Exploring the Impact of Neck Muscle Vibration on Visual search in an ecological environment: Insights from a Museum Study: a brief report.
Bibliographic record
Abstract
Background: Ecological research provides authentic insights into behavior and sensory integration. Neck muscle vibration (NMV) influences proprioception and gaze control, yet its effects in natural settings remain underexplored. Objectives: This study assessed the impact of NMV on visual search and postural activity in a museum setting while addressing methodological challenges of ecological research. Methods: Seventeen older adults (66–79 years) participated in visual search tasks on three large paintings at the Montreal Museum of Fine Arts. Eye-tracking technology and lumbar accelerometer recorded gaze and postural activity under three conditions: no vibration, left NMV, and right NMV. Gaze shifts were analyzed through heatmaps and confidence intervals of gaze extremities. Postural data were compared using non-parametric tests. Results: NMV induced small, nonspecific gaze shifts (2.8°–2.9°) beyond control conditions, with behaviors varying between ipsilateral and contralateral shifts. Postural activity showed no significant differences during static tasks but revealed task-dependent changes during visual search for certain paintings. Inter-painting variability and participant-specific visual strategies complicated comparisons. Discussion: The ecological setting modulated NMV effects on gaze, highlighting individual differences in sensory integration. Results diverged from laboratory studies, where conditions are more controlled. Methodological challenges included variability in gaze patterns and confounding factors like arm movements. Future research should incorporate virtual simulations and standardized stimuli to refine ecological methodologies and reduce variability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".