EEWC 2016: Advances in Enterprise Engineering X pp 103-117 | Cite as
Perceptual Discriminability in Conceptual Modeling
Conference paper
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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 attentionReferences
- 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.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.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.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.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.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.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.Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps. Univ. of Wisconsin Press, Madison (1983)Google Scholar
- 9.Palmer, S., Rock, I.: Rethinking perceptual organization: the role of uniform connectedness. Psychon. Bull. Rev. 1(1), 29–55 (1994)CrossRefGoogle Scholar
- 10.Winn, W.: An account of how readers search for information in diagrams. Contemp. Educ. Psychol. 18(2), 162–185 (1993)CrossRefGoogle Scholar
- 11.Wade, N., Swanston, M.: An Introduction to Visual Perception. Routledge, London (1991)Google Scholar
- 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.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.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.Duncan, J., Humphreys, G.W.: Visual search and stimulus similarity. Psychol. Rev. 96(3), 433 (1989)CrossRefGoogle Scholar
- 16.Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115 (1987)CrossRefGoogle Scholar
- 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.Quinlan, P.T.: Visual feature integration theory: past, present, and future. Psychol. Bull. 129(5), 643–673 (2003)CrossRefGoogle Scholar
- 19.Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognit. Psychol. 12(1), 97–136 (1980)CrossRefGoogle Scholar
- 20.Treisman, A., Gormican, S.: Feature analysis in early vision: evidence from search asymmetries. Psychol. Rev. 95(1), 15 (1988)CrossRefGoogle Scholar
- 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.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.Duncan, J.: Boundary conditions on parallel processing in human vision. Perception 18(4), 457–469 (1989)CrossRefGoogle Scholar
- 24.Huang, L., Pashler, H.: A Boolean map theory of visual attention. Psychol. Rev. 114(3), 599 (2007)CrossRefGoogle Scholar
- 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.Calloghan, T.C.: Dimensional interaction of hue and brightness in preattentive field segregation. Percept. Psychophys. 36(1), 25–34 (1984)CrossRefGoogle Scholar
- 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.Zugal, S., Pinggera, J., Weber, B.: Assessing process models with cognitive psychology. In: EMISA 2011, vol. 190, pp. 177–182 (2011)Google Scholar
- 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.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.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.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.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.Anderson, J.R., Pirolli, P.L.: Spread of activation. J. Exp. Psychol. Learn. Mem. Cogn. 10(4), 791 (1984)CrossRefGoogle Scholar
- 35.Weber, R.: Are attributes entities? A study of database designers’ memory structures. Inf. Syst. Res. 7(2), 137–162 (1996)CrossRefGoogle Scholar
- 36.Krippendorff, K.: Content Analysis: An Introduction to its Methodology. Sage, Thousand Oaks (2012)Google Scholar
- 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.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.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.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.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.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.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
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