Spatiotemporal stimulus properties modulate responses to trajectory changes in a locust looming-sensitive pathway
Bibliographic record
Abstract
The lobula giant movement detector (LGMD) and descending contralateral movement detector (DCMD) constitute one motion-sensitive pathway in the locust visual system that is implicated in collision-avoidance behaviors. While this pathway is thought to respond preferentially to objects approaching on a direct collision course, emerging studies suggest the firing rate is able to monitor more complicated movements that would occur under natural conditions. While previous studies have compared the response of the DCMD to objects on collision courses that travel at different speeds, velocity has not been manipulated for other simple or compound trajectories. Here we test the possibility that the LGMD/DCMD pathway is capable of responding uniquely to complex aspects of object motion, including translation and trajectory changes at different velocities. We found that the response of the DCMD to translational motion initiated in the caudal visual field was a low-amplitude peak in firing rate that occurred before the object crossed 90° azimuth that was invariant to different object velocities. Direct looms at different velocities resulted in peak firing rates that occurred later in time and with greater amplitude for higher velocities. In response to transitions from translational motion to a collision course, the firing rate change depended on both the location within the visual field and the velocity. These results suggest that this pathway is capable of conveying information about multiple properties of a moving object's trajectory.
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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 | 0.000 | 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".