Vestibular Information Is Required for Dead Reckoning in the Rat
Bibliographic record
Abstract
Dead reckoning is an on-line form of spatial navigation used by an animal to identify its present location and return directly to a starting location, even after circuitous outward trips. At present, it is not known which of several self-movement cues (efferent copy from movement commands, proprioceptive information, sensory flow, or vestibular information) are used to compute homeward trajectories. To determine whether vestibular information is important for dead reckoning, the impact of chemical labyrinthectomy was evaluated in a test that demanded on-line computation of a homeward trajectory. Rats were habituated to leave a refuge that was visible from all locations on a circular table to forage for large food pellets, which they carried back to the refuge to eat. Two different probe trials were given: (1) the rats foraged from the same spatial location from a hidden refuge in the light and so were able to use visual cues to navigate; (2) the same procedure took place in the dark, constraining the animals to dead reckon. Although control rats carried food directly and rapidly back to the refuge on both probes, the rats with vestibular lesions were able to do so on the hidden refuge but not on the dark probe. The scores of vestibular reflex tests predicted the dead reckoning deficit. The vestibular animals were also impaired in learning a new piloting task. This is the first unambiguous demonstration that vestibular information is used in dead reckoning and also contributes to piloting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".