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Record W2133437179 · doi:10.1080/02699050500456410

Human spatial navigation deficits after traumatic brain injury shown in the arena maze, a virtual Morris water maze

2006· article· en· W2133437179 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

VenueBrain Injury · 2006
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTraumatic brain injuryMorris water navigation taskPsychologyPhysical medicine and rehabilitationTask (project management)Spatial memorySpatial learningVirtual realityWater mazeDevelopmental psychologyMedicineAudiologyNeuroscienceCognitionArtificial intelligenceComputer sciencePsychiatryWorking memoryEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: Survivors of traumatic brain injury (TBI) often have spatial navigation deficits. This study examined such deficits and conducted a detailed analysis of navigational behaviour in a virtual environment. DESIGN: TBI survivors were tested in a computer simulation of the Morris water maze task that required them to find and remember the location of an invisible platform that was always in the same location. A follow-up questionnaire assessed everyday spatial ability. METHOD: Fourteen survivors of moderate-to-severe TBI were compared to 12 non-injured participants. RESULTS: TBI survivors navigated to a visible platform but could not learn the location of the invisible platform. The difference between TBI survivors and uninjured participants was best indicated by two new dependent variables, path efficacy and spatial scores. CONCLUSION: This study confirms the capacity of virtual environments to reveal spatial navigation deficits after TBI and establishes the best way to identify such deficits.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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