The Ventral Posterior Lateral Thalamus Preferentially Encodes Externally Applied Versus Active Movement: Implications for Self-Motion Perception
Bibliographic record
Abstract
Successful interaction with our environment requires that voluntary behaviors be precisely coordinated with our perception of self-motion. The vestibular sensors in the inner ear detect self-motion and in turn send projections via the vestibular nuclei to multiple cortical areas through 2 principal thalamocortical pathways, 1 anterior and 1 posterior. While the anterior pathway has been extensively studied, the role of the posterior pathway is not well understood. Accordingly, here we recorded responses from individual neurons in the ventral posterior lateral thalamus of macaque monkeys during externally applied (passive) and actively generated self-motion. The sensory responses of neurons that robustly encoded passive rotations and translations were canceled during comparable voluntary movement (~80% reduction). Moreover, when both passive and active self-motion were experienced simultaneously, neurons selectively encoded the detailed time course of the passive component. To examine the mechanism underlying the selective elimination of vestibular sensitivity to active motion, we experimentally controlled correspondence between intended and actual head movement. We found that suppression only occurred if the actual sensory consequences of motion matched the motor-based expectation. Together, our findings demonstrate that the posterior thalamocortical vestibular pathway selectively encodes unexpected motion, thereby providing a neural correlate for ensuring perceptual stability during active versus externally generated motion.
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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".