Area VO in human visual cortex is color selective as revealed by fMRI adaptation
Bibliographic record
Abstract
Introduction: We use an fMRI adaptation paradigm to investigate the selectivity of the human visual cortex to red-green (RG) and achromatic (Ach) contrast, comparing regions in early visual cortex (V1, V2) with those in the dorsal (V3d, V3a, hMT+) and ventral (V3v, V4, VO) cortex. Methods: RG and Ach adaptation and no-adaptation conditions were contrasted within a block design. Test and adapting stimuli were high contrast sinewave counter-phasing rings (0.5cpd, 2Hz), as previously described (Chang et al, JOV, 2014, 14 (10) 983). Regions of interest (as listed above) were independently localized using standard procedures. We assume that cross-adaptation of responses to RG and achromatic stimuli indicates a common neural substrate for both, whereas a lack of cross-adaptation indicates selective neural responses within voxels. Selectivity was defined as significantly greater same-adaptation (RG on RG, or Ach on Ach) than cross-adaptation (RG on Ach, or vice versa), established by RM ANOVAs. Results: Adaptation was present in all areas except for hMT+, which showed no color adaptation. Areas V1 and V2 showed no selectivity of adaptation; specifically, both RG and Ach test stimuli were adapted as much by the cross adaptor as by the same adaptor. In the dorsal cortex, areas V3d, V3a, hMT+ showed significant selectivity for achromatic contrast. In the ventral cortex, VO showed significant selectivity for RG color contrast. Conclusion: Color-luminance responses are dominant in areas V1 and V2 with selectivity developing along the extrastriate pathways. While dorsal areas show selectivity for achromatic contrast, ventral cortex (VO) exhibits selectivity for RG color contrast. Area VO has previously been shown to be a color responsive area in human cortex. Here we show that it is also color selective, suggesting it plays a significant role in color processing. Meeting abstract presented at VSS 2015
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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 | 0.000 | 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) | 0.001 | 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".