The Virtual Shop: A new immersive virtual reality environment and scenario for the assessment of everyday memory
Bibliographic record
Abstract
BACKGROUND: Assessing and predicting memory performance in everyday life is a common assignment for neuropsychologists. However, most traditional neuropsychological tasks are not conceived to capture everyday memory performance. NEW METHOD: The Virtual Shop is a fully immersive task developed to assess memory in a more ecological way than traditional neuropsychological assessments. Two studies were undertaken to assess the feasibility of the Virtual Shop and to appraise its ecological and construct validity. In study 1, 20 younger and 19 older adults completed the Virtual Shop task to evaluate its level of difficulty and the way the participants interacted with the VR material. The construct validity was examined with the contrasted-group method, by comparing the performance of younger and older adults. In study 2, 35 individuals with subjective cognitive decline completed the Virtual Shop task. Performance was correlated with an existing questionnaire evaluating everyday memory in order to appraise its ecological validity. To add further support to its construct validity, performance was correlated with traditional episodic memory and executive tasks. RESULTS: All participants successfully completed the Virtual Shop. The task had an appropriate level of difficulty that helped differentiate younger and older adults, supporting the feasibility and construct validity of the task. COMPARISON WITH EXISTING METHOD(S): The performance on the Virtual Shop was significantly and moderately correlated with the performance on the questionnaire and on the traditional memory and executive tasks. CONCLUSIONS: Results support the feasibility and both the ecological and construct validity of the Virtual Shop.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".