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Record W1996935070 · doi:10.1002/hipo.20400

Navigational skills correlate with hippocampal fractional anisotropy in humans

2008· article· en· W1996935070 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHippocampus · 2008
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFractional anisotropyHippocampal formationHippocampusDiffusion MRIAnisotropyOrientation (vector space)NeurosciencePsychologyPhysicsGeometryOpticsMathematicsMedicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Individuals vary widely in their ability to orient within the environment. We used diffusion tensor imaging to investigate whether this ability, as measured by navigational performance in a virtual environment, correlates with the anatomic structural properties of the hippocampus, i.e., fractional anisotropy. We found that individuals with high fractional anisotropy in the right hippocampus are (a) faster in forming a cognitive map of the environment, and (b) more efficient in using this map for the purpose of orientation, than individuals with low fractional anisotropy. These results are consistent with the role of the hippocampus in navigation, and suggest that its microstructural properties may contribute to the intersubject variability observed in spatial orientation.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.357
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.205
Teacher spread0.198 · 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