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Cited by 875Visual Attention and Saliency DetectionVisual perception and processing mechanismsNeural and Behavioral Psychology Studies

Five factors that guide attention in visual search

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Wolfe, Jeremy M., Horowitz, Todd S.·2017·Nature Human Behaviour
DOI 10.1038/s41562-017-0058PMC PMC9879335
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Abstract

How do we find what we are looking for? Fundamental limits on visual processing mean that even when the desired target is in our field of view, we often need to search, because it is impossible to recognize everything at once. Searching involves directing attention to objects that might be the target. This deployment of attention is not random. It is guided to the most promising items and locations by five factors discussed here: Bottom-up salience, top-down feature guidance, scene structure and meaning, the previous history of search over time scales from msec to years, and the relative value of the targets and distractors. Modern theories of search need to specify how all five factors combine to shape search behavior. An understanding of the rules of guidance can be used to improve the accuracy and efficiency of socially-important search tasks, from security screening to medical image perception.

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  • Depicting landmarks as realistic 3D building models on a 2D basemap serves as bottom-up perceptual guidance by stimulus salience, guiding users' attention to these features.

    “As attention is attracted by items that differ from their surroundings (Wolfe & Horowitz, 2017), depicting landmarks as realistic 3D building models on a 2D basemap serves as a bottom-up perceptual guidance by stimulus salience, guiding users’ attention to these features (Wolfe & Horowitz, 2017).”

    From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Wolfe & Horowitz, 2017

    The “Bottom-up guidance by stimulus salience” section says attention can be attracted to items that differ sufficiently from their surroundings along attributes that guide attention. It does not address 3D building models on 2D basemaps or establish that this particular depiction draws users’ attention to landmarks. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Previous work shows that visually salient landmarks attract and modulate viewers' bottom-up or stimulus-driven visual attention, but in different ways for individuals with different backgrounds, training, and abilities.

    “This finding aligns with previous work in that visually salient landmarks do attract and modulate viewers’ bottom-up or stimulus-driven visual attention, but clearly in different ways for individuals with different backgrounds, training, abilities, etc (Richter & Winter, 2014; Wenczel et al., 2017; Wolfe & Horowitz, 2017; Yesiltepe et al., 2021).”

    From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Wolfe & Horowitz, 2017

    The “Bottom-up guidance by stimulus salience” section (page unavailable) says that attention is attracted to sufficiently distinctive items. “Modulation of search by prior history” (page unavailable) says an observer’s history and familiarity can affect visual search. The paper does not specifically address visually salient landmarks or show that their bottom-up effects differ by viewers’ backgrounds, training, or abilities. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Authors
Wolfe, J. M., & Horowitz, T. S.
Affiliations
  • Brigham and Women's Hospital (US)
  • National Cancer Institute (US)
Corresponding author
Wolfe, Jeremy M.
ORCID
Venue
Nature Human Behaviour
Publisher
Springer Science and Business Media LLC
Published
2017-03-08
Volume
1
Issue
3
ISSN
2397-3374 (ISSN-L)
DOI
10.1038/s41562-017-0058
PMC
PMC9879335
OpenAlex
W2591673470
Cited by
875
References
3
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en
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Licence
Locations
  • Nature Human Behaviour (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • National Eye Institute (R01EY017001)
  • National Cancer Institute (R01CA207490)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
visual search, attention guidance, bottom-up salience, scene structure, search history, visual attention, search efficiency

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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