Evidence map›Paper›PMID 36536205›Full record

ArticleAttention, perception & psychophysics2023

Combined influence of valence and statistical learning on the control of attention II: Evidence from within-domain additivity.

Alex Ogden, Haena Kim, Brian A Anderson

Abstract read
In one paragraph

Article in Attention, perception & psychophysics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Visual statistical learning requires attention.Psychonomic bulletin & review · 2025
    Article
  5. 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.

Alex OgdenDepartment of Psychological & Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843-4235, USA.
Haena KimDepartment of Psychological & Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843-4235, USA.
Brian A AndersonDepartment of Psychological & Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843-4235, USA. brian.anderson@tamu.edu.

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

Attention is biased in favor of stimuli that signal either threat or reward; this experience-dependent attentional bias develops via associative learning and persists into extinction. Physically salient yet task-irrelevant stimuli are also prioritized by the attention system, but the attentional priority of a physically salient distractor can be suppressed when it appears in a location in which it has been frequently encountered in the past. Similar effects of statistical learning on distractor suppression have been observed for distractors appearing in a predictable color. A pair of recent studies demonstrate that statistically learned distractor suppression and valence-based attentional biases combine additively, suggesting independent influences of learning on attentional priority. One limitation of these prior studies, however, is that the effects of statistical learning were defined with respect to spatial attention and the effects of associative learning with respect to feature-based attention. A strong version of the independence account would predict additive influences on attention even when both sources of priority are represented within a single domain of attentional control, which we tested in the present study. The attentional priority of a distractor was elevated when its color was previously associated with electric shock and reduced when its shape was frequently encountered as a distractor in a prior training phase, with these two influences on priority combining additively. Our findings provide strong evidence for the idea that statistical learning and valance-based associative learning exert independent influences on the control of attention, which has implications for contemporary theories of selection history.

Indexed as

AttentionAttentional BiasConditioning, ClassicalHumansLearningReaction TimeAttentional captureSelection historySignal suppressionStatistical learning

Identifiers

PMID36536205
PMCPMC10319402

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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.