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Facilitating Conditional Probability Problems with Visuals

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Human-Computer Interaction. Interaction Platforms and Techniques (HCI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 4551))

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Abstract

In tasks such as disease diagnosis, interpretation of evidence in criminal trials and management of security and risk data, people need to process conditional probabilities to make critical judgments and decisions. As dual-coding theory and the cognitive theory of multimedia learning (CTML) would predict, visual representations (VRs) should aid in these tasks. Conditional probability problems are difficult and require subjects to build a mental model of set inclusion relationships to solve them. Evidence from neurological research confirms that mental model construction relies on visual spatial processing. Prior research has shown conflicting accounts of whether visuals aid in these problems. Prior research has also revealed that individuals differ in their ability to perform spatial processing tasks. Do visuals help solve these problems? Do visualization interface designers need to take into account the nuances of spatial processing and individual differences? This study uses a 3x2 factorial design to determine the relationship between subject’s spatial abilities (high or low) and visual and text representations on user performance and satisfaction.

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References

  1. Brase, G.L.: Which statistical formats facilitate what decisions? The perception and influence of different statistical information formats. Journal of Behavioral Decision Making. 15, 381–401 (2002)

    Article  Google Scholar 

  2. Brase, G.L. (2006)

    Google Scholar 

  3. Burns, K.: Painting pictures to augment advice. In: Burns, K. (ed.) AVI 2004, pp. 344–349. ACM, New York (May 25-28, 2004)

    Chapter  Google Scholar 

  4. Card, S.K., Mackinlay, J.D., Shneiderman, B.: Information Visualization: Using Vision to Think. Academic Press, London (1999)

    Google Scholar 

  5. Cole, W.G: Understanding Bayesian reasoning via graphical displays. In: CHI 1989 Proceedings, pp. 381–386. ACM, New York (1989)

    Google Scholar 

  6. Davis, F.D.: Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 319–340 (September 1989)

    Google Scholar 

  7. Dull, R.B., Tegarden, D.P.: A comparison of three visual representations of complex multidimensional accounting information. Journal of Information Systems 13, 117–131 (1999)

    Google Scholar 

  8. Dean, G.M., Morris, P.E.: The relationship between self-reports of imagery and spatial ability. British Journal of Psychology 94, 245–273 (2003)

    Article  Google Scholar 

  9. Ekstrom, R.B., French, J.W., Harman, H.H.: Manual for kit of factor-referenced cognitive tests. Educational Testing Service, Princeton (1976)

    Google Scholar 

  10. Gigerenzer, G., Edwards, A.: Simple tools for understanding risks: from innumeracy to insight. BMJ 327, 741–744 (2003)

    Article  Google Scholar 

  11. Gigerenzer, G., Hoffrage, U.: How to improve Bayesian reasoning without instruction: frequency formats. frequency formats. Psychological Review 102, 684–701 (1995)

    Google Scholar 

  12. Girotto, V., Gonzalez, M.: Solving probabilistic and statistical problems: a matter of information structure and question form. Cognition 78, 247–276 (2001)

    Article  Google Scholar 

  13. Johnson-Laird, P.N., Legrenzi, P., Girotto, V., Legrenzi, M.S., Caverni, J.: Naïve probability: a mental model theory of extensional reasoning. Psychological Review 106(1), 62–88 (1999)

    Article  Google Scholar 

  14. Kahneman, Daniel, Slovic, Paul, Tversky, Amos.: Judgement Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge (1988)

    Google Scholar 

  15. Kastner, S., Ungerleider, L.G.: Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience 23, 315–341 (2000)

    Article  Google Scholar 

  16. Kossyln, S.M., Ganis, G., Thompson, W.L.: Neural foundations of imagery. Nature Review, Neuroscience 2(9), 635–642 (2001)

    Article  Google Scholar 

  17. Knauff, M., Fangmeier, T., Ruff, C., Johnson-Laird, P.N.: Reasoning, models, images: behavioral measures and cortical activity. Journal of cognitive neuroscience 15(4), 559–573 (2003)

