Elicitation of the Acoustic Change Complex to Long-Duration Speech Stimuli in Four-Month-Old Infants
Bibliographic record
Abstract
The acoustic change complex (ACC) is an auditory-evoked potential elicited to changes within an ongoing stimulus that indicates discrimination at the level of the auditory cortex. Only a few studies to date have attempted to record ACCs in young infants. The purpose of the present study was to investigate the elicitation of ACCs to long-duration speech stimuli in English-learning 4-month-old infants. ACCs were elicited to consonant contrasts made up of two concatenated speech tokens. The stimuli included native dental-dental /dada/ and dental-labial /daba/ contrasts and a nonnative Hindi dental-retroflex /daDa/ contrast. Each consonant-vowel speech token was 410 ms in duration. Slow cortical responses were recorded to the onset of the stimulus and to the acoustic change from /da/ to either /ba/ or /Da/ within the stimulus with significantly prolonged latencies compared with adults. ACCs were reliably elicited for all stimulus conditions with more robust morphology compared with our previous findings using stimuli that were shorter in duration. The P1 amplitudes elicited to the acoustic change in /daba/ and /daDa/ were significantly larger compared to /dada/ supporting that the brain discriminated between the speech tokens. These findings provide further evidence for the use of ACCs as an index of discrimination ability.
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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 | 0.001 |
| 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".