Overcoming Cognitive Challenges in Bioinspired Design and Analogy

Bioinspired design and analogy are powerful tools for innovation. Engineers face many cognitive challenges when seeking to employ design by analogy and bioinspired design. This chapter presents known difficulties engineers must overcome for bioinspired design and summarizes the cognitive psychology, multi-media learning and design evidence for the cognitive challenges. A number of cognitive challenges block a designer from being effective when using design by analogy. The challenges range from retrieving appropriate analogues based on deep similarities to the challenge of seeing multiple solutions based on a single analogue, to becoming fixated on initial solutions. Like any other idea generation process, design fixation limits the solution space explored during design by analogy and bioinspired design. There are empirically proven strategies for mitigating design fixation ranging from presenting uncommon examples to abstractions and categories of solutions. From research on multimedia learning and design, additional heuristics applicable to the design of new bioinspired tools have also been identified. These include annotations directly next to ambiguous or unfamiliar representations to enhance communication and make learning easier. Design heuristics and principles are presented after each section of the relevant research. The chapter ends with the summary of the cognitive design heuristics for bioinspired design methods and tools. This set of heuristics can be used as guidelines for researchers developing new methods and support tools for bioinspired design.

[1]  Julie S. Linsey,et al.  Inspiring Multiple Solutions from a Single Analog , 2010 .

[2]  Arthur B. Markman,et al.  Knowledge Representation , 1998 .

[3]  Mark T. Keane Analogical problem solving , 1988 .

[4]  Ashok K. Goel,et al.  DANE: Fostering Creativity in and through Biologically Inspired Design , 2011 .

[5]  L. H. Shu,et al.  Effective Analogical Transfer Using Biological Descriptions Retrieved With Functional and Biologically Meaningful Keywords , 2009 .

[6]  Matti Perttula,et al.  The idea exposure paradigm in design idea generation , 2007 .

[7]  Arthur B. Markman,et al.  INCREASING INNOVATION: PRESENTATION AND EVALUATION OF THE WORDTREE DESIGN- BY-ANALOGY METHOD , 2008 .

[8]  R. Thaler Toward a positive theory of consumer choice , 1980 .

[9]  Kenneth D. Forbus Qualitative Process Theory , 1984, Artif. Intell..

[10]  D. Gentner,et al.  Avoiding Missed Opportunities in Managerial Life: Analogical Training More Powerful Than Individual Case Training , 2000 .

[11]  Linden J. Ball,et al.  Spontaneous analogising in engineering design: a comparative analysis of experts and novices , 2004 .

[12]  Bo T. Christensen,et al.  The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design , 2007, Memory & cognition.

[13]  Arthur B. Markman,et al.  Design by Analogy: A Study of the WordTree Method for Problem Re-Representation , 2012 .

[14]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[15]  Jonathan Cagan,et al.  The Role of Functionality in the Mental Representations of Engineering Students: Some Differences in the Early Stages of Expertise , 2006, Cogn. Sci..

[16]  Brian Falkenhainer,et al.  The Structure-Mapping Engine: Algorithm and Examples , 1989, Artif. Intell..

[17]  Gregory M. Hallihan,et al.  Confirmation and Cognitive Bias in Design Cognition , 2012 .

[18]  Kristin L. Wood,et al.  “Collaborating To Success”: An Experimental Study of Group Idea Generation Techniques , 2005 .

[19]  L. H. Shu,et al.  Abstraction of Biological Analogies for Design , 2004 .

[20]  P. Paulus,et al.  Idea Generation in Groups : A Basis for Creativity in Organizations , 1994 .

[21]  Amaresh Chakrabarti,et al.  A functional representation for aiding biomimetic and artificial inspiration of new ideas , 2005, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[22]  Julie Linsey,et al.  Exploring Multiple Solutions and Multiple Analogies to Support Innovative Design , 2010, DCC.

[23]  Jonathan Cagan,et al.  The role of timing and analogical similarity in the stimulation of idea generation in design , 2008 .

[24]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[25]  J. Wiley Expertise as mental set: The effects of domain knowledge in creative problem solving , 1998, Memory & cognition.

[26]  E. Hutchins Cognition in the wild , 1995 .

[27]  Arthur B. Markman,et al.  Representing Analogies: Increasing the Probability of Innovation , 2006 .

[28]  L. H. Shu,et al.  Biomimetic design through natural language analysis to facilitate cross-domain information retrieval , 2007, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[29]  D. Gentner,et al.  Making a silk purse out of two sow's ears: young children's use of comparison in category learning. , 2002, Journal of experimental psychology. General.

[30]  John S. Gero,et al.  Fixation and Commitment While Designing and its Measurement , 2011 .

[31]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[32]  Kristin L. Wood,et al.  A Quantitative Similarity Metric for Design-by-Analogy , 2002 .

[33]  D. Gentner,et al.  Structure mapping in analogy and similarity. , 1997 .

