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Record W2553252242 · doi:10.1111/cdev.12629

Nonrandom Acts of Kindness: Parasympathetic and Subjective Empathic Responses to Sadness Predict Children's Prosociality

2016· article· en· W2553252242 on OpenAlexafffund
Jonas G. Miller, Jacob N. Nuselovici, Paul D. Hastings

Bibliographic record

VenueChild Development · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsConcordia University
FundersNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsProsocial behaviorEmpathyPsychologySadnessVagal toneDevelopmental psychologyKindnessEmpathic concernFeelingAngerSocial psychologyPerspective-takingInternal medicineAutonomic nervous systemHeart rateMedicine

Abstract

fetched live from OpenAlex

How does empathic physiology unfold as a dynamic process, and which aspect of empathy predicts children's kindness? In response to empathy induction videos, 4- to 6-year-old children (N = 180) showed an average pattern of dynamic respiratory sinus arrhythmia (RSA) change characterized by early RSA suppression, followed by RSA recovery, and modest subsequent suppression during positive resolution of the empathic event. Children's capacity for this pattern of flexible RSA change was associated with their subjective empathic feelings, which were concurrently associated with more sympathetic and prosocial responses to others. Conversely, only children's dynamic RSA change longitudinally predicted prosocial behavior 2 years later. These findings have implications for understanding the dynamic and multifaceted nature of empathy, and its relation with prosocial development.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.279
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2016
Admission routes2
Has abstractyes

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