Evidence map›Paper›PMID 39198359›Full record

ArticleAttention, perception & psychophysics2024

Statistically learned associations among objects bias attention.

Andrew Clement, Brian A Anderson

Abstract read
PubMed Publisher
In one paragraph

Article in Attention, perception & psychophysics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Andrew ClementDepartment of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA. clemeas@millsaps.edu.
Brian A AndersonDepartment of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing body of research suggests that semantic relationships among objects can influence the control of attention. There is also some evidence that learned associations among objects can bias attention. However, it is unclear whether these findings are due to statistical learning or existing semantic relationships. In the present study, we examined whether statistically learned associations among objects can bias attention in the absence of existing semantic relationships. Participants searched for one of four targets among pairs of novel shapes and identified whether the target was present or absent from the display. In an initial training phase, each target was paired with an associated distractor in a fixed spatial configuration. In a subsequent test phase, each target could be paired with the previously associated distractor or a different distractor. In our first experiment, the previously associated distractor was always presented in the same pair as the target. Participants were faster to respond when this distractor was present on target-present trials. In our second experiment, the previously associated distractor was presented in a different pair than the target in the test phase. In this case, participants were slower to respond when this distractor was present on both target-present and target-absent trials. Together, these findings provide clear evidence that statistically learned associations among objects can bias attention, analogous to the effects of semantic relationships on attention.

Indexed as

Association LearningPattern Recognition, VisualSemanticsAdultAttentionAttentional BiasFemaleHumansMaleOrientationProbability LearningReaction TimeYoung AdultAttentionSelection historySemantic relationshipsStatistical learningVisual search

Identifiers

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.