Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-28T21:10:46.007Z Has data issue: false hasContentIssue false
  • English
  • Français

Cognitive architecture enables comprehensive predictive models of visual search

Published online by Cambridge University Press:  24 May 2017

David E. Kieras  and
Anthony Hornof
David E. Kieras
Affiliation:
EECS Department, University of Michigan, Ann Arbor, MI 48109-2121; [email protected]://web.eecs.umich.edu/~kieras/
Anthony Hornof
Affiliation:
Department of Computer and Information Science, University of Oregon, Eugene, OR 97403-1202. [email protected]://ix.cs.uoregon.edu/~hornof/
Get access Rights & Permissions [Opens in a new window]

Abstract

With a simple demonstration model, Hulleman & Olivers (H&O) effectively argue that theories of visual search need an overhaul. We point to related literature in which visual search is modeled in even more detail through the use of computational cognitive architectures that incorporate fundamental perceptual, cognitive, and motor mechanisms; the result of such work thus far bolsters their arguments considerably.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e142
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abrams, R. A., Meyer, D. E. & Kornblum, S. (1989) Speed and accuracy of saccadic eye movements: Characteristics of impulse variability in the oculomotor system. Journal of Experimental Psychology: Human Perception and Performance 15(3):529–43.Google ScholarPubMed
Anderson, J. R. & Lebiere, C. (1998) The atomic components of thought. Erlbaum.Google Scholar
Anstis, S. M. (1974) A chart demonstrating variations in acuity with retinal position. Vision Research 14(7):589–92.Google Scholar
Bouma, H. (1970) Interaction effects in parafoveal letter recognition. Nature 226:177–78.CrossRefGoogle ScholarPubMed
Engel, F. L. (1977) Visual conspicuity, visual search and fixation tendencies of the eye. Vision Research 17:95–108. doi: 10.1016/0042-6989(77)90207-3.Google Scholar
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):153–97.CrossRefGoogle Scholar
Gordon, J. & Abramov, I. (1977) Color vision in the peripheral retina. II. Hue and saturation. Journal of the Optical Society of America 67(2):202–207.Google Scholar
Halverson, T. & Hornof, A. J. (2011) A computational model of “active vision” for visual search in human-computer interaction. Human-Computer Interaction 26(4):285–314.Google Scholar
Harris, C. M. (1995) Does saccadic undershoot minimize saccadic flight-time? A Monte-Carlo study. Vision Research 35:691–701.Google Scholar
Henderson, J. M. & Castelhano, M. S. (2005) Eye movements and visual memory for scenes. In: Cognitive processes in eye guidance, ed. Underwood, G., pp. 213–35. Oxford University Press.Google Scholar
Hornof, A. J. (2004) Cognitive strategies for the visual search of hierarchical computer displays. Human-Computer Interaction 19(3):183–223.CrossRefGoogle Scholar
Kieras, D. (2011) The persistent visual store as the locus of fixation memory in visual search tasks. Cognitive Systems Research 12:102–12.CrossRefGoogle Scholar
Kieras, D. E. & Hornof, A. J. (2014) Towards accurate and practical predictive models of active-vision-based visual search. In: CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3875–84. ACM.Google 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 Scholar
Kieras, D. E. & Meyer, D. E. (2000) The role of cognitive task analysis in the application of predictive models of human performance. In: Cognitive task analysis, ed. Schraagen, J. M. C., Chipman, S. E. & Shalin, V. L., pp. 237–60. Erlbaum.Google Scholar
Meyer, D. E. & Kieras, D. E. (1997) A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms. Psychological Review 104(1):3–65. doi: 10.1037/0033-295X.104.1.3.Google Scholar
Meyer, D. E. & Kieras, D. E. (1999) Precis to a practical unified theory of cognition and action: Some lessons from computational modeling of human multiple-task performance. In: Attention and performance XVII. Cognitive regulation of performance: Integration of theory and application, ed. Gopher, D. & Koriat, A., pp. 17–88. MIT Press.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):287–92.Google Scholar
Schumacher, E. H., Seymour, T. L., Glass, J. M., Fencsik, D., Lauber, E. J., Kieras, D. E. & Meyer, D. E. (2001) Virtually perfect time-sharing in dual-task performance: Uncorking the central cognitive bottleneck. Psychological Science 12:101–108.Google Scholar
Thompson, E. R., Iyer, N., Simpson, B. D., Wakefield, G. H., Kieras, D. E. & Brungart, D. S. (2015) Enhancing listener strategies using a payoff matrix in speech-on-speech masking experiments. Journal of the Acoustical Society of America 138(3):1297–304.Google Scholar
Virsu, V. & Rovamo, J. (1979) Visual resolution, contrast sensitivity, and the cortical magnification factor. Experimental Brain Research 37:475–94.Google Scholar
Zhang, Y. & Hornof, A. J. (2014) Understanding multitasking through parallelized strategy exploration and individualized cognitive modeling. In: CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3885–94. ACM.CrossRefGoogle Scholar