The selectivity of responses to red‐green colour and achromatic contrast in the human visual cortex: an fMRI adaptation study
Bibliographic record
Abstract
There is controversy as to how responses to colour in the human brain are organized within the visual pathways. A key issue is whether there are modular pathways that respond selectively to colour or whether there are common neural substrates for both colour and achromatic (Ach) contrast. We used functional magnetic resonance imaging (fMRI) adaptation to investigate the responses of early and extrastriate visual areas to colour and Ach contrast. High-contrast red-green (RG) and Ach sinewave rings (0.5 cycles/degree, 2 Hz) were used as both adapting stimuli and test stimuli in a block design. We found robust adaptation to RG or Ach contrast in all visual areas. Cross-adaptation between RG and Ach contrast occurred in all areas indicating the presence of integrated, colour and Ach responses. Notably, we revealed contrasting trends for the two test stimuli. For the RG test, unselective processing (robust adaptation to both RG and Ach contrast) was most evident in the early visual areas (V1 and V2), but selective responses, revealed as greater adaptation between the same stimuli than cross-adaptation between different stimuli, emerged in the ventral cortex, in V4 and VO in particular. For the Ach test, unselective responses were again most evident in early visual areas but Ach selectivity emerged in the dorsal cortex (V3a and hMT+). Our findings support a strong presence of integrated mechanisms for colour and Ach contrast across the visual hierarchy, with a progression towards selective processing in extrastriate visual areas.
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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".