Characterization of Spatial Frequency Channels Underlying Disparity Sensitivity by Factor Analysis of Population Data
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Bibliographic record
Abstract
It has been suggested that at least two mechanisms mediate disparity processing, one for coarse and one for fine disparities. Here we analyze individual differences in our previously measured normative dataset on the disparity sensitivity as a function of spatial frequency of 61 observers to assess the tuning of the spatial frequency channels underlying disparity sensitivity for oblique corrugations (Reynaud et al., 2015). Inter-correlations and factor analysis of the population data revealed two spatial frequency channels for disparity sensitivity: one tuned to high spatial frequencies and one tuned to low spatial frequencies. Our results confirm that disparity is encoded by spatial frequency channels of different sensitivities tuned to different ranges of corrugation frequencies.
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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