Implicit and explicit responses to infant sounds: A cross-sectional study among parents and non-parents
Bibliographic record
Abstract
This research investigates the infant schema in auditory perception by examining how different demographics, including males, females, parents, and non-parents, respond implicitly and explicitly to baby vocalizations in comparison to adult, cat, and kitten sounds. Utilizing a single category implicit association task (SC-IAT) and a detailed questionnaire, we analyzed participants' responses to synthesized vowel sounds from infants, adults, cats, and kittens. The questionnaire focused on participants' liking and perception of cuteness for these sounds. Findings reveal a universal positive implicit preference for baby vocalizations across all groups (p = 0.01), without a similar effect for other sound sources. In contrast, explicit responses varied significantly. While all groups showed a preference for the sounds of cats, babies, and kittens over adults, only mothers demonstrated a statistically significant explicit preference for infant sounds over those of cats and kittens. This study highlights the discrepancy between unconscious and conscious attitudes towards infant sounds. It underscores that while an implicit affinity for baby vocalizations is widespread, explicit preferences, particularly in terms of cuteness and likeability, are markedly stronger in mothers. These insights contribute to our understanding of the auditory dimension of the infant schema, emphasizing the role of gender and parental status in shaping responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".