Quantitative Reassessment of Speed Tuning in the Accessory Optic System and Pretectum of Pigeons
Bibliographic record
Abstract
The correlation model of motion detection has been used to describe visual motion processing in the pretectum and accessory optic system (AOS). One feature of correlation detectors is that they are tuned to a particular temporal frequency (TF) independent of the spatial frequency (SF) but not to a particular stimulus speed (speed = TF/SF). Previous work has suggested that a subset of neurons in the AOS and pretectum of pigeons show apparent speed tuning. However, this study used relatively liberal between-groups statistics to assess speed tuning. From studies of the motion-sensitive neurons in primate cortex, a rigorous within-groups test of speed tuning has been offered. A meta-analysis of the spatiotemporal tuning of units in the AOS and pretectum of pigeons using this within-groups analysis of speed tuning has been performed. We conclude that speed tuning in the AOS and pretectum is rarer than previously estimated, and there is remarkable diversity in the impact of SF on tuning for speed. In total, 18.6% of the units showed significant speed tuning whereas 39.8% showed significant SF/TF independence. However, many cells (41.5%) fell along a continuum between speed tuning and SF/TF independence. This diversity has also been noted in primate cortex and may reflect a general property of motion-sensitive systems.
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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 | 0.002 |
| 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 | 0.001 |
| 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".