    Article  Google Scholar 

  18. Knauff, M., Johnson-Laird, P.N.: Visual imagery can impede reasoning. Memory & Cognition. 30(3), 363–371 (2002)

    Google Scholar 

  19. Kozhevnikov, M., Hegarty, M., Mayer, R.E.: Revising the visualizer-verbalizer dimension: evidence for two types of visualizers. Cognition and Instruction 20(1), 47–77 (2002)

    Article  Google Scholar 

  20. Kozhevnikov, M., Kossyln, S., Shephard, J.: Spatial versus object visualizers: a new characterization of visual cognitive style. Memory and Cognition 33(4), 710–726 (2005)

    Google Scholar 

  21. Kurzenhauser, S., Hoffrage, U.: Teaching Bayesian reasoning: An evaluation of a classroom tutorial for medical students. Medical Teacher 24, 516–521 (2002)

    Article  Google Scholar 

  22. Martignon, L., Wassner, C.: Teaching decision making and statistical thinking with natural frequencies. ICOTS6 ( 2002)

    Google Scholar 

  23. Mirel, B.: Visualization for data exploration and analysis: a critical review of usability research. Technical Communication. Fourth Quarter 1998. pp. 491–509 (1998)

    Google Scholar 

  24. Mayer, R.E.: Multimedia Learning. The Psychology of Learning and Motivation 41, 85–139 (2002)

    Google Scholar 

  25. Plaisant, C.: The challenge of information visualization evaluation. In: AVI 2004, ACM, New York (May 25-28, 2004)

    Google Scholar 

  26. Paivio, A.: Mental Representation: A Dual Coding Approach. Oxford University Press, New York (1990)

    Google Scholar 

  27. Ruff, C., Knauff, M., Fangmeier, T., Spreer, J.: Reasoning and working memory: common and distinct neuronal processes. Neuropsychologia 41, 1241–1253 (2003)

    Article  Google Scholar 

  28. Schnotz, W., Bannert, M.: Construction and interference in learning from multiple representation. Learning and Instruction 13, 141–156 (2003)

    Article  Google Scholar 

  29. Schnotz, W.: An integrated model of text and picture comprehension. In: Mayer, R.E. (ed.) The Cambridge Handbook of Multimedia Learning, Cambridge University Press, Cambridge (2005)

    Google Scholar 

  30. Sedlmeier, P.: How to improve statistical thinking: Choose the task wisely and learn by doing. Instructional Science 28, 227–262 (2000)

    Article  Google Scholar 

  31. Sloman, S.A., Over, D., Slovak, L., Stibel, J.M.: Frequency illusions and other fallacies. Organizational Behavior and Human Decision Processes 91, 296–301 (2003)

    Article  Google Scholar 

  32. Spence, R.: Information Visualization. Addison-Wesley, Reading (2001)

    Google Scholar 

  33. Stanovich, K.E., West, R.F.: Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences 23, 645–726 (2000)

    Article  Google Scholar 

  34. Tegarden, D.P.: Business information visualization. Communications of the AIS. vol. 1(4) (January 1999)

    Google Scholar 

  35. Tufte, E.R.: Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press (1997)

    Google Scholar 

  36. Woodcock, R.W.: New looks in the assessment of cognitive ability. Peabody Journal of Education 77(2), 6–22 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. DePaul University, School of Computer Science, Telecommunications and Information Systems, Chicago, IL, United States of America

    Vince Kellen, Susy Chan & Xiaowen Fang

Authors
  1. Vince Kellen
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  2. Susy Chan
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  3. Xiaowen Fang
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Editor information

Julie A. Jacko

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© 2007 Springer-Verlag Berlin Heidelberg

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Kellen, V., Chan, S., Fang, X. (2007). Facilitating Conditional Probability Problems with Visuals. In: Jacko, J.A. (eds) Human-Computer Interaction. Interaction Platforms and Techniques. HCI 2007. Lecture Notes in Computer Science, vol 4551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73107-8_8

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  • DOI: https://doi.org/10.1007/978-3-540-73107-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73106-1

  • Online ISBN: 978-3-540-73107-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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