Binocular integration of simultaneous density contrast
Why this work is in the frame
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Bibliographic record
Abstract
Texture density, defined as the number of elements per unit visual area, is an important perceptual dimension that is typically studied in two-dimensions (2D) - however it is unclear how we represent texture density information in three-dimensions (3D). One study has suggested that density is represented as if projected onto a 2D plane, based on the finding that density perception is unaffected by the range of depth over which the elements are distributed (Bell, Manson, Edwards, & Meso, 2015). Here we explored the 3D properties of density coding using simultaneous density contrast (SDC), in which the perceived density of a texture region is altered by a surround of different density (Sun et al., 2016). We used a 2AFC staircase procedure in which human observers compared the perceived density of a test plus surround with a match having no surround. We first manipulated the stereo-disparity of the surround plane systematically from near to far relative to the center plane (Experiment 1), and from a small to a large range of random depths (Experiment 2). We found weaker SDC when the center and surround planes were separated in depth, and when the surround dots were distributed across a large depth range. However these binocular SDC effects were found only for dense not sparse surrounds. We also measured SDC with center and surround presented dichoptically, monoptically and binocularly (Experiment 3). Strong interocular transfer of SDC was found in the dichoptic condition, in line with previous evidence showing interocular transfer of density adaptation (Durgin, 2001). Our data suggest that binocular information influences texture density processing, challenging the previous view of a solely 2D representation of texture density. Meeting abstract presented at VSS 2018
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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 it