REAsmash-ET: a methodological framework for combined cognitive and motor assessment through eye-tracking and kinematic metrics in immersive VR search-and-reach task
Bibliographic record
Abstract
BACKGROUND: Virtual Reality (VR) Serious Games (SGs) can provide a functionally relevant framework to capture cognitive and motor dynamics. Their interactive and engaging nature improves compliance, measurement reliability and allows for more frequent evaluations. Additionally, VR SGs enable the parallel collection of multiple types of data within a single session. We present REAsmash-ET, an immersive VR adaptation of the REAsmash SG, grounded in Feature Integration Theory (FIT) and integrating eye-tracking (ET) and upper limb kinematic (UL) analyses. REAsmash-ET introduces a novel methodological framework for the simultaneous assessment of attentional and motor functions in VR. METHODS: REAsmash is an interactive search-and-reach task designed to elicit structured visual exploration and UL motor responses under varying target-distractor saliency conditions. Custom algorithms extract metrics on visual search strategies and UL motor efficiency. Three age groups of adult healthy participants (n = 15 each) were included to test the feasibility and methodological consistency of the task and its metrics. Relative Response Time (RRT) and ET metrics were analyzed using ANOVA with factors: age group (20-39, 40-59, 60-80 years), target-distractor saliency (high vs. low), and number of distractors (11, 17, 23). Kinematic metrics were analyzed by age group and response hand (dominant vs. non-dominant). RESULTS: REAsmash-ET differentiated visuomotor performance across task conditions. RRT and ET metrics showed significant effects of saliency, number of distractors, and their interaction, consistent with FIT. Age-related differences emerged in both RRT and visual search efficiency. Kinematic analyses revealed slower and less efficient movements in older participants, with effects of hand dominance.. The results support the robustness and feasibility of REAsmash-ET as a methodological framework. CONCLUSIONS: The results support the robustness and internal consistency of REAsmash-ET as a methodological framework for the integrated assessment of visual attention and UL motor control in immersive VR. The task's ability to capture visuomotor variability and its multidimensional approach highlight its potential for future research and clinical applications in both healthy and clinical populations. REGISTRY NUMBER: This study was registered at ClinicalTrials.gov (NCT04694833).
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".