Do Older and Young Adults Learn to Integrate Geometry While Navigating in an Environment of a Serious Game?
Bibliographic record
Abstract
We evaluated the outcomes of an intervention using a serious game designed to be played on iPads for improving spatial reorientation by training users to integrate geometry of the environment, instead of relying solely on featural cues. Using data logged online through a clinical study of using this game, the effect of training among 16 older adults (69.3 ± 6.4 years, 4 males), who played the game repeatedly (self-administered) over a period of 8 weeks, was investigated. The game contains a hexagonal room with 3 objects, textured walls, and grids on the floor, which are removed one by one as the participant played the game. In each level, the room also rotates such that the viewpoint of the user is different from that of the previous level. Participants cannot play a higher level unless they make no mistake during the trials of the lower test level. In addition to data of older adults available from that clinical trial, we recruited 16 young adults (27.3 ± 5.6 years, 4 males) to play the game for 5 sessions and compared their results with those of the older adults. We evaluated the error type made in each test level and the scores for each session among older adults. Further, we compared the frequency of each error type between young and older adults during the test levels that a landmark adjacent to the target was removed over the first 5 sessions. The results of older adults' performance suggest they learned to make fewer mistakes over the sessions. Also, both young and older adults learned to integrate the geometrical cues rather than relying on the landmark cue adjacent to the target to find the target. Overall, the results indicate the designed hexagonal room game can enhance spatial cognition among all age groups of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".