MétaCan
Menu
Back to cohort
Record W2957945366 · doi:10.1111/cdev.13285

The Emergence of Cognitive Maps for Spatial Navigation in 7- to 10-Year-Old Children

2019· article· en· W2957945366 on OpenAlexafffund
Ford Burles, Irene Liu, Chelsie M. Hart, Kara Murias, Susan A. Graham, Giuseppe Iaria

Bibliographic record

VenueChild Development · 2019
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Children's Hospital FoundationChildren's Hospital Foundation
KeywordsPsychologySpatial abilityCognitionCognitive developmentChild developmentDevelopmental psychologySpatial cognitionCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

Although much is known about adults' ability to orient by means of cognitive maps (mental representations of the environment), it is less clear when this important ability emerges in development. In the present study, 97 seven- to 10-year-olds and 26 adults played a video game designed to investigate the ability to orient using cognitive maps. The game required participants to reach target locations as quickly as possible, necessitating the identification and use of novel shortcuts. Seven- and 8-year-olds were less effective than older children and adults in using shortcuts. These findings provide clear evidence of a distinct developmental change around 9 years of age when children begin to proficiently orient and navigate using cognitive maps.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueChild DevelopmentSame topicSpatial Cognition and NavigationFrench-language works237,207