Advertisement

Toward a Model of Knowledge-Based Graph Comprehension

  • Eric G. Freedman
  • Priti Shah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2317)

Abstract

Research on graph comprehension has been concerned with relatively low-level information extraction. However, laboratory studies often produce conflicting findings because real-world graph interpretation requires going beyond the data presentation to make inferences and solve problems. Furthermore, in real-world settings, graphical information is presented in the context of relevant prior knowledge. According to our model, knowledge-based graph comprehension involves an interaction of top-down and bottom up processes. Several types of knowledge are brought to bear on graphs: domain knowledge, graphical skills, and explanatory skills. During the initial processing, people chunk the visual features in the graphs. Nevertheless, prior knowledge guides the processing of visual features. We outline the key assumptions of this model and show how this model explains the extant data and generates testable predictions.

Keywords

Prior Knowledge Domain Knowledge Mental Rotation Line Graph Prior Belief 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alloy, L. B., Tabachnik, N. (1984). Assessment of covariation by humans and animals: The joint influence of prior expectations and current situational information. Psychological Review, 91, 112–149.CrossRefGoogle Scholar
  2. 2.
    Anderson, C. A. (1983). Abstract and concrete data in the theory perseverance of social beliefs: When weak data lead to unshakable beliefs. Journal of Experimental Social Psychology, 19, 93–108.CrossRefGoogle Scholar
  3. 3.
    Anderson, C. A., Lepper, M. R., Ross, L. (1980). Perseverance of social theories: The role of explanation in the persistence of discredited information. Journal of Personality and Social Psychology, 39, 1037–1049.CrossRefGoogle Scholar
  4. 4.
    Broniarczyk, S. M., Alba, J. W. (1994). Theory versus data in prediction and correlation tasks. Organization Behavior and Human Decision Processes, 57, 117–139.Google Scholar
  5. 5.
    Carpenter, P. A., Shah, P. (1998). A model of the perceptual and conceptual processes in graph comprehension. Journal of Experimental Psychology: Applied, 4, 75–100.CrossRefGoogle Scholar
  6. 6.
    Carswell, C. M., Wickens, C. D. (1987). Information integration and the object display: An interaction of task demands and display superiority. Ergonomics, 30, 511-527.Google Scholar
  7. 7.
    Carswell, C. M., Emery, C., Lonon, A. M. (1993). Stimulus complexity and information integration in the spontaneous interpretation of line graphs. Applied Cognitive Psychology, 7, 341–357.CrossRefGoogle Scholar
  8. 8.
    Casner, S. M. (1990). Task-analytic design of graphic presentations. Unpublished doctoral dissertation, University of Pittsburgh, Pittsburgh, PA.Google Scholar
  9. 9.
    Casner, S. M., Larkin, J. H. (1989). Cognitive efficiency considerations for good graphic design. Proceedings of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.Google Scholar
  10. 10.
    Chapman, L. J., Chapman, J. P. (1969). Illusory correlation as an obstacle to the use of valid psychodiagnostic signs. Journal of Abnormal Psychology, 74, 271–280.CrossRefGoogle Scholar
  11. 11.
    Chinn, C. A., Brewer, W. F. (1992). Psychological responses to anomalous data. Proceedings of the 14th Annual Conference of the Cognitive Science Society, 165–170.Google Scholar
  12. 12.
    Culbertson, H. M., Powers, R. D. (1959). A study of graph comprehension difficulties, Audio Visual Communication Review, 7, 97–100.Google Scholar
  13. 13.
    Freedman, E. G., Shah, P. S. (November, 2001). Individual differences in domain knowledge, graph reading skills, and explanatory skills during graph comprehension. Paper presented at the 42nd Annual Meeting of the Psychonomic Society, Orlando, FL.Google Scholar
  14. 14.
    Freedman, E. G., Smith, L. D. (1996). The role of theory and data in covariation assessment: Implications for the theory-ladenness of observation. Journal of Mind and Behavior, 17, 321–343.Google Scholar
  15. 15.
    Gattis, M., Holyoak, K. J. (1996). Mapping conceptual to spatial relations in visual reasoning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22, 231–239.CrossRefGoogle Scholar
  16. 16.
    Hirokawa, R.Y., Gouran, D.S., Martz, A.E. (1988). Understanding the sources of faulty group decision making: A lesson from the Challenger disaster. Small Group Behavior,19, 411–433.CrossRefGoogle Scholar
  17. 17.
    Jennings, D. L., Amabile, T., Ross, L. (1982). Informal covariation assessment: Databased versus theory-based judgments. In D. Kahneman, P. Slovic, and A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.Google Scholar
