MétaCan
Menu
Back to cohort
Record W4407850755 · doi:10.1080/13875868.2025.2467624

Exploring human spatial orientation and navigation with electroencephalography: a scoping review

2025· review· en· W4407850755 on OpenAlexafffund
Michael McLaren-Gradinaru, Ford Burles, Kelsey Cnudde, Alia Damji, Lila Berger, Nicole Betts, Hessan Hanif, Andrea B. Protzner, Giuseppe Iaria

Bibliographic record

VenueSpatial Cognition and Computation · 2025
Typereview
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroencephalographyOrientation (vector space)Computer scienceHuman–computer interactionArtificial intelligenceComputer visionPsychologyNeuroscience

Abstract

fetched live from OpenAlex

This scoping review explores and describes how electroencephalography (EEG) has been used to study higher-order spatial orientation and navigation in healthy adults. 22 studies were included, where key findings highlighted the presence of theta (especially frontal-midline) during spatial memory encoding and alpha desynchronization during complex wayfinding. Event-related potentials revealed rapid changes in neural processing tied to path decisions, reward feedback, and navigation errors. Source localization findings suggest regions such as the retrosplenial complex and posterior parietal cortex are involved in egocentric and allocentric strategies. These results reinforce the value of EEG in capturing unique neural activity underlying spatial navigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.010
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.333
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueSpatial Cognition and ComputationSame topicSpatial Cognition and NavigationFrench-language works237,207