Hummingbirds use compensatory eye movements to stabilize both rotational and translational visual motion
Bibliographic record
Abstract
To maintain stable vision, behaving animals make compensatory eye movements in response to image slip, a reflex known as the optokinetic response (OKR). Although OKR has been studied in several avian species, eye movements during flight are expected to be minimal. This is because vertebrates with laterally placed eyes typically show weak OKR to nasal-to-temporal motion (NT), which simulates typical forward locomotion, compared with temporal-to-nasal motion (TN), which simulates atypical backward locomotion. This OKR asymmetry is also reflected in the pretectum, wherein neurons sensitive to global visual motion also exhibit a TN bias. Hummingbirds, however, stabilize visual motion in all directions through whole-body movements and are unique among vertebrates in that they lack a pretectal bias. We therefore predicted that OKR in hummingbirds would be symmetrical. We measured OKR in restrained hummingbirds by presenting gratings drifting across a range of speeds. OKR in hummingbirds was asymmetrical, although the direction of asymmetry varied with stimulus speed. Hummingbirds moved their eyes largely independently of one another. Consistent with weak eye-to-eye coupling, hummingbirds also exhibited disjunctive OKR to visual motion simulating forward and backward translation. This unexpected oculomotor behaviour, previously unexplored in birds, suggests a potential role for compensatory eye movements during flight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".