Identification of 3D shape from texture and motion across the visual field
Bibliographic record
Abstract
Little is known about the perception of 3D shape in the visual periphery. Here we ask whether identification accuracy in shape-from-texture and shape-from-motion tasks can be equated across the visual field with sufficient stimulus magnification. Both tasks employed 3D surfaces comprising hills, valleys, and plains in three possible locations, yielding a 27 alternative forced-choice task (27AFC). Participants performed the task at eccentricities of 0 to 16 deg in the right visual field over a 64-fold range of stimulus sizes. Performance reached ceiling levels at all eccentricities, indicating that stimulus magnification was sufficient to compensate for eccentricity-dependent sensitivity loss. The parameter E(2) (in the equation F = 1 + E / E(2)) was used to characterize the rate at which stimulus size must increase with eccentricity (E) to achieve foveal levels of performance. Three parameter models (mu, sigma, and E(2)) captured most of the variability in the psychometric functions relating stimulus size and eccentricity to accuracy for all participants' data in the two experiments. For the shape-from-texture task, the average E(2) was 1.52, and for the shape-from-motion task, it was 0.61. The E(2) values indicate that sensitivity to structure from motion declines at a faster rate with eccentricity than does sensitivity to structure from texture. Although size scaling with F = 1 + E / E(2) eliminated most eccentricity variation from the structure-from-motion data, there was some evidence that E(2) increases as accuracy decreases in the shape-from-texture task, suggesting that there may be more than one eccentricity-dependent limitation on performance in this task.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".