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Optimal eye movement strategies in visual search

Author

Listed:
  • Jiri Najemnik

    (University of Texas at Austin)

  • Wilson S. Geisler

    (University of Texas at Austin)

Abstract

Good looking, so refined Few activities are more important for survival than searching the local area with the eyes to find relevant objects: food, predators, potential mates, oncoming cars. Nonetheless, eye movements recorded during visual search often appear haphazard; it has even been suggested that gaze directions are selected randomly. A study in human subjects tasked to spot a target hidden in a cluttered background now shows that the process is far from random: human eye movements are very near to the mathematically determined optimal strategy. The model developed for this work can also be used to analyse search strategies in other species, and in the refinement of robotic vision systems.

Suggested Citation

  • Jiri Najemnik & Wilson S. Geisler, 2005. "Optimal eye movement strategies in visual search," Nature, Nature, vol. 434(7031), pages 387-391, March.
    • Handle: RePEc:nat:nature:v:434:y:2005:i:7031:d:10.1038_nature03390
    DOI: 10.1038/nature03390
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    Citations

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    Cited by:

    1. Frederick Callaway & Antonio Rangel & Thomas L Griffiths, 2021. "Fixation patterns in simple choice reflect optimal information sampling," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-29, March.
    2. Sang-Hoon Yeo & David W Franklin & Daniel M Wolpert, 2016. "When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-22, December.
    3. Niklas Wilming & Simon Harst & Nico Schmidt & Peter König, 2013. "Saccadic Momentum and Facilitation of Return Saccades Contribute to an Optimal Foraging Strategy," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-13, January.
    4. Joseph Snider & Dongpyo Lee & Howard Poizner & Sergei Gepshtein, 2015. "Prospective Optimization with Limited Resources," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-28, September.
    5. Hang Zhang & Camille Morvan & Louis-Alexandre Etezad-Heydari & Laurence T Maloney, 2012. "Very Slow Search and Reach: Failure to Maximize Expected Gain in an Eye-Hand Coordination Task," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
    6. Constantin Carapencea & Irina Mocanu, 2015. "Real-Time Gaze Tracking With A Single Camera," Romanian Economic Business Review, Romanian-American University, vol. 9(1), pages 37-49, May.
    7. Michel Wedel & Rik Pieters & Ralf Lans, 2023. "Modeling Eye Movements During Decision Making: A Review," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 697-729, June.
    8. Sheng Zhang & Miguel P Eckstein, 2010. "Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-11, September.
    9. Camille Morvan & Laurence T Maloney, 2012. "Human Visual Search Does Not Maximize the Post-Saccadic Probability of Identifying Targets," PLOS Computational Biology, Public Library of Science, vol. 8(2), pages 1-11, February.
    10. Emre Akbas & Miguel P Eckstein, 2017. "Object detection through search with a foveated visual system," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-28, October.

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