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Interpretation in design: modelling how the situation changes during design activity

Abstract

This paper presents a model of the way that designers move between situations when interpreting during design activity. Three hypotheses are presented that arise from this model: that designers change their situation during interpretation, that small changes in a source can lead to large changes in the representation and that changes to the situation have their origins in the experience of the designer. The paper demonstrates how this internal movement between situations can be computationally implemented using three examples. The systems implemented demonstrate the way that interpretation can lead to changes in the situation and present an example of how the changes to a designer’s situation can be guided by past experiences.

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Notes

  1. 1.

    Gero and Smith (2009) focus upon these distinctions of definition.

  2. 2.

    The only difference here is in the inclusion of a different kind of ‘diagonal’ cross.

  3. 3.

    Algorithms were written for each of the letters A, E, H and T such that a letter of a random size was produced.

  4. 4.

    The words HAT, CAT, EAT, ATE.

References

  1. Barsalou LW (1999) Perceptual Symbol Systems. Behav Brain Sci 22:577–660

  2. Barsalou LW (2005) Abstraction as dynamic interpretation in perceptual symbol systems. In: Gershkoff-Stowe L, Rakison D (eds) Building object categories, Carnegie Symposium Series. Erlbaum, Mahwah, NJ, pp 389–431

  3. Barsalou LW (2007) Grounded Cognition. Annu Rev Psychol 59(1):617–645. doi:10.1146/annurev.psych.59.103006.093639

  4. Barsalou LW (2009) Simulation, situated conceptualization, and prediction. Philos Trans R Soc B Biol Sci 364(1521):1281–1289

  5. Beyer O, Cimiano P, Griffiths S (2012) Towards action representation within the framework of conceptual spaces: preliminary results. Paper presented at the workshops at the twenty-sixth AAAI conference on artificial intelligence

  6. Bilda Z, Gero JS, Purcell T (2006) To sketch or not to sketch? That is the question. Des Stud 27(5):587–613

  7. Carpenter GA, Grossberg S (1988) The ART of adaptive pattern recognition by a self-organizing neural network. Computer 21(3):77–88

  8. Carpenter GA, Grossberg S (1990) ART 3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Netw 3(2):129–152

  9. Chisholm RM (1982) The foundations of knowing. University of Minnesota Press, Minneapolis, MN

  10. Clancey W (1997) Situated cognition: on human knowledge and computer representations. Cambridge University Press, Cambridge

  11. Clancey WJ (1999) Conceptual coordination: how the mind orders experience in time. Erlbaum Associates, Mahwah, NJ

  12. Cross N (1982) Designerly ways of knowing. Des Stud 3(4):221–227. doi:10.1016/0142-694x(82)90040-0

  13. Dittenbach M, Merkl D, Rauber A (2000) The growing hierarchical self-organizing map. Paper presented at the neural networks, 2000. IJCNN 2000, proceedings of the IEEE-INNS-ENNS international joint conference on

  14. Fish J, Scrivener S (1990) Effects of environmental context on recognition memory in an unusual environment. Percept Mot Skills 63:1047–1050

  15. Fodor JA (1975) The language of thought. Harvard University Press, Cambridge, MA

  16. Gabora L, Rosch E, Aerts D (2008) Toward an Ecological Theory of Concepts. Ecol Psychol 20(1):84–116. doi:10.1080/10407410701766676

  17. Gärdenfors P (2000) Conceptual spaces: the geometry of thought. The MIT Press, Cambridge, MA

  18. Gero JS (1998) Conceptual designing as a sequence of situated acts. In: Smith I (ed) Artificial intelligence in structural engineering. Springer, Berlin, pp 165–177

  19. Gero JS, Fujii H (2000) A computational framework for concept formation in a situated design agent. Knowl-Based Syst 13(6):361–368

  20. Gero JS, Kannengiesser U (2004) The situated function–behaviour–structure framework. Des Stud 25(4):373–391

  21. Gero JS, Smith G (2009) Context, situations and design agents. Knowl-Based Syst 22:600–609

  22. Goel AK (1997) Design, analogy, and creativity. IEEE Expert 12(3):62–70

  23. Gombrich EH (1966) Norm and form: studies in the art of the renaissance. Phaidon, Oxford

  24. Graf P, Schacter DL (1985) Implicit and explicit memory for new associations in normal and amnesic subjects. J Exp Psychol Learn Mem Cogn 11(3):501–518. doi:10.1037/0278-7393.11.3.501

  25. Gross M, Do EY-L (2000) Drawing on the back of an envelope: a framework for interacting with application programs by freehand drawing. Comput Graph 24(6):835–849

