Can perceptual learning alleviate the global motion direction discrimination deficit in amblyopia?
Bibliographic record
Abstract
Amblyopia causes a contrast sensitivity deficit in the amblyopic eye. However there is also evidence of additional deficits affecting functions further along the processing stream, e.g. an impairment in global motion processing that is not specific to the amblyopic eye. Perceptual learning studies on normal observers have demonstrated significant improvements in global motion tasks. On this basis, we have applied these methods in an attempt to: i) improve global motion processing in amblyopic observers, and ii) determine whether the learning effects are specific to the trained eye. We tested 5 normals and 6 amblyopes on a motion direction discrimination task. Our stimuli were a field of isotropic log-Gabors with peak spatial frequency of 3 c/deg (spatially band-pass "dots"). In each trial we first presented a stimulus with a fixed reference motion direction, and then a test stimulus with its motion direction defined as an offset from that reference. The observer responded whether the second interval's direction was clockwise or anti-clockwise relative to the first. The difficulty of the task was varied by modifying the offset angle. We measured monocular baseline thresholds for each eye (day 1), and then conducted 10 days of monocular training for 40 minutes/day (days 2-11). Half of the amblyopes trained with their amblyopic eye, half with their fellow eye. After training we then made two retest measurements for each eye (days 12 and 13). Surprisingly, we do not find the expected training effect in either our normal or our amblyopic observers. Thresholds were generally lower following training, however this difference is not statistically significant. It is possible that the critical difference between our study and those that have found large training effects is the spatially broadband nature of the stimuli used in previous studies. Meeting abstract presented at VSS 2016
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".