Fast and persistent adaptation to new spectral cues for sound localization suggests a many-to-one mapping mechanism
Bibliographic record
Abstract
The adult human auditory system can adapt to changes in spectral cues for sound localization. This plasticity was demonstrated by changing the shape of the pinna with earmolds. Previous results indicate that participants regain localization accuracy after several weeks of adaptation and that the adapted state is retained for at least one week without earmolds. No aftereffect was observed after mold removal, but any aftereffect may be too short to be observed when responses are averaged over many trials. This work investigated the lack of aftereffect by analyzing single-trial responses and modifying visual, auditory, and tactile information during the localization task. Results showed that participants localized accurately immediately after mold removal, even at the first stimulus presentation. Knowledge of the stimulus spectrum, tactile information about the absence of the earmolds, and visual feedback were not necessary to localize accurately after adaptation. Part of the adaptation persisted for one month without molds. The results are consistent with the hypothesis of a many-to-one mapping of the spectral cues, in which several spectral profiles are simultaneously associated with one sound location. Additionally, participants with acoustically more informative spectral cues localized sounds more accurately, and larger acoustical disturbances by the molds reduced adaptation success.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".