Attention Differentially Modulates Similar Neuronal Responses Evoked by Varying Contrast and Direction Stimuli in Area MT
Bibliographic record
Abstract
The effects of attention on the responses of visual neurons have been described as a scaling or additive modulation independent of stimulus features and contrast, or as a contrast-dependent modulation. We explored these alternatives by recording neuronal responses in macaque area MT to moving stimuli that evoked similar firing rates but varied in contrast and direction. We presented two identical pairs of stimuli, one inside the neurons' receptive field and the other outside, in the opposite hemifield. One stimulus of each pair always had high contrast and moved in the recorded cell's antipreferred direction (AP pattern), while the other (test pattern) could either move in the cell's preferred direction and vary in contrast, or have the same contrast as the AP pattern and vary in direction. For different stimulus pairs evoking similar responses, switching attention between the two AP patterns, or directing attention from a fixation spot to the AP pattern inside or outside the receptive field, produced a stronger suppression of responses to varying contrast pairs, reaching a maximum ( approximately 20%) at intermediate contrast. For invariable contrast pairs, switching attention from the fixation spot to the AP pattern produced a modulation that ranged from 10% suppression when the test pattern moved in the cells preferred direction to 14% enhancement when it moved in a direction 90 degrees away from that direction. Our results are incompatible with a scaling or additive modulation of MT neurons' response by attention, but support models where spatial and feature-based attention modulate input signals into the area normalization circuit.
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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".