Functional specialization in the middle temporal area for smooth pursuit initiation
Why this work is in the frame
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Bibliographic record
Abstract
<ns4:p>Smooth pursuit eye movements have frequently been used to model sensorimotor transformations in the brain. In particular, the initiation phase of pursuit can be understood as a transformation of a sensory estimate of target velocity into an eye rotation. Despite careful laboratory controls on the stimulus conditions, pursuit eye movements are frequently observed to exhibit considerable trial-to-trial variability. In theory, this variability can be caused by the variability in sensory representation of target motion, or by the variability in the transformation of sensory information to motor commands. Previous work has shown that neural variability in the middle temporal (MT) area is likely propagated to the oculomotor command, and there is evidence to suggest that the magnitude of this variability is sufficient to account for the variability of pursuit initiation. This line of reasoning presumes that the MT population is homogeneous with respect to its contribution to pursuit initiation. At the same time, there is evidence that pursuit initiation is strongly linked to a subpopulation of MT neurons (those with strong surround suppression) that collectively generate less motor variability. To distinguish between these possibilities, we have combined human psychophysics, monkey electrophysiology, and computational modeling to examine how the pursuit system reads out the MT population during pursuit initiation. We find that the psychophysical data are best accounted for by a model that gives stronger weight to surround-suppressed MT neurons, suggesting that variability in the initiation of pursuit could arise from multiple sources along the sensorimotor transformation.</ns4:p>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it