Comparisons of Target Localization Abilities during Physical and Virtual Rotating Scenes by Cognitively-Intact and Cognitively Impaired Older Adults
Bibliographic record
Abstract
Background: Previous studies have reported that coordinate information (i.e. distance between any two objects in a specific direction) is encoded differently from Virtual Reality (VR) and physical scenes. However, the accuracy of encoding categorical information (i.e. relative positions of objects) from VR scenes has not been adequately investigated. During this study, we used a novel rotating visual scene to study the effects of aging, prior experience with VR, and dementia on the accuracy of encoding categorical information between physical and virtual environments. Methods: We recruited a cohort of 60 cognitively-healthy older adults, with and without previous VR experience (Experiment 1), as well as 18 older adults with mild to moderate Alzheimer disease (AD) (Experiment 2). During both of the experiments, the participants were asked to attend to a target window in a virtual or real small-scale model building (dependent upon group assignment) as the building was rotated around its vertical axis in depth of the scene. Participants were required to verbally judge the final position of the target in terms of direction (e.g., left, right, back, and front) with respect to the entrance of the buildings after the full rotation has stopped. A score was calculated for each participant based on s/her accuracy in locating the target window. Results: Healthy older adults succeeded in accurately localizing the target's position from both environments, whereas individuals with AD were only able to encode the target’s position from the physical environment. Conclusions: Our results suggest the inability to encode from a rotating VR scene might be a symptom of dementia.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".