  18. 18.
    Kintsch, W. (1988). The role of knowledge in discourse comprehension. A constructionintegration model. Psychological Review, 95, 163–182.CrossRefGoogle Scholar
  19. 19.
    Kosslyn, S. (1989). Understanding charts and graphs. Applied Cognitive Psychology, 3, 185–225.CrossRefGoogle Scholar
  20. 20.
    Larkin, J. H., Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.CrossRefGoogle Scholar
  21. 21.
    Lewandowsky, S., Behrens, J. T. (1999). Statistical graphs and maps. In F. T. Durson, R. S. Nickerson, R. W. Schvaneveldt, S. T. Dumais, D. S. Lindsay, and M. T. H. Chi (Eds.) Handbook of Applied Cognition (pp. 513–549). Chichester, England: John Wiley and Sons, Ltd.Google Scholar
  22. 22.
    Lohse, G. L. (1993). A cognitive model of understanding graphical perception. Human-Computer Interaction, 8, 353–388.CrossRefGoogle Scholar
  23. 23.
    Maichle, U. (1994). Cognitive processes in understanding line graphs. In W. Schnotz and R. W. Kulhavy (Eds.), Comprehension of Graphs (pp 207–226). Amsterdam, Netherlands: Elsevier Science.Google Scholar
  24. 24.
    Pinker, S. (1990). A theory of graph comprehension. In R. Freedle, (Ed.), Artificial Intelligence and the Future of Testing, (pp. 73–126). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  25. 25.
    Oestermeier, U., Hesse, F. W. (2000). Verbal and visual causal arguments. Cognition, 75, 65–104.CrossRefGoogle Scholar
  26. 26.
    Sá, W. C., West, R. F., Stanovich, K. E. (1999). The domain specificity and generality of belief bias: Searching for a generalizable critical skill. Journal of Educational Psychology, 91, 497–510.CrossRefGoogle Scholar
  27. 27.
    Schiano, J. D., & Tversky, B. (1992). Structure and strategy in encoding simplified graphs. Memory and Cognition, 20, 12–20.Google Scholar
  28. 28.
    Shah, P. (2000). Graph comprehension: The role of format, content, and individual differences. In M. Anderson, M., B. Meyer, & P. Olivier. (Ed) Diagrammatic Representation and Reasoning. Springer Verlag.Google Scholar
  29. 29.
    Shah, P., Carpenter, P. A. (1995). Conceptual limitations in comprehending line graphs. Journal of Experimental Psychology: General, 124, 43–61.CrossRefGoogle Scholar
  30. 30.
    Shah, P., Hoeffner, J. (in press). Review of Graph Comprehension Research: Implications for Instruction. Educational Psychology Review.Google Scholar
  31. 31.
    Shah, P., & Shellhammer, D. (1999). The Role of Domain Knowledge and Graph Reading Skills in Graph Comprehension. Presented at the 1999 Meeting of the Society for Applied Research in Memory and Cognition, Boulder, CO.Google Scholar
  32. 32.
    Shah, P., Freedman, E. G., Vekiri, I. (forthcoming). Graph Comprehension. In A. Miyake and P. Shah (Eds.). Handbook of Visuospatial Cognition. New York, NY: Cambridge University Press.Google Scholar
  33. 33.
    Shah, P., Mayer, R. E., & Hegarty, M. (1999). Graphs as aids to knowledge construction: Signaling techniques for guiding the process of graph comprehension. Journal of Educational Psychology, 91, 690–702.CrossRefGoogle Scholar
  34. 34.
    Shah, P., Hoeffner, J., Gergle, D., Shellhammer, D., & Anderson, N. (2000). A construction-integration approach to graph comprehension. Poster presented at the 2000 annual meeting of the psychonomics society, New Orleans, LAGoogle Scholar
  35. 35.
    Tabachneck-Schijf, H. J. M., Leonardo, A. M., Simon, H. A. (1997). CaMeRa: A Computational Model of Multiple Representations. Cognitive Science, 21, 305–350.CrossRefGoogle Scholar
  36. 36.
    Trafton, J. G., Trickett, S. B. (2001). A new model of graph and visualization usage. Unpublished manuscript.Google Scholar
  37. 37.
    Trolier, T. K., & Hamilton, D. L. (1986). Variables influencing judgments of correlational relations. Journal of Personality and Social Psychology, 50, 879–888.CrossRefGoogle Scholar
  38. 38.
    Tufte, E. R. (1983). The visual display of quantitative information, Cheshire, CT: Graphics Press.Google Scholar
  39. 39.
    Tversky, B., Schiano, D. J. (1989). Perceptual and conceptual factors in distortions in memory for graphs and maps. Journal of Experimental Psychology: General, 118, 387–398.CrossRefGoogle Scholar
  40. 40.
    Wright, J. C., Murphy, G. L. (1984). The utility of theories in intuitive statistics: The robustness of theory-based judgments. Journal of Experimental Psychology: General, 113, 301–322.CrossRefGoogle Scholar
  41. 41.
    Zacks, J., Tversky, B. (1999). Bars and lines: A study of graphic communication. Memory & Cognition, 27, 1073–1079.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Eric G. Freedman
    • 1
  • Priti Shah
    • 2
  1. 1.Department of PsychologyUniversity of Michigan-FlintFlint
  2. 2.Department of PsychologyUniversity of MichiganAnn Arbor

Personalised recommendations