Bibliographic record
Abstract
The dorsal visual pathway in primates has a hierarchical organization, with neurons in V1 coding local velocities and neurons in the later stages of the extrastriate cortex encoding complex motion patterns. In order to understand the computations that occur along each stage of the hierarchy, we have recorded from single neurons in areas V1, MT, and MST of the alert macaque monkey. Results with standard plaid stimuli show that pattern motion selectivity is, not surprisingly, more common in area MST than in MT or V1. However, similar results were found with plaids that were made perceptually transparent, suggesting that neurons at more advanced stages of the hierarchy tend to integrate motion signals obligatorily, even when the composition of the stimulus is more consistent with the motion of multiple objects. Thus neurons in area MST in particular show a tendency for increased motion integration that does not necessarily correlate with the (presumptive) perception of the stimulus. Data from local field potentials recorded simultaneously show a strong bias toward component selectivity, even in brain regions in which the spiking activity is overwhelmingly pattern selective. This suggests that neurons with greater pattern selectivity are not overrepresented in the outputs of areas like V1 and MT, but rather that the visual system computes pattern motion multiple times at different hierarchical stages. Moreover, our results are consistent with the idea that LFPs can be used to estimate different anatomical contributions to processing at each visual cortical stage.
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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 | 0.001 | 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".