Skip to main content
SpringerLink
Log in

Toward a Model of Knowledge-Based Graph Comprehension

  • Conference paper
  • First Online:
Diagrammatic Representation and Inference (Diagrams 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2317))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  8. Casner, S. M. (1990). Task-analytic design of graphic presentations. Unpublished doctoral dissertation, University of Pittsburgh, Pittsburgh, PA.

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. Culbertson, H. M., Powers, R. D. (1959). A study of graph comprehension difficulties, Audio Visual Communication Review, 7, 97–100.

    Google Scholar 

  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. 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. Gattis, M., Holyoak, K. J. (1996). Mapping conceptual to spatial relations in visual reasoning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22, 231–239.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Kintsch, W. (1988). The role of knowledge in discourse comprehension. A constructionintegration model. Psychological Review, 95, 163–182.

    Article  Google Scholar 

  19. Kosslyn, S. (1989). Understanding charts and graphs. Applied Cognitive Psychology, 3, 185–225.

    Article  Google Scholar 

  20. Larkin, J. H., Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.

    Article  Google Scholar 

  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. Lohse, G. L. (1993). A cognitive model of understanding graphical perception. Human-Computer Interaction, 8, 353–388.

    Article  Google Scholar 

  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. 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. Oestermeier, U., Hesse, F. W. (2000). Verbal and visual causal arguments. Cognition, 75, 65–104.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  27. Schiano, J. D., & Tversky, B. (1992). Structure and strategy in encoding simplified graphs. Memory and Cognition, 20, 12–20.

    Google Scholar 

  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. Shah, P., Carpenter, P. A. (1995). Conceptual limitations in comprehending line graphs. Journal of Experimental Psychology: General, 124, 43–61.

    Article  Google Scholar 

  30. Shah, P., Hoeffner, J. (in press). Review of Graph Comprehension Research: Implications for Instruction. Educational Psychology Review.

    Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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, LA

    Google Scholar 

  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.

    Article  Google Scholar 

  36. Trafton, J. G., Trickett, S. B. (2001). A new model of graph and visualization usage. Unpublished manuscript.

    Google Scholar 

  37. Trolier, T. K., & Hamilton, D. L. (1986). Variables influencing judgments of correlational relations. Journal of Personality and Social Psychology, 50, 879–888.

    Article  Google Scholar 

  38. Tufte, E. R. (1983). The visual display of quantitative information, Cheshire, CT: Graphics Press.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  41. Zacks, J., Tversky, B. (1999). Bars and lines: A study of graphic communication. Memory & Cognition, 27, 1073–1079.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, University of Michigan-Flint, 411 Murchie Science Building, 48502, Flint, MI

    Eric G. Freedman

  2. Department of Psychology, University of Michigan, 525 East University, 48109-1109, Ann Arbor, MI

    Priti Shah

Authors
  1. Eric G. Freedman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Priti Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of California, 93106, Santa Barbara, CA, USA

    Mary Hegarty

  2. School of Computer Science and Software Engineering, Monash University, Clayton Campus, Wellington Road, 3800, Victoria, Australia

    Bernd Meyer

  3. Department of Computer Science and Software Engineering, Auburn University, 107 Dunstan Hall, 36849, Auburn, AL, USA

    N. Hari Narayanan

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Freedman, E.G., Shah, P. (2002). Toward a Model of Knowledge-Based Graph Comprehension. In: Hegarty, M., Meyer, B., Narayanan, N.H. (eds) Diagrammatic Representation and Inference. Diagrams 2002. Lecture Notes in Computer Science(), vol 2317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46037-3_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-46037-3_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43561-7

  • Online ISBN: 978-3-540-46037-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation