Evidence map›Paper›PMID 42422912›Full record

ArticleeLife2026

How attention simplifies mental representations for planning.

Jason da Silva Castanheira, Christina Chang He, Nicholas Shea, Stephen M Fleming

Abstract read
In one paragraph

Article in eLife, 2026. 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. 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

4 authors.

Jason da Silva CastanheiraDepartment of Experimental Psychology and Institute for Cognitive Neuroscience, University College London, London, United Kingdom.ORCID 0000-0002-7022-9009
Christina Chang HeDepartment of Experimental Psychology and Institute for Cognitive Neuroscience, University College London, London, United Kingdom.
Nicholas SheaInstitute of Philosophy, School of Advanced Study, University of London, London, United Kingdom.
Stephen M FlemingDepartment of Experimental Psychology and Institute for Cognitive Neuroscience, University College London, London, United Kingdom.ORCID 0000-0003-0233-4891

Funding

European Research Council 101043666Wellcome 206648/Z/17/ZWellcome Trust
6 · The paper itself

Abstract

Human planning is efficient - it frugally deploys limited cognitive resources to accomplish difficult tasks - and flexible - adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception and perception shapes planning - but the perceptual and attentional mechanisms governing how this interaction unfolds remain unknown. Here, we harness virtual maze navigation to characterise how spatial attention controls which aspects of a task representation enter subjective awareness and are available for planning. We find that spatial proximity governs which aspects of a maze are available for planning and that when task-relevant information follows natural (lateralised) contours of attention, people can more easily construct simplified and useful maze representations. This influence of attention varies considerably across individuals, explaining differences in people's task representations and behaviour. Inspired by the 'spotlight of attention

Indexed as

AttentionAdultCognitionDecision MakingFemaleHumansMaleMaze LearningSpace PerceptionYoung Adultattentioncognitive neurosciencecomputational modelhumanneuroscienceplanningpsychologyvisuospatial attention

Identifiers

PMID42422912
PMCPMC13349381

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.