Frequency tagging evidence supports perceptual separation of rapid stimuli in human fetuses
Bibliographic record
Abstract
Abstract In early human development, perceptual processes grow faster with maturation, as inferred using the duration of the attentional blink and multisensory integration window. The consequences of this developmental trend for sensory-cognition in fetuses are unclear: does the fetus perceive rapid stimuli as discrete events or, rather, one fused stimulus? We addressed this question using frequency tagging in two experiments with rapid auditory stimuli while neural responses were recorded in the third trimester with fetal magnetoencephalography (MEG). Our results are the first successful demonstration of frequency tagging in the fetal MEG amplitude spectrum and show that the fetal cortex generates separate neural responses to discrete auditory stimuli in both experiments, and similar results were also obtained when one experiment was repeated in newborns. While we cannot rule out perceptual fusion of rapid stimuli in higher-order association cortices, our results weaken the hypothesis that fetuses fuse rapid auditory stimuli into a single prolonged percept. Finally, our work points to frequency tagging analysis as a solution which avoids the uncertainties surrounding immature neural response latencies in time-domain analysis of fetal MEG.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".