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Record W2102884801 · doi:10.1002/hipo.20303

Spontaneous navigational strategies and performance in the virtual town

2007· article· en· W2102884801 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 · 2007
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsSpatial memoryFunctional magnetic resonance imagingPsychologyTask (project management)HippocampusCognitive psychologyVirtual realityNeuroscienceVirtual machineResponse inhibitionSpatial cognitionCaudate nucleusComputer scienceHuman–computer interactionWorking memoryCognition

Abstract

fetched live from OpenAlex

The 4-on-8 virtual maze provides evidence for variability in spontaneous strategy use during navigation. Functional magnetic resonance imaging (MRI) confirmed that these spatial and response strategies rely on the hippocampus and caudate nucleus memory systems, respectively. We asked whether the spontaneous use of a particular navigational strategy was associated with a particular ability to navigate in one's environment. We tested 30 young participants on the 4-on-8 virtual maze and we assessed their way finding ability in a virtual town. As expected, spatial learners performed well in the virtual town and the response learners, who never used external landmarks and relied purely on an egocentric strategy, performed poorly. Interestingly, a group who used the most efficient response strategy based on external landmarks in the 4-on-8 virtual maze, switched to the most efficient spatial strategy in the virtual town. Our data suggest that the best navigators are those who appropriately use spatial or response strategies depending on the demands of the task.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.243

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 head0.041
GPT teacher head0.285
Teacher spread0.244 · 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