Advertisement

Extending Predictive Models of Exploratory Behavior to Broader Populations

  • Shari Trewin
  • John Richards
  • Rachel Bellamy
  • Bonnie E. John
  • Cal Swart
  • David Sloan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6765)

Abstract

We describe the motivation for research aimed at extending predictive cognitive modeling of non-expert users to a broader population. Existing computational cognitive models have successfully predicted the navigation behavior of users exploring unfamiliar interfaces in pursuit of a goal. This paper explores factors that might lead to significant between-group differences in the exploratory behavior of users, with a focus on the roles of working memory, prior knowledge, and information-seeking strategies. Validated models capable of predicting novice goal-directed exploration of computer interfaces can be a valuable design tool. By using data from younger and older user groups to inform the development of such models, we aim to expand their coverage to a broader range of users.

Keywords

Cognitive modeling information foraging usability testing accessibility interface design older users 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Card, S.K., Moran, T.P., Newell, A.: The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, Hillsdale (1983)Google Scholar
  2. 2.
    Gray, W.D., John, B.E., Atwood, M.E.: Project Ernestine: Validating a GOMS Analysis for Predicting and Explaining Real-World Task Performance. Human-Computer Interaction 8(3), 237–309 (1993)CrossRefGoogle Scholar
  3. 3.
    Callander, M., Zorman, L.: Usability on Patrol. In: CHI 2007 Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, USA, April 28 - May 03, pp. 1709–1714. ACM, New York (2007)Google Scholar
  4. 4.
    John, B.E., Kieras, D.E.: Using GOMS for User Interface Design and Evaluation: Which Technique? ACM Transactions on Computer-Human Interaction 3(4), 287–319 (1996)CrossRefGoogle Scholar
  5. 5.
    Luo, L., John, B.E.: Predicting Task Execution Time on Handheld Devices Using the Keystroke-Level Model. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2005), Portland, Oregon, April 2-7. ACM, New York (2005)Google Scholar
  6. 6.
    Knight, A., Pyrzak, G., Green, C.: When Two Methods Are Better Than One: Combining User Study with Cognitive Modeling. In: CHI 2007 Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, USA, April 28 - May 03, pp. 1783–1788. ACM, New York (2007)Google Scholar
  7. 7.
    John, B.E., Prevas, K., Salvucci, D.D., Koedinger, K.: Predictive Human Performance Modeling Made Easy. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2004), pp. 455–462. ACM, New York (2004)Google Scholar
  8. 8.
    Daily, L., Lovett, M., Reder, L.: Modeling Individual Differences in Working Memory Performance: A Source Activation Account. Cognitive Science 25, 315–353 (2001)CrossRefGoogle Scholar
  9. 9.
    Rehling, J., Lovett, M., Lebiere, C., Reder, L., Demiral, B.: Modeling Complex Tasks: An Individual Difference Approach. In: Proceedings of the 26th Annual Conference of the Cognitive Science Society, Chicago, IL, August 4-7, pp. 1137–1142 (2004)Google Scholar
  10. 10.
    Jastrzembski, T.S., Charness, N.: The Model Human Processor and the Older Adult: Validation in a Mobile Phone Task. Journal of Experimental Psychology: Applied 13, 224–248 (2007)Google Scholar
  11. 11.
    Jastrzembski, T.S., Myers, C., Charness, N.: A Principled Account of the Older Adult in ACT-R: Age Specific Model Human Processor Extensions in a Mobile Phone Task. In: Proceedings of the Human Factors and Ergonomics Society 54th Annual Meeting, San Francisco, CA, September 27-October 1 (2010)Google Scholar
  12. 12.
    John, B.E., Jastrzembski, T.S.: Exploration of Costs and Benefits of Predictive Human Performance Modeling for Design. In: Salvucci, D.D., Gunzelmann, G. (eds.) Proceedings of the 10th International Conference on Cognitive Modeling, Philadelphia, PA, pp. 115–120 (2010)Google Scholar
  13. 13.
    Pirolli, P., Card, S.: Information Foraging in Information Access Environments. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1995), pp. 51–58. ACM Press, New York (1995)CrossRefGoogle Scholar
