Evidence map›Paper›PMID 40379870›Full record

ArticleScientific reports2025

Age-invariant benefits of spatiotemporal predictions amidst distraction during dynamic visual search.

Nir Shalev, Sage Boettcher, Anna C Nobre

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nir Shalev *Department of Gerontology, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, IL, Israel. nir.shalev@psy.ox.ac.uk.
Sage Boettcher *Department of Experimental Psychology, University of Oxford, Oxford, UK.
Anna C NobreDepartment of Experimental Psychology, University of Oxford, Oxford, UK.

Funding

Israel Science Foundation 1073/24James S. McDonnell Foundation 220020448Wellcome Trust 104571/Z/14/Z
6 · The paper itself

Abstract

Visual search tasks are widely used to study attention amidst distraction, often revealing age-related differences. Research shows older adults typically exhibit poorer performance and greater sensitivity to distraction, reflecting declines in goal-driven attention. However, traditional search tasks are static and fail to capture the challenges and opportunities in natural environments, which include predictive structures within extended contexts. We designed a search variation where targets and distractors compete over time and embedded spatiotemporal regularities afford prediction-led guidance of attention. Critically, we manipulated the number of distractors to chart how benefits of expectations and deficits from distraction varied with age. Younger and older adults searched for multiple targets as they faded in and out of the display while varying the number of distracting elements between trials. Half the targets appeared at the same time and approximate locations and could be predicted. While we found evidence for decrement and elevated sensitivity to distraction with increasing age, benefits from predictions occurred in all groups. Interestingly, regardless of age, effects of predictions were only significant during periods of high distraction. This work extends our understanding of attention control through ageing to dynamic settings and indicates a dissociation between goal-directed and learning-driven attentional guidance.

Indexed as

AgingAttentionVisual PerceptionAdolescentAdultAgedAge FactorsFemaleHumansMaleMiddle AgedPhotic StimulationReaction TimeYoung AdultAgeingAttentionLifespanVisual search

Identifiers

PMID40379870
PMCPMC12084335

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.