Age and dementia related differences in spatial navigation within an immersive virtual environment.
Bibliographic record
Abstract
BACKGROUND: Immersive virtual reality (VR) is an innovative tool that can allow study of human spatial navigation in a realistic but controlled environment. The purpose of this study was to examine age- and Alzheimer's disease-related differences in route learning and memory using VR. MATERIAL/METHODS: The spatial memory task took place in a VR environment set up on a Computer Workstation. Participants were immersed by putting video unit goggles over their eyes using a Head Mounted. Participants were shown a path within a virtual city, and then had to navigate it as quickly and accurately as possible. They were granted four learning trials on this path. An interference path was then presented before asking participants to re-navigate the first route at short and long delays. Finally, participants were tested for recognition of the city's buildings and objects. RESULTS: Young adults were consistently quicker and more accurate in their path navigation than older participants whilst those patients with Alzheimer's Disease made more mistakes on the recognition task in particular, being more likely to mistakenly affirm having seen an element in the city when it was in fact a foil. CONCLUSIONS: Our study would suggest that spatial navigation is susceptible to the effects of aging and Alzheimer's Disease. The potential applications of VR to the study of spatial navigation is seemingly important in that it may help place the science of neuropsychology on firmer scientific grounds in terms of its validity to real world function and dysfunction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".