Electrophysiological responses of audiovisual integration from infancy to adulthood
Bibliographic record
Abstract
Our ability to merge information from different senses into a unified percept is a crucial perceptual process for efficient interaction with our multisensory environment. Yet, the developmental process underlying how the brain implements multisensory integration (MSI) remains poorly known. This cross-sectional study aims to characterize the developmental patterns of audiovisual events in 131 individuals aged from 3 months to 30 years. Electroencephalography (EEG) was recorded during a passive task, including simple auditory, visual, and audiovisual stimuli. In addition to examining age-related variations in MSI responses, we investigated Event-Related Potentials (ERPs) linked with auditory and visual stimulation alone. This was done to depict the typical developmental trajectory of unisensory processing from infancy to adulthood within our sample and to contextualize the maturation effects of MSI in relation to unisensory development. Comparing the neural response to audiovisual stimuli to the sum of the unisensory responses revealed signs of MSI in the ERPs, more specifically between the P2 and N2 components (P2 effect). Furthermore, adult-like MSI responses emerge relatively late in the development, around 8 years old. The automatic integration of simple audiovisual stimuli is a long developmental process that emerges during childhood and continues to mature during adolescence with ERP latencies decreasing with age.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".