Dissociable Spatial and Feature Tuning of Gamma and Alpha Rhythms in Human Visual Cortex
Bibliographic record
Abstract
Abstract Visual stimulation in humans reliably induces narrowband gamma (40–80 Hz) increases and alpha (8–12 Hz) suppression in EEG, but the relationship between these rhythms is not fixed. Using high-density EEG, we mapped gamma and alpha responses across a broad set of retinotopic, orientation, and motion conditions. Gamma was strongly retinotopically tuned, with linear summation of subfield responses accurately predicting full-field responses. In contrast, alpha showed little spatial tuning, substantial responses even in the absence of visual input (anticipatory suppression), and subadditive summation consistent with divisive normalization. Across most retinotopic configurations, higher gamma coincided with stronger alpha suppression, yet systematic dissociations emerged, with full-field and foveal-centered gratings evoking stronger than expected gamma relative to alpha suppression. Orientation tuning was robust for gamma but weak for alpha, with oblique gratings producing high gamma yet weak alpha suppression, reversing the usual inverse relationship. These patterns indicate that EEG gamma power primarily reflects large-scale synchronous activity that matches the ‘global LFP’ observed in macaque V1, whose spatial and feature tuning properties are independent of both single-neuron selectivity and feedback-driven alpha dynamics. The results establish a mechanistic dissociation between gamma and alpha rhythms, highlighting distinct circuit origins and tuning principles for these canonical visual responses.
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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 | 0.000 |
| 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) | 0.001 | 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".