Evidence map›Paper›PMID 36549130›Full record

ArticleCognition2023

Exploration patterns shape cognitive map learning.

Iva K Brunec, Melissa M Nantais, Jennifer E Sutton, Russell A Epstein, Nora S Newcombe

Abstract read
In one paragraph

Article in Cognition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Survey of the intersection of mental maps and visualization.Visual computing for industry, biomedicine, and art · 2026
    Review
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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

5 authors.

Iva K BrunecTemple University; University of Pennsylvania. Electronic address: ivab@sas.upenn.edu.
Melissa M NantaisUniversity of Western Ontario.
Jennifer E SuttonUniversity of Western Ontario; Brescia University College.
Russell A EpsteinUniversity of Pennsylvania.
Nora S NewcombeTemple University.

Funding

Spatial and nonspatial knowledgeR01EY031286 · NEI · UNIVERSITY OF PENNSYLVANIA · PI EPSTEIN, RUSSELL A · 2021 to 2025
$2.6M
NEI NIH HHS R01 EY031286
6 · The paper itself

Abstract

Spontaneous, volitional spatial exploration is crucial for building up a cognitive map of the environment. However, decades of research have primarily measured the fidelity of cognitive maps after discrete, controlled learning episodes. We know little about how cognitive maps are formed during naturalistic free exploration. Here, we investigated whether exploration trajectories predicted cognitive map accuracy, and how these patterns were shaped by environmental structure. In two experiments, participants freely explored a previously unfamiliar virtual environment. We related their exploration trajectories to a measure of how long they spent in areas with high global environmental connectivity (integration, as assessed by space syntax). In both experiments, we found that participants who spent more time on paths that offered opportunities for integration formed more accurate cognitive maps. Interestingly, we found no support for our pre-registered hypothesis that self-reported trait differences in navigation ability would mediate this relationship. Our findings suggest that exploration patterns predict cognitive map accuracy, even for people who self-report low ability, and highlight the importance of considering both environmental structure and individual variability in formal theory- and model-building.

Indexed as

Spatial NavigationCognitionHumansLearningSpace PerceptionCognitive mapEnvironmental structureExplorationRoamingSpace syntaxSpatial navigationVirtual reality

Identifiers

PMID36549130
PMCPMC9983142

What OpenQuestion holds

Textmetadata
LicenceTDM
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.