Introducing a new age-and-cognition-sensitive measurement for assessing spatial orientation using a landmark-less virtual reality navigational task
Bibliographic record
Abstract
Age-related impairments during spatial navigation have been widely reported in egocentric and allocentric paradigms. However, the effect of age on more specific navigational components such as the ability to drive or update directional information has not received enough attention. In this study we investigated the effect of age on spatial updating of a visual target after a series of whole-body rotations and transitions using a novel landmark-less virtual reality (VR) environment. Moreover, a significant number of previous studies focused on measures susceptible to a general decline in motor skills such as the spent time navigating, the distance traversed. The current paper proposes a new compound spatial measure to assess navigational performance, examines its reliability and compares its power with those of the measures of duration and traversed distance in predicting participants' age and cognitive groups assessed by Montreal Cognitive Assessment (MoCA) scores. Using data from 319 adults (20-83 years), our results confirm the reliability, the age sensitivity, and the cognitive validity of the designed spatial measure as well as its superiority to the measures of duration and traversed distance in predicting age and MoCA score. In addition, the results show the significant effect of age cognitive status on spatial updating.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".