Perceptual Discriminability in Conceptual Modeling

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 252)

Abstract

Perceptual discriminability can be used to help distinguishing modeling constructs in conceptual models. It can further be used to produce parallel processing of modeling constructs that make these constructs virtually pop-out from the model. Moody has described a condition which is necessary to produce a pop-out effect in his principle of perceptual discriminability. This work extends the principle of perceptual discriminability for further conditions to produce a pop-out. Extended perceptual discriminability is exemplarily applied to a modeling grammar.

Keywords

Conceptual modeling Parallel processing Pop-out Perceptual discriminability Visual attention 

References

  1. 1.
    Moody, D.: The “physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6), 756–779 (2009)CrossRefGoogle Scholar
  2. 2.
    Caire, P., Genon, N., Heymans, P., Moody, D.: Visual notation design 2.0: towards user comprehensible requirements engineering notations. In: 21st Requirements Engineering Conference, pp. 115–124 (2013)Google Scholar
  3. 3.
    Mendling, J., Reijers, H.A., van der Aalst, W.M.: Seven process modeling guidelines (7PMG). Inf. Softw. Technol. 52(2), 127–136 (2010)CrossRefGoogle Scholar
  4. 4.
    Blackwell, A.F., Britton, C., Cox, A., et al.: Cognitive dimensions of notations: design tools for cognitive technology. In: Beynon, M., Nehaniv, C.L., Dautenhahn, K. (eds.) CT 2001. LNCS (LNAI), vol. 2117, pp. 325–341. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Green, T.R., Petre, M.: Usability analysis of visual programming environments: a ‘cognitive dimensions’ framework. J. Vis. Lang. Comput. 7(2), 131–174 (1996)CrossRefGoogle Scholar
  6. 6.
    Genon, N., Heymans, P., Amyot, D.: Analysing the cognitive effectiveness of the BPMN 2.0 visual notation. In: Malloy, B., Staab, S., van den Brand, M. (eds.) SLE 2010. LNCS, vol. 6563, pp. 377–396. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Figl, K., Mendling, J., Strembeck, M.: The influence of notational deficiencies on process model comprehension. J. Assoc. Inf. Syst. 14(6), 312 (2013)Google Scholar
  8. 8.
    Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps. Univ. of Wisconsin Press, Madison (1983)Google Scholar
  9. 9.
    Palmer, S., Rock, I.: Rethinking perceptual organization: the role of uniform connectedness. Psychon. Bull. Rev. 1(1), 29–55 (1994)CrossRefGoogle Scholar
  10. 10.
    Winn, W.: An account of how readers search for information in diagrams. Contemp. Educ. Psychol. 18(2), 162–185 (1993)CrossRefGoogle Scholar
  11. 11.
    Wade, N., Swanston, M.: An Introduction to Visual Perception. Routledge, London (1991)Google Scholar
  12. 12.
    Agarwal, R., Sinha, A.P., Tanniru, M.: Cognitive fit in requirements modeling: a study of object and process methodologies. J. Manag. Inf. Syst. 13, 137–162 (1996)CrossRefGoogle Scholar
  13. 13.
    Healey, C.G., Enns, J.T.: Attention and visual memory in visualization and computer graphics. IEEE Trans. Vis. Comput. Graph. 18(7), 1170–1188 (2012)CrossRefGoogle Scholar
  14. 14.
    Wolfe, J.M., Cave, K.R., Franzel, S.L.: Guided search: an alternative to the feature integration model for visual search. J. Exp. Psychol. Hum. Percept. Perform. 15(3), 419 (1989)CrossRefGoogle Scholar
  15. 15.
    Duncan, J., Humphreys, G.W.: Visual search and stimulus similarity. Psychol. Rev. 96(3), 433 (1989)CrossRefGoogle Scholar
  16. 16.
    Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115 (1987)CrossRefGoogle Scholar
  17. 17.
    Reijers, H.A., Freytag, T., Mendling, J., Eckleder, A.: Syntax highlighting in business process models. Decis. Support Syst. 51(3), 339–349 (2011)CrossRefGoogle Scholar
  18. 18.
    Quinlan, P.T.: Visual feature integration theory: past, present, and future. Psychol. Bull. 129(5), 643–673 (2003)CrossRefGoogle Scholar
  19. 19.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognit. Psychol. 12(1), 97–136 (1980)CrossRefGoogle Scholar
  20. 20.
    Treisman, A., Gormican, S.: Feature analysis in early vision: evidence from search asymmetries. Psychol. Rev. 95(1), 15 (1988)CrossRefGoogle Scholar
  21. 21.
