Bibliographic record
Abstract
People with amblyopia present deficit in spatial vision such as low acuity and bad contrast sensitivity, but also in motion perception such as inaccurate estimation of speed. In this study, we wanted to test motion trajectory estimation in amblyopic participants. We used a motion prediction task. Participants needed to estimate the time at which a target would reach a designated goal, after its motion had been occluded. The stimuli presented was a white bar moving at a constant speed (2.5, 5, 10, 15 deg/s) for a fixed viewing distance (3, 6, 9 deg) before being occluded for the rest of its trajectory (1, 2, 4, 8 deg). The participants’ task was to press a button when they thought that the occluded target reached the designated goal. We tested three different visual conditions: monocular (amblyopic or fellow eye) and binocular. At medium speeds (5 and 10 deg/s), participants estimated correctly the time-to-reach of the target. However, they overestimated the distance when the speed was high (15 deg/s) and an underestimated it when the speed was low (2.5 deg/s). As occlusion distance was increased, at 15 deg/s, they were performing better. However, at 2.5 deg/s speed, the opposite pattern was observed: increasing the occlusion distance led to worse performance. No difference was observed between the monocular and binocular viewing conditions. Speed seems to be a critical parameter for the ability of amblyopic people to accurately estimate trajectories. Occlusion distance had variable impact on performance at lowest and highest speeds.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".