Baby don't cry : measuring the empathetic response towards infant cries in a Singaporean nonparent context
Bibliographic record
Abstract
Females and feminine gender roles are considered to be more empathetic according to \nsocietal stereotypes and experimental studies. Therefore, they are expected to respond more \nempathetically in difficult situations, such as when one hears an infant crying. Crying, which \nserves as the primary means of communication for infants, is often perceived as aversive, \nthereby activating empathy-related brain areas. However, few neuroimaging studies that \nexamine the empathetic response towards infant cries, or the effects of gender roles on such \nan empathetic response have been conducted. This study aims to (1) examine the empathetic \nresponses towards infant cries of different intensities; (2) investigate sex differences in \nempathetic responses toward infant cries; and (3) assess whether individuals’ empathetic \nresponses are moderated by the gender roles they are classified as using the Bem Sex-Role \nInventory (BSRI). In this study, the Toronto Empathy Questionnaire (TEQ) and functional \nnear-infrared spectroscopy (fNIRS) were used to measure the nonparent participants’ (n = 38) \ntrait empathy and empathetic response towards mild and intense infant cries \nrespectively. Results showed that a higher empathetic response was elicited for mild cry \nintensities in the medial prefrontal cortex (mPFC), a core neural processing region of \nempathy. Nonsignificant sex differences in empathetic response was observed in the mPFC. \nMasculinity, not femininity, was correlated with an empathetic response in the mPFC in \nresponse to intense but not mild infant cries. Overall, our results suggest the existence of \ndiffering empathetic responses towards mild and intense infant vocalizations, with gender \nroles moderating the response.
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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 | 0.001 |
| 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) | 0.002 | 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".