Early vestibular processing does not discriminate active from passive self-motion if there is a discrepancy between predicted and actual proprioceptive feedback
Bibliographic record
Abstract
Most of our sensory experiences are gained by active exploration of the world. While the ability to distinguish sensory inputs resulting of our own actions (termed reafference) from those produced externally (termed exafference) is well established, the neural mechanisms underlying this distinction are not fully understood. We have previously proposed that vestibular signals arising from self-generated movements are inhibited by a mechanism that compares the internal prediction of the proprioceptive consequences of self-motion to the actual feedback. Here we directly tested this proposal by recording from single neurons in monkey during vestibular stimulation that was externally produced and/or self-generated. We show for the first time that vestibular reafference is equivalently canceled for self-generated sensory stimulation produced by activation of the neck musculature (head-on-body motion), or axial musculature (combined head and body motion), when there is no discrepancy between the predicted and actual proprioceptive consequences of self-motion. However, if a discrepancy does exist, central vestibular neurons no longer preferentially encode vestibular exafference. Specifically, when simultaneous active and passive motion resulted in activation of the same muscle proprioceptors, neurons robustly encoded the total vestibular input (i.e., responses to vestibular reafference and exafference were equally strong), rather than exafference alone. Taken together, our results show that the cancellation of vestibular reafference in early vestibular processing requires an explicit match between expected and actual proprioceptive feedback. We propose that this vital neuronal computation, necessary for both accurate sensory perception and motor control, has important implications for a variety of sensory systems that suppress self-generated signals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".