In The Psychology of Learning and Motivation (Bower, G.H., Ed.), Vol. 19, pp. 59–87. New York: Academic Press.Google Scholar
Holyoak, K.J., & Koh, K. (1987). Surface and structural similarity in analogical transfer. Memory & Cognition 15, 332–340.CrossRefGoogle ScholarPubMed
Holyoak, K.J., & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science 13, 295–355.CrossRefGoogle Scholar
Holyoak, K.J., & Thagard, P. (1997). The analogical mind. American Psychologist 52 (1), 35–44.CrossRefGoogle ScholarPubMed
Jansson, D.G., & Smith, S.M. (1991). Design fixation. Design Studies 12, 3–11.CrossRefGoogle Scholar
Jin, Y., & Chusilp, P. (2006). Study of mental iteration in different design situations. Design Studies 27, 25–55.Google Scholar
Kim, Y.S., Jin, S.T., & Lee, S.W. (2006). Design activities and personal creativity characteristics: a case study of dual protocol analysis using design information and process. Proc. ASME Int. Conf. Design Theory and Methodology, Philadelphia, PA.CrossRefGoogle Scholar
Koestler, A. (1964). The Act of Creation. New York: Macmillan.Google Scholar
Kokotovich, V. (2000). Mental synthesis and creativity in design: an experimental examination. Design Studies 21, 437–449.CrossRefGoogle Scholar
Langley, P. (1979). Rediscovering physics with BACON 3. Proc. 6th Int. Joint Conf. Artificial Intelligence, pp. 505–507.Google Scholar
Lenat, D.B. (1977). Automated theory formation in mathematics. Proc. 5th Int. Joint Conf. Artificial Intelligence, pp. 833–842.Google Scholar
Linsey, J.S., Wood, K.L., & Markman, A.B. (2008). Increasing innovation: presentation and evaluation of the wordtree design by analogy method. Proc. ASME Design Theory and Methodology Conf., Paper DETC2008-49317, Brooklyn, NY.Google Scholar
Liu, Y.-T. (1994). Encoding explicit and implicit emergent subshapes based on empirical findings about human vision. In Artificial Intelligence in Design'94 (Gero, J.S., & Sudweeks, F., Eds.), pp. 401–418. Dordrecht: Kluwer.Google Scholar
McAdams, D., & Wood, K. (2000). Quantitative measures for design by analogy. Proc. ASME Design Theory and Methodology Conf., Paper DETC2000/DTM14562, Baltimore, MD.Google Scholar
McNeill, T., Gero, J.S., & Warren, J. (1998). Understanding conceptual electronic design using protocol analysis. Research in Engineering Design 10, 129–140.Google Scholar
Novick, L.R. (1988). Analogical transfer. In Analogical Reasoning Perspectives of Artificial Intelligence, Cognitive Science and Philosophy (Helman, D.H., Ed.). Amsterdam: Kluwer Academic.Google Scholar
Osborn, A. (1979). Applied Imagination. New York: Scribner.Google Scholar
Peterson, M.A. (1993). The ambiguity of mental images: insights regarding the structure of shape memory and its function in creativity. In Imagery, Creativity and Discovery: A Cognitive Approach (Roskos-Ewoldson, B., Intons-Peterson, M.J., & Anderson, R.E., Eds.), pp. 151–186. Amsterdam: Elsevier.CrossRefGoogle Scholar
Rhorbach, B. (1969). Creative nach regeln: methode 635. Eine neue technik zum losen von problemen. Absatzwirtschaft 12.Google Scholar
Schon, D.A., & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies 13, 135–156.CrossRefGoogle Scholar
Shah, J. (1998). Experimental investigation of progressive idea generation techniques in engineering design. Proc. ASME Design Theory and Methodology Conf., Atlanta, GA.CrossRefGoogle Scholar
Shah, J. (2008). Studying engineering design ideation: 20 years odyssey. Proc. NSF Workshop on Studying Design Creativity, University of Provence, Aix-en-Provence, France, March 10–11.Google Scholar
Shah, J., Santosh, V.K., & Hernandez, N. (2000). Guidelines for experimental evaluation of idea generation methods. Proc. ASME Design Theory and Methodology Conf., Baltimore, MD.Google Scholar
Shah, J., Smith, S., & Vargas-Hernandez, N. (2006). Empirical studies of design ideation 2006. ASME Design Theory Conf., Paper DETC2006-99642, Philadelphia, PA.Google Scholar
Shah, J., Vargas-Hernandez, N., & Smith, S.M. (2003). Metrics for measuring ideation effectiveness. Design Studies 22, 111–134.Google Scholar
Shah, J., Vargas-Hernandez, N., Summers, J., & Kulkarni, S. (2001). Collaborative Sketching (C-Sketch)—an idea generation technique for engineering design. Journal of Creative Behavior 35 (3), 168–198.CrossRefGoogle Scholar
Simon, H.A. (1998). The Sciences of the Artificial, 3rd ed.Cambridge, MA: MIT Press.Google Scholar
Smith, S.M., Ward, T.B., & Finke, R.A. (1995 a). The Creative Cognition Approach. Cambridge, MA: MIT Press.Google Scholar
Smith, S.M., Ward, T.B., & Finke, R.A. (1995 b). Cognitive processes in creative context. In The Creative Cognition Approach (Smith, S.M, Ward, T.B., & Finke, R.A., Eds.), pp. 1–5. Cambridge, MA: MIT Press.Google Scholar
Suwa, M., Gero, J., & Purcell, T. (2000). Unexpected discoveries and s-invention of design requirements: important vehicles for a design process. Design Studies 21, 539–567.Google Scholar
Suwa, M., Purcell, T., & Gero, J. (1998). Macroscopic analysis of design processes based on a scheme for coding designers cognitive actions. Design Studies 19 (4), 455–483.CrossRefGoogle Scholar
Taura, T., Nagai, Y., & Tanaka, S. (2005). Design space blending—a key for creative design. Int. Conf. Engineering Design (ICED 05), Melbourne, August 15–18.Google Scholar
Thompson, A., & Klatzky, R.L. (1978). Study of visual synthesis: integration of fragments into forms. Journal of Experimental Psychology: Human Perception and Performance 4 (2), 244–263.Google Scholar
Ullman, D.G. (1997). The Mechanical Design Process. New York: McGraw–Hill.Google Scholar
van Someren, M.W., Barnard, Y.F., & Sandberth, J.A.C. (1994). The Think Aloud Method: A Practical Guide to Modeling Cognitive Processes. London: Academic Press.Google Scholar
Ward, T.B. (2007). Creative cognition as a window on creativity. Methods 42 (1), 28–37.CrossRefGoogle ScholarPubMed
Yang, M. (2009). Observations on concept generation and sketching in engineering design. Research in Engineering Design 20 (1), 1–11.CrossRefGoogle Scholar