People with dementia experiencing presence in mixed reality technologies
Bibliographic record
Abstract
Abstract Background Mixed Reality Technologies (MRTs) spanning the physical‐virtual continuum offer possibilities for machine‐based prompting to support people with dementia. The effectiveness of MRTs requires users to experience ‘presence’ – the feeling of being there and the illusion of non‐ mediation by the technology ‐ which has not been explored in dementia. Method Participants with mild to moderate dementia aged between 63‐90 years of age played games on two MRTs: Tangram on Osmo (Augmented Virtuality) and Young Conker on Hololens (Augmented Reality) in separate sessions to explore their experience of presence and look at their responses to prompts in the games. The sessions were video‐recorded for analysis with Noldus Observer XT 14.1 to identify themes relating to presence and responses to prompts. Result Three themes relating to the experience of presence were identified: 1. Affordances and perceptual elements, 2. Degree of realism and 3. Social element. Participants responded differently to visual and verbal prompts in the games. Conclusion People with dementia experience presence in MRTs that is mediated by the clues in the visual auditory and material properties of the physical and virtual elements of the games. These findings have design implications for future development of MRTs to support people with 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".