Speech Perception by 6‐to 8‐Month‐Olds in the Presence of Distracting Sounds
Bibliographic record
Abstract
The role of selective attention in infant phonetic perception was examined using a distraction masker paradigm. We compared perception of /bu/ versus /gu/ in 6‐ to 8‐month‐olds using a visual fixation procedure. Infants were habituated to multiple natural productions of 1 syllable type and then presented 4 test trials (old‐new‐old‐new). Perception of the new syllable (indexed as novelty preference) was compared across 3 groups: habituated and tested on syllables in quiet (Group 1), habituated and tested on syllables mixed with a nonspeech signal (Group 2), and habituated with syllables mixed with a non‐speech signal and tested on syllables in quiet (Group 3). In Groups 2 and 3, each syllable was mixed with a segment spliced from a recording of bird and cricket songs. This nonspeech signal has no overlapping frequencies with the syllable; it is not expected to alter the sensory structure or perceptual coherence of the syllable. Perception was negatively affected by the presence of the auditory distracter during habituation; individual performance levels also varied more in these groups. The findings show that perceiving speech in the presence of irrelevant sounds poses a cognitive challenge for young infants. We conclude that selective attention is an important skill that supports speech perception in infants; the significance of this skill for language learning during infancy deserves investigation.
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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) | 0.001 | 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 | 0.001 |
| 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".