Visuospatial attention during obstacle crossing: A pilot study
Bibliographic record
Abstract
Crossing an obstacle requires visuospatial attetion (VSA) to identify the target in space so one can safely overcome the barrier without falling. In this study, we designed a VSA task that was embedded in an obstacle-crossing gait task to examine directly how these abilities interact. Seven subjects performed the VSA task projected on the floor during quiet standing and during obstacle-crossing gait task. The VSA task required the subjects to identify a briefly presented (500ms) stimulus (E or 3) among distractors (2s and 5s) within a visual display as quickly and accurately as possible. We positioned the stimulus at 1 of 9 locations around a circle (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, central). Each stimulus occurred five times in each location in a random order resulting in 90 trials in total. The obstacle was set to 10% height of the subject's height. As expected, the subjects performed the VSA task more accurately during quiet standing (87.76%) compared to obstacle crossing (79.80%). In addition, however, during the obstacle-crossing trials, accuracy in the VSA task was better for targets on the left-hand side of space compared to the right-hand side of space. Thus, the processes underlying VSA appear to be biased by the obstacle-crossing gait task.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".