Evidence map›Paper›PMID 32320631›Full record

ReviewAnnual review of vision science2020

Visual Search: How Do We Find What We Are Looking For?

Jeremy M Wolfe

Abstract readReview
In one paragraph

Review in Annual review of vision science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.

0numbers the graph read from it
0cells of the map it votes in
99citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

99 citing papers in PubMed.

  1. Article
  2. Review
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  6. Observational
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  11. Effort expectation and strategic cue use in visual search.Cognitive, affective & behavioral neuroscience · 2026
    Article
  12. Article
  13. Article
  14. Statistical learning and the efficiency of visual search.Attention, perception & psychophysics · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

39 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Jeremy M WolfeDepartment of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02115, USA.

Funding

Prevalence effects in visual search: Theoretical and practical implicationsR01EY017001 · NEI · BRIGHAM AND WOMEN'S HOSPITAL · PI Jeremy M Wolfe · 2007 to 2026
$8.2M
Improving Perception in Digital Breast TomographyR01CA207490 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI WOLFE, JEREMY M · 2016 to 2025
$4.6M
NCI NIH HHS R01 CA207490NEI NIH HHS R01 EY017001
6 · The paper itself

Abstract

In visual search tasks, observers look for targets among distractors. In the lab, this often takes the form of multiple searches for a simple shape that may or may not be present among other items scattered at random on a computer screen (e.g., Find a red T among other letters that are either black or red.). In the real world, observers may search for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the subway, can I find lunch, my friend, and a street sign in the scene before me?). This article reviews work on how search is guided intelligently. I ask how serial and parallel processes collaborate in visual search, describe the distinction between search templates in working memory and target templates in long-term memory, and consider how searches are terminated.

Indexed as

Memory, Short-TermColor PerceptionEye MovementsHumansPattern Recognition, Visualforagingparallel processingserial processingvisual attentionvisual searchworking memory

Identifiers

PMID32320631
PMCPMC13537705

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.