[34]  Daniel A. McAdams,et al.  Biologically Meaningful Keywords for Functional Terms of the Functional Basis , 2011 .

[35]  Julie S. Linsey,et al.  Design Fixation in Physical Modeling: An Investigation on the Role of Sunk Cost , 2011 .

[36]  Gabriela Goldschmidt,et al.  Expertise and the use of visual analogy: implications for design education , 1999 .

[37]  Jonathan Cagan,et al.  Formal Engineering Design Synthesis , 2005 .

[38]  Julie S. Linsey,et al.  Design-by-analogy and representation in innovative engineering concept generation , 2007 .

[39]  Julie S. Linsey,et al.  Role of Sunk Cost in Engineering Idea Generation: An Experimental Investigation , 2013 .

[40]  Jacquelyn K. S. Nagel,et al.  An Engineering-to-Biology Thesaurus for Engineering Design , 2010 .

[41]  K. Holyoak,et al.  Surface and structural similarity in analogical transfer , 1987, Memory & cognition.

[42]  Ashok K. Goel,et al.  Use of design patterns in analogy-based design , 2004, Adv. Eng. Informatics.

[43]  John S. Gero,et al.  Design and other types of fixation , 1996 .

[44]  John E. Hummel,et al.  The One-to-One Constraint in Analogical Mapping and Inference , 2005, Cogn. Sci..

[45]  Jonathan Cagan,et al.  A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty , 2010 .

[46]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[47]  Paul Thagard,et al.  Analogical Mapping by Constraint Satisfaction , 1989, Cogn. Sci..

[48]  Dorla A. Evans,et al.  The effect of sunk costs on uncertain decisions in experimental markets , 1987 .

[49]  Ricardo Lopez Characterizing the Effects of Noise and Domain Distance in Analogous Design , 2012 .

[50]  H. Arkes,et al.  The Psychology of Sunk Cost , 1985 .

[51]  Benjamin Kuipers,et al.  Qualitative reasoning: Modeling and simulation with incomplete knowledge , 1994, Autom..

[52]  Arthur B. Markman,et al.  An Experimental Study of Group Idea Generation Techniques: Understanding the Roles of Idea Representation and Viewing Methods , 2011 .

[53]  K. Dugosh,et al.  Cognitive and social comparison processes in brainstorming , 2005 .

[54]  D. Gentner,et al.  Structural Alignment during Similarity Comparisons , 1993, Cognitive Psychology.

[55]  L. H. Shu,et al.  Biomimetic Concept Generation Applied to Design for Remanufacture , 2002 .

[56]  Ashok K. Goel,et al.  Biologically inspired design: process and products , 2009 .

[57]  Willett Kempton,et al.  Two Theories of Home Heat Control , 1986, Cogn. Sci..

[58]  D. Gentner Structure‐Mapping: A Theoretical Framework for Analogy* , 1983 .

[59]  Jean-François Boujut,et al.  An annotation model to reduce ambiguity in design communication , 2009 .

[60]  Julie S. Linsey,et al.  A Study on the Role of Expertise in Design Fixation and its Mitigation , 2012 .

[61]  Amaresh Chakrabarti,et al.  A BEHAVIOURAL MODEL FOR REPRESENTING BIOLOGICAL AND ARTIFICIAL SYSTEMS FOR INSPIRING NOVEL DESIGNS , 2005 .

[62]  Julie S. Linsey,et al.  Design Fixation and Its Mitigation: A Study on the Role of Expertise , 2013 .

[63]  Ann Heylighen,et al.  5.8 Analogies per Hour , 2002, AID.

[64]  George Basalla,et al.  The Evolution of Technology: Selection (2): Social and Cultural Factors , 1989 .

[65]  John E. Hummel,et al.  Distributed representations of structure: A theory of analogical access and mapping. , 1997 .

[66]  Arthur B. Markman,et al.  Increasing Innovation: A Trilogy of Experiments Towards a Design-by-Analogy Method , 2007 .

[67]  K. Holyoak,et al.  Analogical problem solving , 1980, Cognitive Psychology.

[68]  L. H. Shu,et al.  Using descriptions of biological phenomena for idea generation , 2008 .

[69]  Jami J. Shah,et al.  Evaluation of idea generation methods for conceptual design: Effectiveness metrics and design of experiments , 2000 .

[70]  Arthur B. Markman,et al.  Modality and representation in analogy , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[71]  Richard E. Mayer,et al.  e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning , 2002 .

[72]  D. Proffitt,et al.  Understanding the surface orientation of liquids , 1991, Cognitive Psychology.

[73]  R. Weisberg,et al.  Following the wrong footsteps: fixation effects of pictorial examples in a design problem-solving task. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[74]  Julie S. Linsey,et al.  Physical Models in Idea Generation: Hindrance or Help? , 2010 .