A cortical locus for anisotropic overlay suppression of stimuli presented at fixation
Bibliographic record
Abstract
Human contrast sensitivity for narrowband Gabor targets is suppressed when superimposed on narrowband masks of the same spatial frequency and orientation (referred to as overlay suppression), with suppression being broadly tuned to orientation and spatial frequency. Numerous behavioral and neurophysiological experiments have suggested that overlay suppression originates from the initial lateral geniculate nucleus (LGN) inputs to V1, which is consistent with the broad tuning typically reported for overlay suppression. However, recent reports have shown narrowly tuned anisotropic overlay suppression when narrowband targets are masked by broadband noise. Consequently, researchers have argued for an additional form of overlay suppression that involves cortical contrast gain control processes. The current study sought to further explore this notion behaviorally using narrowband and broadband masks, along with a computational neural simulation of the hypothesized underlying gain control processes in cortex. Additionally, we employed transcranial direct current stimulation (tDCS) in order to test whether cortical processes are involved in driving narrowly tuned anisotropic suppression. The behavioral results yielded anisotropic overlay suppression for both broadband and narrowband masks and could be replicated with our computational neural simulation of anisotropic gain control. Further, the anisotropic form of overlay suppression could be directly modulated by tDCS, which would not be expected if the suppression was primarily subcortical in origin. Altogether, the results of the current study provide further evidence in support of an additional overlay suppression process that originates in cortex and show that this form of suppression is also observable with narrowband masks.
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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".