Evidence map›Paper›PMID 37426056›Full record

ArticleJournal of cognition2023

A Critique of the Attentional Window Account of Capture Failures.

Nicholas Gaspelin, Howard E Egeth, Steven J Luck

Abstract read
In one paragraph

Article in Journal of cognition, 2023. 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. 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

3 authors.

Nicholas GaspelinDepartment of Psychology, State University of New York at Binghamton, US.ORCID https://orcid.org/0000-0002-1182-0632
Howard E EgethDepartment of Psychological and Brain Sciences, Johns Hopkins University, US.ORCID https://orcid.org/0000-0002-0147-0827
Steven J LuckCenter for Mind and Brain, University of California, Davis, US.ORCID https://orcid.org/0000-0002-3725-1474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been a lengthy debate about whether salient stimuli have the power to automatically capture attention, even when entirely task irrelevant. Theeuwes (2022) has suggested that an attentional window account could explain why capture is observed in some studies, but not others. According to this account, when search is difficult, participants narrow their attentional window, and this prevents the salient distractor from generating a saliency signal. In turn, this causes the salient distractor to fail to capture attention. In the present commentary, we describe two major problems with this account. First, the attentional window account proposes that attention must be focused so narrowly that featural information from the salient distractor will be filtered prior to saliency computations. However, many previous studies observing no capture provided evidence that featural processing was sufficiently detailed to guide attention toward the target shape. This indicates that the attentional window was sufficiently broad to allow featural processing. Second, the attentional window account proposes that capture should occur more readily in easy search tasks than difficult search tasks. We review previous studies that violate this basic prediction of the attentional window account. A more parsimonious account of the data is that control over feature processing can be exerted proactively to prevent capture, at least under certain conditions.

Indexed as

attentionattentional captureattentional window accountsuppression

Identifiers

PMID37426056
PMCPMC10327827

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.