JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Prediction of Human Performance Time to Find Objects on Multi-display Monitors using ACT-R Cognitive Architecture
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Prediction of Human Performance Time to Find Objects on Multi-display Monitors using ACT-R Cognitive Architecture
Oh, Hyungseok; Myung, Rohae; Kim, Sang-Hyeob; Jang, Eun-Hye; Park, Byoung-Jun;
  PDF(new window)
 Abstract
Objective: The aim of this study was to predict human performance time in finding objects on multi-display monitors using ACT-R cognitive architecture. Background: Display monitors are one of the representative interfaces for interaction between people and the system. Nowadays, the use of multi-display monitors is increasing so that it is necessary to research about the interaction between users and the system on multi-display monitors. Method: A cognitive model using ACT-R cognitive architecture was developed for the model-based evaluation on multi-display monitors. To develop the cognitive model, first, an experiment was performed to extract the latency about the where system of ACT-R. Then, a menu selection experiment was performed to develop a human performance model to find objects on multi-display monitors. The validation of the cognitive model was also carried out between the developed ACT-R model and empirical data. Results: As a result, no significant difference on performance time was found between the model and empirical data. Conclusion: The ACT-R cognitive architecture could be extended to model human behavior in the search of objects on multi-display monitors.. Application: This model can help predicting performance time for the model-based usability evaluation in the area of multi-display work environments.
 Keywords
ACT-R;Cognitive architecture;Model-based evaluation;Multi-display monitors;
 Language
Korean
 Cited by
 References
1.
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C. and Qin, Y., An Integrated Theory of Mind, Psychological Review, 111, 1036-1060, 2004. crossref(new window)

2.
Anderson, J.R. and Matessa, M., The rational analysis of categorization and the ACT-R architecture. In M. Oaksford & N. Chater (Eds.) Rational models of cognition, Oxford: Oxford University Press, 197-217, 1998.

3.
Anderson, J.R., Matessa, M. and Lebiere, C., ACT-R: A theory of higher level cognition and its relation to visual attention. Human Computer Interaction, 12(4), 439-462, 1997. crossref(new window)

4.
Byrne, M.D., ACT-R/PM and Menu Selection: Applying a Cognitive Architecture to HCI, International Journal of Human-Computer Studies, Volume 55, Issue 1, 41-84, 2001. crossref(new window)

5.
Byrne, M.D. and Anderson, J.R., Enhancing ACT-R's Perceptual-Motor Abilities. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates. 1997.

6.
Byrne, M.D. and Anderson, J.R., Perception and action, In J. Anderson and J. Lebiere (Ed), The Atomic Components of Thought, Mahwah, NJ: Lawrence Erlbaum, 167-200, 1998.

7.
Campbell, G.E. and Bolton, A.E., HBR Validation: Integrating Lessons Learned From Multiple Academic Disciplines, Applied Communities, and the AMBR Project. In K. Gluck and R. Pew (Ed), Modeling Human Behavior with Integrated Cognitive Architectures, Hillsdale, NJ: Lawrence Erlbaum Associates, 2005.

8.
Card, S.K., Moran, T.P. and Newell, A., The Psychology of Human Computer Interaction, L. Erlbaum Associates, 1983.

9.
Chung, Y.K., Case Analysis of Multi Display and A Study on Management Interface. Journal of Korea Design Knowledge. 18, 180-189, 2011.

10.
Ehret, B.D., Learning where to look: The acquisition of location knowledge in display-based interaction. Dissertation Abstracts International: Section B: the Sciences & Engineering, 60(10-B), 5239, 2000.

11.
Fleetwood, M.D. and Byrne, M.D., Modeling icon search in ACT-R/PM. Cognitive Systems Research, 3, 25-33, 2002. crossref(new window)

12.
Goodale, M.A. and Milner, A.D., Separate visual Pathways for Perception and Action, Trends in Neurosciences, 15(1), 20-25, 1992. crossref(new window)

13.
http://act-r.psy.cmu.edu

14.
John, B.E. and Kieras, D.E., Using GOMS for User Interface Design and Evaluation: Which Technique?, ACM Transactions on Computer- Human Interaction, 3(4), 287-319, 1996a. crossref(new window)

15.
John, B.E. and Kieras, D.E., The GOMS Family of User Interface Analysis Techniques: Comparison and Contrast, ACM Transactions on Computer-Human Interaction, 3(4), 320-350, 1996b. crossref(new window)

16.
Kieras, D.E. and Meyer, D.E., An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction 12, pp. 391-438, 1997. crossref(new window)

17.
Laird, J.E., Newell, A. and Rosenbloom, P.S., SOAR: An architecture for general intelligence, Artificial Intelligence, 33, pp. 1-64, 1987. crossref(new window)

18.
Lim, S., Jo, S., Myung, R., Kim, S., Jang, E. and Park, B., ACT-R Predictive Model of Korean text Entry on Touchscreen, Journal of the Ergonomics Society of Korea, 31(2), 291-298, 2012. crossref(new window)

19.
Min, J., Jo, S. and Myung, R., Prediction of Menu Selection on Touchscreen using a Cognitive Architecture: ACT-R, Journal of the Ergonomics Society of Korea, 29(6), 907-914, 2010. crossref(new window)

20.
Salvucci, D.D., Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362-380, 2006. crossref(new window)

21.
Sanders, M.S. and McCormick, E.J., Human factors in Engineering and design, 7th Edition. McGraw-Hill Book Company, NY. 1993.

22.
St. Amant, R., Horton, T.E. and Ritter, F.E., Model-Based Evaluation of Expert Cell Phone Menu Interaction. ACM Transactions on Computer- Human Interaction, Vol. 14, No. 1, Article 1, 2007.

23.
U.S. Department of Defense, Human Factors Engineering Design for Army Material MIL-HDBK 759A, Washington, DC. 1981.