Tell me more? the effects of mental model soundness on personalizing an intelligent agent

Conference on Human Factors in Computing Systems - Proceedings

Volumn , Issue , 2012, Pages 1-10

  Kulesza, Todd ^a   Stumpf, Simone ^b   Burnett, Margaret ^a   Kwan, Irwin ^a  

^a Oregon State University   (United States)

^b CITY UNIVERSITY   (United Kingdom)

Author keywords

Debugging; Intelligent agents; Mental models; Music; Personalization; Recommenders

Indexed keywords

EMPIRICAL STUDIES; END USERS; MENTAL MODEL; MUSIC; MUSIC RECOMMENDER SYSTEMS; PERSONALIZATIONS; RECOMMENDERS; STRUCTURAL KNOWLEDGE; USER NEED;

COMPUTER DEBUGGING; HUMAN COMPUTER INTERACTION; HUMAN ENGINEERING; RECOMMENDER SYSTEMS;

INTELLIGENT AGENTS;

EID: 84862110831     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2207676.2207678     Document Type: Conference Paper

References (29)

1. Amershi, S., Fogarty, J., Kapoor, A., and Tan, D. Examining multiple potential models in end-user interactive concept learning. In Proc. CHI, ACM (2010), 1357-1360.
2. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 8, 2 (1977).
3. Benedek, J. and Miner, T. Measuring desirability: New methods for evaluating desirability in a usability lab setting. In Proc. Usability Professionals' Association International Conference (2002).
4. Bozionelos, N. The relationship of instrumental and expressive traits with computer anxiety. Personality and Individual Differences 31 (2001), 955-974.
5. Compeau, D. and Higgins, C. Application of social cognitive theory to training for computer skills. Information Systems Research, 6,2 (1995), 118-143.
6. Echo Nest, The. http://the.echonest.com (July, 2011).
7. Fiebrink, R., Cook, P., and Trueman, D. Human model evaluation in interactive supervised learning. In Proc. CHI, ACM (2011), 147-156.
8. Hart, S. and Staveland, L. Development of a NASATLX (Task load index): Results of empirical and theoretical research, Hancock, P. and Meshkati, N. (Eds.), Human Mental Workload (1988), 139-183.
9. Herlocker, J., Konstan, J., Riedl, J. Explaining collaborative filtering recommendations. In Proc. CSCW, ACM (2000), 241-250.
10. Johnson-Laird, P.N. Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge University Press (1983).
11. Kapoor, A., Lee, B., Tan, D., and Horvitz, E. Interactive optimization for steering machine classification. In Proc. CHI, ACM (2010), 1343-1352.
12. Kolb, D. A. Experiential Learning. Prentice-Hall Englewood Cliffs, NJ (1984).
13. Kulesza, T., Wong, W.-K., Stumpf, S., Perona, S., White, R., Burnett, M., Oberst, I., and Ko, A. J. Fixing the program my computer learned: barriers for end users, barriers for the machine. In Proc. IUI, ACM (2009), 187-196.
14. Kulesza, T., Stumpf, S., Burnett, M., Wong, W., Riche, Y., Moore, T., Oberst, I., Shinsel, A., McIntosh, K. Explanatory debugging: Supporting end-user debugging of machine-learned programs. In Proc. VL/HCC, IEEE (2010), 41-48.
15. Lim, B. Y., Dey, A. K., and Avrahami, D. Why and why not explanations improve the intelligibility of contextaware intelligent systems. In Proc. CHI, ACM (2009), 2119-2128.
16. Lim, B. Y. and Dey, A. K. Toolkit to support intelligibility in context-aware applications. In Proc. UbiComp, ACM (2010), 13-22.
17. McNee, S. M., Lam, S. K., Guetzlaff, C., Konstan, J. A., and Riedl, J. Confidence displays and training in recommender systems. In Proc. INTERACT, IFIP (2003), 176-183.
18. Norman, D. Some observations on mental models, Gentner, D. and Stevens, A. (Eds.), Mental Models (1983), 7-14.
19. Pandora Media, Inc. Initial Public Offering Form S-1 (2011).
20. Rosson, M. B., Carroll, J. M., and Bellamy, R. K. E. Smalltalk scaffolding: a case study of minimalist instruction. In Proc. CHI, ACM (1990), 423-430.
21. Sharp, H., Rogers, Y., and Preece, J. Interaction Design: Beyond Human-Computer Interaction (3rd edition), John Wiley (2011).
22. Sinha, R. R. and Swearingen, K. The role of transparency in recommender systems. In Proc. CHI Extended Abstracts, ACM (2002), 830-831.
23. Stone, P., Dunphy, D., Smith, M., Ogilvie, D., and associates. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press (1966).
24. Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., and Herlocker, J. Toward harnessing user feedback for machine learning. In Proc. IUI, ACM (2007), 82-91.
25. Stumpf, S., Rajaram, V., Li, L., Burnett, M., Wong, W.- K., Dietterich, T., Sullivan, E., Drummond, R., and Herlocker, J. Interacting meaningfully with machine learning systems: Three experiments. International Journal of Human-Computer Studies 67, 8 (2009).
26. Talbot, J., Lee, B., Tan, D., and Kapoor, A. EnsembleMatrix: Interactive visualization to support machine learning with multiple classifiers. In Proc. CHI, ACM (2009), 1283-1292.
27. Tintarev, N., and Masthoff, J. Effective explanations of recommendations: User-centered design. In Proc. Recommender Systems (2007), 153-156.
28. Tullio, J., Dey, A.K., Chalecki, J., and Fogarty, J. How it works: A field study of non-technical users interacting with an intelligent system. In Proc. CHI, ACM (2007).
29. Wilfong, J. Computer anxiety and anger: The impact of computer use, computer experience, and self-efficacy beliefs. Computers in Human Behavior 22 (2006).