Bibliographic record
Abstract
Patterns of sparse texture elements that vary smoothly in depth appear to lie on a three-dimensional surface when depth is interpolated across the intervening blank spaces. Estimates of the upper limit of 3D surface interpolation range from 3 min to 0.3 deg (Westheimer 1986, Yang and Blake 1995). However, physiological studies have shown that there is a broad range of receptive field sizes in the visual cortex. Simple cells in area V1 range in width from 0.05 to 0.5 deg (Parker and Hawkin, 1988) and in cortical areas such as V2, V3 and V4 receptive fields are substantially larger. The existence of different sized receptive fields at a given retinal location indicates that processing occurs at different scales. Thus, one might expect that surface interpolation is supported over a range of stimulus scales, and the upper limit is scale dependent. We tested this proposition by assessing 3D interpolation at a large range of scales using a bisection task that has proven to be a reliable index of 3D interpolation. Surface interpolation was assessed for stimuli with widths ranging from 15 to 58 deg. Once irrelevant task variables are taken into account, 3D surface interpolation is scale invariant up to an average nearest neighbour element spacing of 3 deg (SD 2 deg). These experiments provide strong evidence for scale invariance of 3D surface interpolation, and for an upper limit that is scale dependent. These data also show that previous estimates underestimate the capacity of the human visual system to interpolate surfaces in depth.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".