Modulation of the Contrast Response Function by Electrical Microstimulation of the Macaque Frontal Eye Field
Bibliographic record
Abstract
Spatial attention influences representations in visual cortical areas as well as perception. Some models predict a contrast gain, whereas others a response or activity gain when attention is directed to a contrast-varying stimulus. Recent evidence has indicated that microstimulating the frontal eye field (FEF) can produce modulations of cortical area V4 neuronal firing rates that resemble spatial attention-like effects, and we have shown similar modulations of functional magnetic resonance imaging (fMRI) activity throughout the visual system. Here, we used fMRI in awake, fixating monkeys to first measure the response in 12 visual cortical areas to stimuli of varying luminance contrast. Next, we simultaneously microstimulated subregions of the FEF with movement fields that overlapped the stimulus locations and measured how microstimulation modulated these contrast response functions (CRFs) throughout visual cortex. In general, we found evidence for a nonproportional scaling of the CRF under these conditions, resembling a contrast gain effect. Representations of low-contrast stimuli were enhanced by stimulation of the FEF below the threshold needed to evoke saccades, whereas high-contrast stimuli were unaffected or in some areas even suppressed. Furthermore, we measured a characteristic spatial pattern of enhancement and suppression across the cortical surface, from which we propose a simple schematic of this contrast-dependent fMRI response.
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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".