The development of audio–visual temporal precision precedes its rapid recalibration
Bibliographic record
Abstract
Through development, multisensory systems reach a balance between stability and flexibility: the systems integrate optimally cross-modal signals from the same events, while remaining adaptive to environmental changes. Is continuous intersensory recalibration required to shape optimal integration mechanisms, or does multisensory integration develop prior to recalibration? Here, we examined the development of multisensory integration and rapid recalibration in the temporal domain by re-analyzing published datasets for audio-visual, audio-tactile, and visual-tactile combinations. Results showed that children reach an adult level of precision in audio-visual simultaneity perception and show the first sign of rapid recalibration at 9 years of age. In contrast, there was very weak rapid recalibration for other cross-modal combinations at all ages, even when adult levels of temporal precision had developed. Thus, the development of audio-visual rapid recalibration appears to require the maturation of temporal precision. It may serve to accommodate distance-dependent travel time differences between light and sound.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".