David E. Kieras
ABSTRACT
Being able to predict the performance of interface designs using models of human cognition and performance is a long-standing goal of HCI research. This paper presents recent advances in cognitive modeling which permit increasingly realistic and accurate predictions for visual human-computer interaction tasks such as icon search by incorporating an "active vision" approach which emphasizes eye movements to visual features based on the availability of features in relationship to the point of gaze. A high fidelity model of a classic visual search task demonstrates the value of incorporating visual acuity functions into models of visual performance. The features captured by the high-fidelity model are then used to formulate a model simple enough for practical use, which is then implemented in an easy-to-use GLEAN modeling tool. Easy-to-use predictive models for complex visual search are thus feasible and should be further developed.
- Anstis, S. M. (1974). A chart demonstrating variations in acuity with retinal position. Vision Research, 14(7), 589--592.Google Scholar
Cross Ref
- Bellamy, R., John, B., & Kogan, S. (2011). Deploying CogTool: Integrating quantitative usability assessment into real-world software development. In Proceedings of the 2011 International Conference on Software Engineering, 691--700. Google ScholarDigital Library
- Callander, M., & Zorman, L. (2007). Usability on patrol. In CHI '07 Extended Abstracts, 1709--1714. Google ScholarDigital Library
- Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum Associates. Google ScholarDigital Library
- Cockburn, A., & Gutwin, C. (2009). A predictive model of human performance with scrolling and hierarchical lists. Human-Computer Interaction, 24(3), 273--314.Google ScholarCross Ref
- Findlay, J. M., & Gilchrist, I. D. (2003). Active Vision: The Psychology of Looking and Seeing. Oxford University Press.Google Scholar
- Fleetwood, M. D., & Byrne, M. D. (2006). Modeling the visual search of displays: A revised ACT-R/PM model of icon search based on eye tracking data. Human-Computer Interaction, 21(2), 153--197. Google ScholarDigital Library
- Gordon, J., & Abramov, I. (1977). Color vision in the peripheral retina. II. Hue and saturation. J. Opt. Soc. Am., 67(2), 202--207.Google ScholarCross Ref
- Halverson, T., & Hornof, A. J. (2011). A computational model of "active vision" for visual search in humancomputer interaction. Human-Computer Interaction, 26(4), 285'314. Google ScholarDigital Library
- Henderson, J. M., & Castelhano, M. S. (2005). Eye movements and visual memory for scenes. In G. Underwood (Ed.), Cognitive Processes in Eye Guidance. (pp. 213--35). Oxford University Press.Google Scholar
- Hornof, A. J., & Kieras, D. E. (1997). Cognitive modeling reveals menu search is both random and systematic. In Proceedings of CHI '97, 107--114. Google ScholarDigital Library
- John, B., Starr, S., & Utesch, B. (2012). Experiences with collaborative, distributed predictive human performance modeling. In CHI '12 Ext. Abs., 437--452. Google ScholarDigital Library
- John, B. E., & Kieras, D. E. (1996). Using GOMS for user interface design and evaluation: Which technique' ACM Transactions on Computer-Human Interaction, 3(4), 287--319. Google ScholarDigital Library
- John, B. E., Prevas, K., Salvucci, D. D., & Koedinger, K. (2004). Predictive human performance modeling made easy. In Proceedings of CHI 2004, 455--462. Google ScholarDigital Library
- Kieras, D. (2009). The persistent visual store as the locus of 'xation memory in visual search tasks. In R. Cooper & D. Peebles (Eds.), Proceedings of the Ninth International Conference on Cognitive Modeling, UK.Google Scholar
- Kieras, D. (2010). Modeling visual search of displays of many objects: The role of differential acuity and 'xation memory. In D. D. Salvucci & G. Gunzelmann (Eds.) Proceedings of the 10th International Conference on Cognitive Modeling, August 6--8, 2010, Philadelphia.Google Scholar
- Kieras, D. E. (2007). Model-based evaluation. In J. A. Jacko & A. Sears (Eds.), The Human-Computer Interaction Handbook (2nd ed.). (pp. 1191--208). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
- Kieras, D. E., & Marshall, S. P. (2006). Visual availability and 'xation memory in modeling visual search using the EPIC architecture. In Proceedings of the Annual Meeting of the Cognitive Science Society, 423--428.Google Scholar
- Kieras, D. E. (2004). The EPIC Architecture: Principles of Operation. Retrieved from http:// www.eecs.umich.edu/~kieras/docs/EPIC/ EPICPrinOp.pdfGoogle Scholar
- Kieras, D. E., & Meyer, D. E. (1997). An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction, 12(4), 391--438. Google ScholarDigital Library
- Kieras, D. E., Wood, S. D., Abotel, K., & Hornof, A. (1995). GLEAN: A computer-based tool for rapid GOMS model usability evaluation of user interface designs. In Proceedings of UIST '95, ACM, 91--100. Google ScholarDigital Library
- Maeda, J. (2013, June 12). The future of design is more than making Apple iOS 'at. Wired Magazine. Retrieved from http://www.wired.com/opinion/2013/06/thefuture-of-design-is-more-than-making-apple-ios--at/Google Scholar
- Osga, G., Van Orden K., Campbell, N., Kellmeyer, D., and Lulue D. (2002). Design and Evaluation of War'ghter Task Support Methods in a Multi-Modal Watchstation. Space & Naval Warfare Center, San Diego, Technical Report 1874.Google Scholar
- Peterson, M. S., Kramer, A. F., Wang, R. F., Irwin, D. E., & McCarley, J. S. (2001). Visual search has memory. Psychological Science, 12(4), 287--292.Google ScholarCross Ref
- Salvucci, D. D., Zuber, M., Beregovaia, E., & Markley, D. (2005). Distract-R: Rapid prototyping and evaluation of in-vehicle interfaces. In Proc. of CHI '05, 581--589. Google ScholarDigital Library
- Sanders, M. S., & McCormick, E. J. (1987). Human Factors in Engineering and Design (6th ed.). New York, New York: McGraw-Hill.Google Scholar
- Teo, John, & Blackmon. (2012). CogTool-Explorer: A model of goal-directed user exploration that considers information layout. In Proc. of CHI '12, 2479--2488. Google ScholarDigital Library
- Vera, A. H., John, B. E., Remington, R., Matessa, M., & Freed, M. A. (2005). Automating human-performance modeling at the millisecond level. Human-Computer Interaction, 20, 225'265. Google ScholarDigital Library
- Williams, L. G. (1966). A Study Of Visual Search Using Eye Movement Recordings. Technical Report, Honeywell Inc., St. Paul Minnesota, Feb. 28, 1966. NTIS AD0629624.Google Scholar
- Williams, L. G. (1967). The effects of target Specification on objects 'xated during visual search. Acta Psychologica, 27, 355--360.Google ScholarCross Ref
Index Terms
- Towards accurate and practical predictive models of active-vision-based visual search
Recommendations
Visual Grouping in Menu Interfaces
CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing SystemsMenu interfaces often arrange options into semantic groups. This semantic structure is then usually conveyed to the user by supplementary visual grouping cues. We investigate whether these visual grouping cues actually help users locate items in menus ...
Individualising Graphical Layouts with Predictive Visual Search Models
Special Issue on IUI 2018In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual ...
Subtle cueing for visual search in augmented reality
ISMAR '12: Proceedings of the 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)Visual search in augmented reality environments is an important task that can be facilitated through different cueing methods. Current cueing methods rely on explicit cueing, which can potentially reduce visual search performance. In comparison, this ...
Comments