    Treisman, A.: Search, similarity, and integration of features between and within dimensions. J. Exp. Psychol. Hum. Percept. Perform. 17(3), 652 (1991)CrossRefGoogle Scholar
  22. 22.
    Snowden, R.J.: Texture segregation and visual search: a comparison of the effects of random variations along irrelevant dimensions. J. Exp. Psychol. Hum. Percept. Perform. 24(5), 1354–1367 (1998)CrossRefGoogle Scholar
  23. 23.
    Duncan, J.: Boundary conditions on parallel processing in human vision. Perception 18(4), 457–469 (1989)CrossRefGoogle Scholar
  24. 24.
    Huang, L., Pashler, H.: A Boolean map theory of visual attention. Psychol. Rev. 114(3), 599 (2007)CrossRefGoogle Scholar
  25. 25.
    Callaghan, T.C.: Interference and dominance in texture segregation: hue, geometric form, and line orientation. Percept. Psychophys. 46(4), 299–311 (1989)CrossRefGoogle Scholar
  26. 26.
    Calloghan, T.C.: Dimensional interaction of hue and brightness in preattentive field segregation. Percept. Psychophys. 36(1), 25–34 (1984)CrossRefGoogle Scholar
  27. 27.
    Healey, C.G., Enns, J.T.: Large datasets at a glance: combining textures and colors in scientific visualization. IEEE Trans. Vis. Comput. Graph. 5(2), 145–167 (1999)CrossRefGoogle Scholar
  28. 28.
    Zugal, S., Pinggera, J., Weber, B.: Assessing process models with cognitive psychology. In: EMISA 2011, vol. 190, pp. 177–182 (2011)Google Scholar
  29. 29.
    Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81 (1956)CrossRefGoogle Scholar
  30. 30.
    Cowan, N.: The magical mystery four how is working memory capacity limited, and why? Curr. Dir. Psychol. Sci. 19(1), 51–57 (2010)CrossRefGoogle Scholar
  31. 31.
    Natschläger, C.: Deontic BPMN. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 264–278. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  32. 32.
    Zur Muehlen, M., Recker, J.: How much language is enough? Theoretical and practical use of the business process modeling notation. In: Advanced Information Systems Engineerin, pp. 465–479 (2008)Google Scholar
  33. 33.
    Collins, A.M., Quillian, M.R.: Experiments on semantic memory and language comprehension. In: Gregg, L.W. (ed.) Cognition in Learning and Memory. Wiley, New York (1972)Google Scholar
  34. 34.
    Anderson, J.R., Pirolli, P.L.: Spread of activation. J. Exp. Psychol. Learn. Mem. Cogn. 10(4), 791 (1984)CrossRefGoogle Scholar
  35. 35.
    Weber, R.: Are attributes entities? A study of database designers’ memory structures. Inf. Syst. Res. 7(2), 137–162 (1996)CrossRefGoogle Scholar
  36. 36.
    Krippendorff, K.: Content Analysis: An Introduction to its Methodology. Sage, Thousand Oaks (2012)Google Scholar
  37. 37.
    Lowry, P.B., Moody, D., Gaskin, J., Galletta, D.F., Humphreys, S., Barlow, J.B., Wilson, D.: Evaluating journal quality and the association for information systems (AIS) senior scholars’ journal basket via bibliometric measures: do expert journal assessments add value? MIS Q. 37(4), 993–1012 (2013)Google Scholar
  38. 38.
    Gemino, A., Wand, Y.: Complexity and clarity in conceptual modeling: comparison of mandatory and optional properties. Data Knowl. Eng. 55(3), 301–326 (2005)CrossRefGoogle Scholar
  39. 39.
    Genero, M., Poels, G., Piattini, M.: Defining and validating metrics for assessing the understandability of entity–relationship diagrams. Data Knowl. Eng. 64(3), 534–557 (2008)CrossRefGoogle Scholar
  40. 40.
    Khatri, V., Vessey, I., Ramesh, V., Clay, P., Park, J.-S.: Understanding conceptual schemas: exploring the role of application and IS domain knowledge. Inf. Syst. Res. 17(1), 81–99 (2006)CrossRefGoogle Scholar
  41. 41.
    Bodart, R., Patel, A., Sim, M., Weber, R.: Should optional properties be used in conceptual modelling? A theory and three empirical tests. Inf. Syst. Res. 12(4), 384–405 (2001)CrossRefGoogle Scholar
  42. 42.
    Masri, K., Parker, D., Gemino, A.: Using iconic graphics in entity-relationship diagrams: the impact on understanding. J. Database Manag. 19(3), 22 (2008)CrossRefGoogle Scholar
  43. 43.
    Parsons, J.: Effects of local versus global schema diagrams on verification and communication in conceptual data modeling. J. Manag. Inf. Syst. 19(3), 155–183 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Chair of Wirtschaftsinformatik, esp. Systems DevelopmentTechnische Universität DresdenDresdenGermany

Personalised recommendations