Cortical orientation domains are invariant with carrier type for contrast envelopes
Bibliographic record
Abstract
We have recently demonstrated (Zhan & Baker, 2003) highly similar cortical orientation domains for three kinds of stimuli (sinewave gratings, contrast envelopes, and illusory contours) differing in the type of stimulus attribute being modulated (luminance, contrast, and phase, respectively). We now ask whether the orientation domains for contrast envelopes are dependent on the nature of the carrier texture whose contrast is modulated. We have used intrinsic signal optical imaging to compare the spatial organization of orientation domains in cat A18 for 4 kinds of contrast envelopes, which differed in their carrier types — 1D sinewave gratings, regular checkerboards, random check noise, and 2D fractal noise. The envelope spatial frequencies were in the optimal range as for first-order stimuli (0.065–0.15 cpd). Although these carriers differed qualitatively in their power spectra (as well as appearance), we found invariant orientation domains as long as the carrier spatial frequencies (0.652–1.3 cpd) were outside the luminance passband for the measured cortical area. All these stimuli activated similar orientation domains as first-order visual stimuli, as demonstrated by near-zero differential orientation maps, and near-unity correlation coefficients between all the orientation maps. Together with the previous study, these results suggest that the neurons responsive to different kinds of orientation contours are evenly distributed across A18, and that coarse-scale orientation is coded by anatomical domains which are invariant to the composition of the oriented stimuli. Canadian CIHR Grant MOP 9685 to CLB
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".