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Cognitive Strategies Dependent on the Hippocampus and Caudate Nucleus in Human Navigation: Variability and Change with Practice

2003· article· en· W2127963272 on OpenAlexafffund
Giuseppe Iaria, Michael Petrides, Alain Dagher, Véronique D. Bohbot

Bibliographic record

VenueJournal of Neuroscience · 2003
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University Institute
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCaudate nucleusFunctional magnetic resonance imagingNeurosciencePsychologyHippocampusTask (project management)Spatial memoryCognitionSpatial cognitionBrain mappingBrain activity and meditationCognitive psychologyElectroencephalographyWorking memory

Abstract

fetched live from OpenAlex

The human brain activity related to strategies for navigating in space and how it changes with practice was investigated with functional magnetic resonance imaging. Subjects used two different strategies to solve a place-learning task in a computer-generated virtual environment. One-half of the subjects used spatial landmarks to navigate in the early phase of training, and these subjects showed increased activation of the right hippocampus. The other half used a nonspatial strategy and showed, with practice, sustained increased activity within the caudate nucleus during navigation. Activation common to both groups was observed in the posterior parietal and frontal cortex. These results provide the first evidence for spontaneous variability and shift in neural mechanisms during navigation in humans.

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)0.000
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0010.000

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 head0.035
GPT teacher head0.286
Teacher spread0.250 · 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

Citations808
Published2003
Admission routes2
Has abstractyes

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