IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v434y2005i7031d10.1038_nature03390.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature03390
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature03390?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:434:y:2005:i:7031:d:10.1038_nature03390. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.