A Causal Role of Area hMST for Self-Motion Perception in Humans
Bibliographic record
Abstract
Previous studies in the macaque monkey have provided clear causal evidence for an involvement of the medial-superior-temporal area (MST) in the perception of self-motion. These studies also revealed an overrepresentation of contraversive heading. Human imaging studies have identified a functional equivalent (hMST) of macaque area MST. Yet, causal evidence of hMST in heading perception is lacking. We employed neuronavigated transcranial magnetic stimulation (TMS) to test for such a causal relationship. We expected TMS over hMST to induce increased perceptual variance (i.e., impaired precision), while leaving mean heading perception (accuracy) unaffected. We presented 8 human participants with an optic flow stimulus simulating forward self-motion across a ground plane in one of 3 directions. Participants indicated perceived heading. In 57% of the trials, TMS pulses were applied, temporally centered on self-motion onset. TMS stimulation site was either right-hemisphere hMST, identified by a functional magnetic resonance imaging (fMRI) localizer, or a control-area, just outside the fMRI localizer activation. As predicted, TMS over area hMST, but not over the control-area, increased response variance of perceived heading as compared with noTMS stimulation trials. As hypothesized, this effect was strongest for contraversive self-motion. These data provide a first causal evidence for a critical role of hMST in visually guided navigation.
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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 | 0.002 |
| 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) | 0.003 | 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".