Expansion of MT Neurons Excitatory Receptive Fields during Covert Attentive Tracking
Bibliographic record
Abstract
Primates can attentively track moving objects while keeping gaze stationary. The neural mechanisms underlying this ability are poorly understood. We investigated this issue by recording responses of neurons in area MT of two rhesus monkeys while they performed two different tasks. During the Attend-Fixation task, two moving random dot patterns (RDPs) translated across a screen at the same speed and in the same direction while the animals directed gaze to a fixation spot and detected a change in its luminance. During the Tracking task, the animals kept gaze on the fixation spot and attentively tracked the two RDPs to report a change in the local speed of one of the patterns' dots. In both conditions, neuronal responses progressively increased as the RDPs entered the neurons' receptive field (RF), peaked when they reached its center, and decreased as they translated away. This response profile was well described by a Gaussian function with its center of gravity indicating the RF center and its flanks the RF excitatory borders. During Tracking, responses were increased relative to Attend-Fixation, causing the Gaussian profiles to expand. Such increases were proportionally larger in the RF periphery than at its center, and were accompanied by a decrease in the trial-to-trial response variability (Fano factor) relative to Attend-Fixation. These changes resulted in an increase in the neurons' performance at detecting targets at longer distances from the RF center. Our results show that attentive tracking dynamically changes MT neurons' RF profiles, ultimately improving the neurons' ability to encode the tracked stimulus features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".