Amblyopic deficits in structure-from-motion processing
Bibliographic record
Abstract
Amblyopia is known to include striate deficits (e.g. Barnes et al., 2001; Movshon et al., 1987), but less is known about whether extra-striate mechanisms are additionally impacted (e.g. Barnes et al., 2001; Kiorpes, 1998; Simmers, 2006). To tap into extra-striate processing, we tested amblyopic and control observers on a structure-from-motion (SFM) task, which requires integration of local elements to perceive the global structure. Subjects were monocularly presented with a 2-IFC shape discrimination task, indicating whether two consecutive SFM stimuli represented the same or different shapes. Two interleaved 3-down-1-up staircases were used to determine the threshold amount of depth necessary to distinguish the shapes. Preliminary data with 10 amblyopic and 10 control subjects indicates that amblyopes need more depth information to reliably discriminate shape identities (t(38) = 2.5, p = .02), and that this deficit was present for both the amblyopic and fellow-fixing eyes (t(9) = 1.22, p = .26). These results suggest that amblyopic observers experience a deficit in SFM processing. Future work will adapt this task to incorporate band-pass local elements, which will enhance our ability to control for low-level contributions to this task, and help differentiate whether deficits in this task are specific to high-level neural processing mechanisms, or whether these deficits are adequately accounted for by existing known low-level deficits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".