  14. 14.
    Pirolli, P.: Computational Models of Information Scent-Following in a Very Large Browsable Text Collection. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1997), pp. 3–10. ACM Press, New York (1997)CrossRefGoogle Scholar
  15. 15.
    Pirolli, P.: Information Foraging Theory: Adaptive Interaction with Information. Oxford University Press, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Lawrance, J., Bellamy, R., Burnett, M., Rector, K.: Using Information Scent to Model the Dynamic Foraging Behavior of Programmers in Maintenance Tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2008), pp. 1323–1332. ACM Press, New York (2008)Google Scholar
  17. 17.
    Lawrance, J., Burnett, M., Bellamy, R., Bogart, C., Swart, C.: Reactive Information Foraging for Evolving Goals. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2010), pp. 25–34. ACM, New York (2010)Google Scholar
  18. 18.
    Anderson, J.R.: The Adaptive Character Of Thought. Erlbaum, Hillsdale (1990)Google Scholar
  19. 19.
    Blackmon, M., Kitajama, M., Polson, P.: Tool for Accurately Predicting Website Navigation Problems, Non-Problems, Problem Severity, and Effectiveness of Repairs. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2005), pp. 31–40. ACM Press, New York (2005)CrossRefGoogle Scholar
  20. 20.
    Chi, E., Rosien, A., Supattanasiri, G., Williams, A., Royer, C., Chow, C., Robles, E., Dalal, B., Chen, J., Cousins, S.: The Bloodhound Project: Automating Discovery of Web Usability Issues Using the InfoScentTM Simulator. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2003), pp. 505–512. ACM Press, New York (2003)Google Scholar
  21. 21.
    Teo, L., John, B.E.: Towards a Tool for Predicting Goal-Directed Exploratory Behavior. In: Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting, pp. 950–954 (2008)Google Scholar
  22. 22.
    Anderson, J., Lebiere, C.: The Atomic Components of Thought. Erlbaum, USA (1998)Google Scholar
  23. 23.
    Fu, W., Pirolli, P.: SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web. Human-Computer Interaction 22(4), A355–A412 (2007)Google Scholar
  24. 24.
    Halverson, T., Hornoff, A.: A Minimal Model for Predicting Visual Search in Human Computer Interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2007), pp. 431–434. ACM Press, New York (2007)CrossRefGoogle Scholar
  25. 25.
    Turney, P.D.: Mining the Web for Synonyms: PMI-IR Versus LSA on TOEFL. In: Proceedings of the Twelfth European Conference on Machine Learning, Freiburg, Germany, pp. 491–502 (2001)Google Scholar
  26. 26.
    Stone, B., Dennis, S., Kwantes, P.J.: A Systematic Comparison of Semantic Models on Human Similarity Rating Data: The Effectiveness of Subspacing. In: The Proceedings of the Thirtieth Conference of the Cognitive Science Society (2008)Google Scholar
  27. 27.
    Kitajima, M., Blackmon, M.H., Polson, P.G.: Cognitive Architecture for Website Design and Usability Evaluation: Comprehension and Information Scent in Performing by Exploration. In: Proceedings of the HCI International Conference (2005)Google Scholar
  28. 28.
    Mikels, J., Larkin, G., Reuter-Lorenz, P.: Divergent Trajectories in the Aging Mind: Changes in Working Memory for Affective Versus Visual Information with Age. Psychology and Aging, APA 20(4), 542–553 (2005)CrossRefGoogle Scholar
  29. 29.
    Salthouse, T.A.: Differential Age-Related Influences on Memory for Verbal-Symbolic Information and Visual-Spatial Information. Journal of Gerontology 50B, 193–201 (1995)CrossRefGoogle Scholar
  30. 30.
    Park, D.C., Lautenschlager, G., Hedden, T., Davidson, N.S., Smith, A.D., Smith, P.K.: Models of Visuospatial and Verbal Memory Across the Adult Life Span. Psychology and Aging 17, 299–320 (2002)CrossRefGoogle Scholar
  31. 31.
    Brown, S.C., Park, D.C.: Theoretical Models of Cognitive Aging and Implications for Translational Research in Medicine. The Gerontologist 43(suppl. 1), 57–67 (2003)CrossRefGoogle Scholar
  32. 32.
    Dobbs, A.R., Rule, B.G.: Adult Age Differences in Working Memory. Psychology and Aging 4, 500–503 (1989)CrossRefGoogle Scholar
  33. 33.
