Using virtual reality simulation to study navigation in a complex environment as a functional-cognitive task; A pilot study
Bibliographic record
Abstract
BACKGROUND: Navigation skills are required for performance of functional complex tasks and may decline due to aging. Investigation of navigation skills should include measurement of cognitive-executive and motor aspects, which are part of complex tasks. OBJECTIVE: to compare young and older healthy adults in navigation within a simulated environment with and without a functional-cognitive task. METHODS: Ten young adults (25.6±4.3 years) and seven community dwelling older men (69.9±3.8 years) were tested during a single session. After training on a self-paced treadmill to navigate in a non-functional simulation, they performed the Virtual Multiple Errands Test (VMET) in a mall simulation. Outcome measures included cognitive-executive aspects of performance and gait parameters. RESULTS: Younger adults' performance of the VMET was more efficient (1.8±1.0) than older adults (5.3±2.7; p < 0.05) and faster (younger 478.1±141.5 s, older 867.6±393.5 s; p < 0.05). There were no differences between groups in gait parameters. Both groups walked slower in the mall simulation. CONCLUSIONS: The shopping simulation provided a paradigm to assess the interplay between motor and cognitive aspects involved in the efficient performance of a complex task. The study emphasized the role of the cognitive-executive aspect of task performance in healthy older adults.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".