Improving the ecological validity of cognitive functions assessment through Virtual Environments : Results of a usability evaluation with healthy adults
Bibliographic record
Abstract
The early detection of mild cognitive impairment has the potential to significantly impact the lives of individuals who may be unaware of their cognitive decline. Such a condition can greatly affect their quality of life and ability to perform daily activities. Cognitive screening tests have been developed to identify those who may have cognitive problems or who may be at risk of developing them in the future, aiming to provide appropriate evaluation and treatment. However, a challenge with current tools is the limited correlation often observed between formal tests of executive functions and real-life functioning, which is known as ecological validity. To address this issue, cutting-edge technologies like Virtual Reality can be utilized to assess cognitive functions and increase ecological validity, resulting in more reliable assessments. This study presents a virtual reality application called CE360°VR within which various cognitive functions are examined through tasks, while both quantitative and qualitative data are collected. To achieve this, a set of fourteen scenes has been defined, drawing from standardized tests such as the “Montreal Cognitive Assessment” and the “Mini-Mental State Examination”. The application has been developed using Unity, and the virtual environments are recreated using 360° pictures. The study involves a sample of healthy adults (n=68), aiming to assess the usability and perceived user experience of the Virtual Reality Application. A questionnaire has been administered at the end of the experience including three different standardized scales: System Usability Scale, User Experience Questionnaire, and Affinity for Technology Interaction Scale. According to the results of the study, the Virtual Reality application is classified as “good” with an “acceptable” level of usability, as per the adjective scale developed by Bangor et al. The data collected, which turned into performance indicators of the participants, was found to have no correlation with either the technology affinity or the age. Instead, it was observed that the instruction level of the patients was correlated with their performance. Building upon these findings, a normative study would be necessary to standardize the results and consider this system as a reliable and valid tool for clinical use.
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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 | 0.011 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".