Motion parallax processing in pigeon (<i>Columba livia</i>) pretectal neurons
Bibliographic record
Abstract
In the visual system of invertebrates and vertebrates there are specialised groups of motion-sensitive neurons, with large receptive fields, which are optimally tuned to respond to optic flow produced by the animals' movement through the 3-D world. From their response characteristics, shared frame of reference with the vestibular or inertial system, and anatomical connections, these neurons have been implicated in the stabilisation of retinal images, the control of posture and balance, and the animal's motion trajectories through the world. Using standard electrophysiological techniques and computer-generated stimuli, we show that some of these flow-field neurons in the pretectal nucleus lentiformis mesencephali in pigeons appear to be processing motion parallax. Two large overlapping planes of random dots moving independently were used to simulate motion parallax, in which one with larger dots was moved fast and the other with smaller dots was moved slowly in the opposite direction. Their neural responses to these two superimposed planes were facilitated above those produced by a single plane of moving dots and those produced by two layers moving in the same direction. Furthermore, some of these neurons preferred backward motion in the visual field and others preferred forward motion, suggesting that they may separately code visual objects 'nearer' and 'farther' than the stabilised ('on') plane during forward translational motion. A simple system is proposed whereby the relative activity in 'near', 'far' and 'on' populations could code depth through motion parallax in a metameric manner similar to that employed to code color vision and stereopsis.
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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 | 0.000 |
| 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".