Evidence map›Paper›PMID 37038029›Full record

ArticlePsychonomic bulletin & review2023

Selection history contributes to suboptimal attention strategies.

David S Lee, Brian A Anderson

Abstract read
In one paragraph

Article in Psychonomic bulletin & review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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  3. 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

2 authors.

David S LeeDepartment of Psychological and Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843-4235, USA. lee73769598@tamu.edu.ORCID http://orcid.org/0000-0002-9384-5293
Brian A AndersonDepartment of Psychological and Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843-4235, USA.

Funding

The Control of Attention and Learning from Physical EffortR01DA046410 · NIDA · TEXAS A&M UNIVERSITY · PI ANDERSON, BRIAN AUGUST · 2019 to 2023
$1.9M
NIDA NIH HHS R01 DA046410
6 · The paper itself

Abstract

Attentional control balances the competing drives of performance maximization and effort minimization. One way the attention system minimizes effort is through a bias to persist in the use of attentional control strategies that have been useful in the past. In the present study, we asked whether such selection history can result in the persistence of an attentional control strategy that is counterproductive, effectively competing with a more optimal strategy. Participants first completed a training in which one color target was encountered more frequently than another, and then completed a test phase in which they could search for one of two targets on any given trial, one of which would be more optimal to search for given the distribution of color stimuli. An attentional bias for the more frequent target color was observed in the training phase and the choice of which target to report was robustly optimal in the test phase, reflecting performance maximization. Importantly, participants also exhibited a tendency to report the target rendered in the previously more frequent target color in the test phase, even when the distribution of non-target colors made it suboptimal to do so. Our findings shed light on the fundamental question of why attentional control is sometimes suboptimal, demonstrating a role for selection history in the perseveration of previously employed attentional strategies even when such strategies produce suboptimal performance.

Indexed as

AttentionAttentional BiasColor PerceptionHumansReaction TimeSelection historySelective attentionStrategyVisual search

Identifiers

PMID37038029
PMCPMC10562514

What OpenQuestion holds

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Registered trials

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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.