Protocol for exploring the relationship between sound localization and the representation of the body in space
Bibliographic record
Abstract
The influence of multisensory integration on spatial hearing has received more attention in recent years. Notably, incongruent sensory inputs can bias auditory spatial processing. Here, we present a protocol for producing an illusory shift in the localization of a sound source by inducing an unconscious shift in the representation of the body in space. We describe steps for screening participants and evaluating vestibular and hearing abilities. We then detail procedures for performing auditory localization tasks both with and without disorientation. For complete details on the use and execution of this protocol, please refer to Paromov et al. 1 • Steps for evaluating auditory localization during body representation perturbations • Technique to elicit errors in auditory spatial processing • Approach for a behavioral assessment of auditory spatial cognition Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The influence of multisensory integration on spatial hearing has received more attention in recent years. Notably, incongruent sensory inputs can bias auditory spatial processing. Here, we present a protocol for producing an illusory shift in the localization of a sound source by inducing an unconscious shift in the representation of the body in space. We describe steps for screening participants and evaluating vestibular and hearing abilities. We then detail procedures for performing auditory localization tasks both with and without disorientation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.018 |
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".