  26. Harnad S (1999) The symbol grounding problem. Physica D 42:335–346

  27. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

  28. Jupp J (2005) Diagrammatic reasoning in design: computational and cognitive studies in similarity assessment. The University of Sydney, Sydney

  29. Jupp J, Gero J (2010) Let’s Look at Style: Visual and Spatial Representation and Reasoning in Design. In: Argamon S, Burns K, Dubnov S (eds) The structure of style. Springer, Berlin, pp 159–195

  30. Kennedy PJ, Shapiro ML (2004) Retrieving Memories via Internal Context Requires the Hippocampus. J Neurosci 24(31):6979–6985. doi:10.1523/jneurosci.1388-04.2004

  31. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480. doi:10.1109/5.58325

  32. Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Paper presented at the proceedings of the 26th annual international conference on machine learning

  33. Menezes A, Lawson B (2006) How designers perceive sketches. Des Stud 27(5):571–585

  34. Minsky M (1975) A framework for representing knowledge. In: Winston PH (ed) The psychology of computer vision. McGraw-Hill, New York, pp 211–277

  35. Montare A (1994) Knowledge acquired from learning: new evidence of hierarchical conceptualization. Percept Mot Skills 79(2):975–993. doi:10.2466/pms.1994.79.2.975

  36. Mountcastle V (1997) The columnar organization of the neocortex. Brain 120:701–722

  37. Murphy GL (2002) The big book of concepts. MIT Press, Cambridge, MA

  38. Newell A (1994) Unified theories of cognition, vol 187. Harvard University Press, Cambridge, MA

  39. Nosofsky RM (1988) Similarity, frequency, and category representations. J Exp Psychol Learn Mem Cogn 14(1):54

  40. Peng W, Gero J (2006) Concept formation in a design optimization tool. In: van Leeuwen JP, Timmermans HJP (eds) Innovations in design decision support systems in architecture and urban planning. Springer, Netherlands, pp 293–308

  41. Piaget J (1954) The construction of reality in the child. Basic Books, London

  42. Pylyshyn ZW (1977) What the mind’s eye tells the mind’s brain: a critique of mental imagery. Images Percept Knowl. Springer, Berlin, pp 1–36

  43. Pylyshyn ZW (1984) Computation and cognition. Harvard University Press, Cambridge, MA

  44. Riecke L, van Opstal A, Goebel R, Formisano E (2007) Sensory-perceptual transformations in primary auditory cortex. J Neurosci 27(46):12684–12689

  45. Rosch E (1978) Principles of Categorization. In: Rosch E, Lloyd BB (eds) Cognition and categorization. Lawrence Erlbaum Associates, Hillsdale

  46. Schacter D (1987) Implicit memory: history and current status. J Exp Psychol Learn Mem Cogn 13(3):501–518

  47. Schon D, Wiggins G (1992) Kinds of seeing and their functions in designing. Des Stud 13(2):135–156. doi:citeulike-article-id:8497732

  48. Shaffer D, Hatfield D, Svarovsky G, Nash P, Nulty A, Bagley E et al (2009) Epistemic Network Analysis: a prototype for 21st Century assessment of learning. Int J Learn Media 1(2):33–53

  49. Suwa M, Tversky B (1997) What do architects and students perceive in their design sketches? Des Stud 18(4):385–403

  50. Suwa M, Gero J, Purcell T (2000) Unexpected discoveries and S-invention of design requirements: important vehicles for a design process. Des Stud 21(6):539–567. doi:10.1016/s0142-694x(99)00034-4

  51. Thoreau HD (1851/1993) A year in Thoreau’s journal: 1851. Penguin Books, New York

  52. Tscherepanow M (2010) TopoART: a topology learning hierarchical ART network. Artif Neural Netw ICANN 2010. Springer, Berlin, pp 157–167

  53. Wright PC, Monk AF (1985) Evaluation for design. In: Sutcliffe A, Macualay L (eds) People and computers V. Cambridge University Press, Cambridge, pp 345–358

  54. Yaner PW, Goel AK (2008) Analogical recognition of shape and structure in design drawings. AI EDAM 22(2):117–128

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Acknowledgments

This work has been supported by an Australian Postgraduate Award scholarship, by the Australian Research Council under grant no. DP 0559885 and by the US National Science Foundation under Grant No. SBE-0915482. Writing support has been provided by the Pontificia Universidad Católica de Chile through the MECESUP PUC0611 project. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Australian Research Council or of the US National Science Foundation.

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Correspondence to Nick Kelly.

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Kelly, N., Gero, J.S. Interpretation in design: modelling how the situation changes during design activity. Res Eng Design 25, 109–124 (2014). https://doi.org/10.1007/s00163-013-0168-y

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Keywords

  • Situated design
  • Computational modelling
  • Interpretation