Perceptual and locomotor factors affect obstacle avoidance in persons with visuospatial neglect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: For safe ambulation in the community, detection and avoidance of static and moving obstacles is necessary. Such abilities may be compromised by the presence of visuospatial neglect (VSN), especially when the obstacles are present in the neglected, i.e. contralesional field. METHODS: Twelve participants with VSN were tested in a virtual environment (VE) for their ability to a) detect moving obstacles (perceptuo-motor task) using a joystick with their non-paretic hand, and b) avoid collision (locomotor task) with moving obstacles while walking in the VE. The responses of the participants to obstacles approaching on the contralesional side and from head-on were compared to those during ipsilesional approaches. RESULTS: Up to 67 percent of participants (8 out of 12) collided with either contralesional or head-on obstacles or both. Delay in detection (perceptuo-motor task) and execution of avoidance strategies, and smaller distances from obstacles (locomotor task) were observed for colliders compared to non-colliders. Participants' performance on the locomotor task was not explained by clinical measures of VSN but slower walkers displayed fewer collisions. CONCLUSION: Persons with VSN are at the risk of colliding with dynamic obstacles approaching from the contralesional side and from head-on. Locomotor-specific assessments of navigational abilities are needed to appreciate the recovery achieved or challenges faced by persons with VSN.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it