Tactile Flow Overrides Other Cues To Self Motion
Bibliographic record
Abstract
Vestibular-somatosensory interactions are pervasive in the brain but it remains unclear why. Here we explore the contribution of tactile flow to processing self-motion. We assessed two aspects of self-motion: timing and speed. Participants sat on an oscillating swing and either kept their hands on their laps or rested them lightly on an earth-stationary surface. They viewed a grating oscillating at the same frequency as their motion and judged its phase or, in a separate experiment, its speed relative to their perceived motion. Participants required the phase to precede body movement (with or without tactile flow) or tactile flow by ~5° (44 ms) to appear earth-stationary. Speed judgments were 4-10% faster when motion was from tactile flow, either alone or with body motion, compared to body motion alone (where speed judgments were accurate). By comparing response variances we conclude that phase and speed judgments do not reflect optimal integration of tactile flow with other cues to body motion: instead tactile flow dominates perceived self-motion - acting as an emergency override. This may explain why even minimal tactile cues are so helpful in promoting stability and suggests that providing artificial tactile cues might be a powerful aid to perceiving self-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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".