Perceived Timing of Postural Instability Onset With and Without Vision
Bibliographic record
Abstract
Abstract Background Vision can significantly impact both the perception and behaviour related to postural control. This study examines the influence of vision on the perception of postural instability onset. Previous research employing Temporal Order Judgment (TOJ) tasks to investigate the perceived timing of postural perturbation onset has not incorporated visual cues. Research question Does the presence of visual feedback affect the point of subjective simultaneity (PSS) between postural perturbation onset and an auditory reference stimulus and does this additional sensory cue increase TOJ precision? Methods Using a lean-and-release paradigm, 10 participants were exposed to postural perturbations in both eyes closed (EC) and eyes open (EO) conditions using a TOJ task where they indicated whether they perceived postural instability onset or sound onset as occurring first for each trial. Separate paired t-tests between EC and EO PSS and just noticeable difference (JND) values were used. One-sample t-tests were also used on PSS values for both conditions, comparing them to 0ms (true simultaneity). Results The EC condition demonstrated a perceived delay of postural instability onset by 25.78 ms, while the EO condition showed a perceived delay of the auditory stimulus by 12.33 ms. However, no significant differences were found between the conditions or in comparison to true simultaneity. Mean JND values for EC (39.88 ms) and EO (46.48 ms) were not significantly different, suggesting visual information does not affect response precision for this task under these conditions. Significance These findings indicate that visual information does not significantly affect the perception of postural instability onset. This suggests that visual information may play a limited role in the early perceptual stages of postural instability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".