    Salthouse, T.A., Babcock, R.L.: Decomposing Adult Age Differences in Working Memory. Developmental Psychology 27, 763–776 (1991)CrossRefGoogle Scholar
  34. 34.
    Reuter-Lorenz, P., Sylvester, C.: The Cognitive Neuroscience of Working Memory and Aging. In: Cabeza, R., Nyberg, L., Park, D. (eds.) Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging, pp. 186–217. Oxford University Press, Oxford (2005)Google Scholar
  35. 35.
    Craik, F., McDowd, J.: Age Differences in Recall and Recognition. Journal of Experimental Psychology: Learning, Memory and Cognition 13(3), 57–67 (1987)Google Scholar
  36. 36.
    May, C.P., Hasher, L., Kane, M.J.: The Role of Interference in Memory Span. Memory and Cognition 27, 759–767 (1999)CrossRefGoogle Scholar
  37. 37.
    Lustig, C., May, C.P., Hasher, L.: Working Memory Span and the Role of Proactive Interference. Journal of Experimental Psychology 130, 19–207 (2001)Google Scholar
  38. 38.
    Baddeley, A.D., Hitch, G.J.: Working memory. In: Bower, G.A. (ed.) Recent Advances in Learning and Motivation, pp. 47–89. Academic Press, London (1974)Google Scholar
  39. 39.
    Baddeley, A.: The Psychology of Memory. In: Baddeley, A., Kopelman, M., Wilson, B. (eds.) The Essential Handbook of Memory Disorders for Clinicians,  ch.1. John Wiley & Sons, Chichester (2004)Google Scholar
  40. 40.
    Silver, H., Feldman, P., Bilker, W., Gur, R.C.: Working Memory Deficit as a Core Neuropsychological Dysfunction in Schizophrenia. American Journal of Psychiatry 160, 1809–1816 (2003)CrossRefGoogle Scholar
  41. 41.
    Marusiak, C., Janzen, H.: Assessing the Working Memory Abilities of ADHD Children Using the Stanford-Binet Intelligence Scales. Canadian Journal of School Psychology 20(1-2), 84–97 (2005)CrossRefGoogle Scholar
  42. 42.
    Swanson, H.: Individual Differences in Working Memory: A Model Testing and Subgroup Analysis of Learning-Disabled and Skilled Readers. Intelligence 17(3), 285–332 (1993)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Naumann, J., Richter, T., Christmann, U., Groeben, N.: Working Memory Capacity and Reading Skill Moderate the Effectiveness of Strategy Training in Learning from Hypertext. Learning and Individual Differences 18(2), 197–213 (2008)CrossRefGoogle Scholar
  44. 44.
    Chin, J., Fu, W., Kannampallil, T.: Adaptive Information Search: Age-Dependent Interactions Between Cognitive Profiles and Strategies. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2009), Boston, MA, USA, April 04–09, pp. 1683–1692. ACM, New York (2009)Google Scholar
  45. 45.
    Fairweather, P.: How Younger and Older Adults Differ in Their Approach to Problem Solving on a Complex Website. In: Proceedings of 10th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2008). ACM Press, New York (2008)Google Scholar
  46. 46.
    Mata, R., Wilke, A., Czienskowski, U.: Cognitive Aging and Foraging Behavior. J. Gerontol. B Psychol. Sci. Soc. Sci. 64B(4), 474–481 (2009)CrossRefGoogle Scholar
  47. 47.
    Hanson, V.: Influencing Technology Adoption by Older Adults. Interacting with Computers 22, 502–509 (2010)CrossRefGoogle Scholar
  48. 48.
    Paxton, J., Barch, D., Racine, C., Braver, T.: Cognitive Control, Goal Maintenance, and Prefrontal Function in Healthy Aging. Cerebral Cortex 18(5), 1010–1028 (2008)CrossRefGoogle Scholar
  49. 49.
    Anderson, J., Reder, L., Lebiere, C.: Working Memory: Activation Limitations on Retrieval. Cognitive Psychology 30, 221–256 (1996)CrossRefGoogle Scholar
  50. 50.
    Anderson, J., Bothell, D., Lebiere, C., Matessa, M.: An Integrated Theory of List Memory. Journal of Memory and Language 38, 341–380 (1998)CrossRefGoogle Scholar
  51. 51.
    Huss, D., Byrne, M.: An ACT-R/PM Model of the Articulatory Loop. In: Detje, F., Doerner, D., Schaub, H. (eds.) Proceedings of the Fifth International Conference on Cognitive Modeling, pp. 135–140. Universitats-Verlag Bamberg, Germany (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shari Trewin
    • 1
  • John Richards
    • 1
    • 2
  • Rachel Bellamy
    • 1
  • Bonnie E. John
    • 1
  • Cal Swart
    • 1
  • David Sloan
    • 2
  1. 1.IBM T. J. Watson Research CenterHawthorneUSA
  2. 2.School of ComputingUniversity of DundeeDundeeScotland

Personalised recommendations