Bayesian integration of visual and vestibular signals for heading
Bibliographic record
Abstract
Self-motion through an environment involves a composite of signals such as visual and vestibular cues. Building upon previous results showing that visual and vestibular signals combine in a statistically optimal fashion, we investigated the relative weights of visual and vestibular cues during self-motion. This experiment was comprised of three experimental conditions: vestibular alone, visual alone (with four different standard heading values), and visual-vestibular combined. In the combined cue condition, inter-sensory conflicts were introduced (Δ = ±6° or ±10°). Participants performed a 2-interval forced choice task in all conditions and were asked to judge in which of the two intervals they moved more to the right. The cue-conflict condition revealed the relative weights associated with each modality. We found that even when there was a relatively large conflict between the visual and vestibular cues, participants exhibited a statistically optimal reduction in variance. On the other hand, we found that the pattern of results in the unimodal conditions did not predict the weights in the combined cue condition. Specifically, visual-vestibular cue combination was not predicted solely by the reliability of each cue, but rather more weight was given to the vestibular cue.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".