A looming-sensitive pathway responds to changes in the trajectory of object motion
Bibliographic record
Abstract
Two identified locust neurons, the lobula giant movement detector (LGMD) and its postsynaptic partner, the descending contralateral movement detector (DCMD), constitute one motion-sensitive pathway in the visual system that responds preferentially to objects that approach on a direct collision course and are implicated in collision-avoidance behavior. Previously described responses to the approach of paired objects and approaches at different time intervals (Guest BB, Gray JR. J Neurophysiol 95: 1428-1441, 2006) suggest that this pathway may also be affected by more complicated movements in the locust's visual environment. To test this possibility we presented stationary locusts with disks traveling along combinations of colliding (looming), noncolliding (translatory), and near-miss trajectories. Distinctly different responses to different trajectories and trajectory changes demonstrate that DCMD responds to complex aspects of local visual motion. DCMD peak firing rates associated with the time of collision remained relatively invariant after a trajectory change from translation to looming. Translatory motion initiated in the frontal visual field generated a larger peak firing rate relative to object motion initiated in the posterior visual field, and the peak varied with simulated distance from the eye. Transition from translation to looming produced a transient decrease in the firing rate, whereas transition away from looming produced a transient increase. The change in firing rate at the time of transition was strongly correlated with unique expansion parameters described by the instantaneous angular acceleration of the leading edge and subtense angle of the disk. However, response time remained invariant. While these results may reflect low spatial resolution of the compound eye, they also suggest that this motion-sensitive pathway may be capable of monitoring dynamic expansion properties of objects that change the trajectory of motion.
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 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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".