Evidence map›Paper›PMID 34609475›Full record

ArticleJournal of vision2021

Meaning and expected surfaces combine to guide attention during visual search in scenes.

Candace E Peacock, Deborah A Cronin, Taylor R Hayes, John M Henderson

Abstract read
In one paragraph

Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Naturalistic reinforcement learning.Trends in cognitive sciences · 2024
    Review
  4. Article
  5. Article
  6. Article
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

4 authors.

Candace E PeacockCenter for Mind and Brain, University of California, Davis, Davis, CA, USA.
Deborah A CroninCenter for Mind and Brain, University of California, Davis, Davis, CA, USA.
Taylor R HayesCenter for Mind and Brain, University of California, Davis, Davis, CA, USA.
John M HendersonCenter for Mind and Brain, University of California, Davis, Davis, CA, USA.

Funding

Vision Science Training Program: Cornea to CortexT32EY015387 · NEI · UNIVERSITY OF CALIFORNIA DAVIS · PI MARIE E BURNS · 2003 to 2026
$4.8M
Guiding Attention in Real-World ScenesR01EY027792 · NEI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI John M Henderson · 2017 to 2026
$2.5M
NEI NIH HHS R01 EY027792NEI NIH HHS T32 EY015387
6 · The paper itself

Abstract

How do spatial constraints and meaningful scene regions interact to control overt attention during visual search for objects in real-world scenes? To answer this question, we combined novel surface maps of the likely locations of target objects with maps of the spatial distribution of scene semantic content. The surface maps captured likely target surfaces as continuous probabilities. Meaning was represented by meaning maps highlighting the distribution of semantic content in local scene regions. Attention was indexed by eye movements during the search for target objects that varied in the likelihood they would appear on specific surfaces. The interaction between surface maps and meaning maps was analyzed to test whether fixations were directed to meaningful scene regions on target-related surfaces. Overall, meaningful scene regions were more likely to be fixated if they appeared on target-related surfaces than if they appeared on target-unrelated surfaces. These findings suggest that the visual system prioritizes meaningful scene regions on target-related surfaces during visual search in scenes.

Indexed as

AttentionVisual PerceptionEye MovementsHumansPattern Recognition, VisualProbabilitySemantics

Identifiers

PMID34609475
PMCPMC8496418

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.