Development of infant mismatch responses to auditory pattern changes between 2 and 4 months old
Bibliographic record
Abstract
In order to process speech and music, the auditory cortex must learn to process patterns of sounds. Our previous studies showed that with a stream consisting of a repeating (standard) sound, younger infants show an increase in the amplitude of a positive slow wave in response to occasional changes (deviants) in pitch or duration, whereas older infants show a faster negative response that resembles mismatch negativity (MMN) in adults (Trainor et al., 2001, 2003; He et al., 2007). MMN reflects an automatic change-detection process that does not require attention, conscious awareness or behavioural response for its elicitation (Picton et al., 2000; Näätänen et al., 2007). It is an important tool for understanding auditory perception because MMN reflects a change-detection mechanism, and not simply that repetition of a stimulus results in a refractory state of sensory neural circuits while occasional changes to a new sound activate new non-refractory neural circuits (Näätänen et al., 2005). For example, MMN is elicited by a change in the pattern of a repeating note sequence, even when no new notes are introduced that could activate new sensory circuits (Alain et al., 1994, 1999;Schröger et al., 1996). In the present study, we show that in response to a change in the pattern of two repeating tones, MMN in 4-month-olds remains robust whereas the 2-month-old response does not. This indicates that the MMN response to a change in pattern at 4 months reflects the activation of a change-detection mechanism similarly as in adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".