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Record W4403175026 · doi:10.1111/desc.13569

Toddlers’ Helping Behavior Is Affected by the Effortful Costs Associated With Helping Others

2024· article· en· W4403175026 on OpenAlexafffundabout
Mia Radovanovic, Hannah Solby, Katie S. Rose, Jeong Yi Hwang, Ece Yucer, Jessica A. Sommerville

Bibliographic record

VenueDevelopmental Science · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoJohn Templeton Foundation
KeywordsPsychologyHelping behaviorTask (project management)Variety (cybernetics)CognitionDevelopmental psychologySocial psychologyHelping handCognitive psychologyApplied psychologyComputer science

Abstract

fetched live from OpenAlex

Although the presence of early helping behavior has been firmly established, it is unclear to what extent children are willing to adopt costs to help others, as well as how this willingness changes as children get older. Canadian 21- to 36-month-olds (N = 48) participated in four helping tasks varying in the type and degree of effort required to help (lifting force, cognitive load, the number of steps in a task, and pushing force). When costs were lower, toddlers were not only more likely to help but also provided help more readily and helped in ways that prioritized others' needs. Importantly, we found that age and how costly helping was to individual children each uniquely predicted high-cost helping, but not low-cost helping. Overall, we demonstrate that toddlers' helping is sensitive to a variety of effortful costs, while simultaneously demonstrating that maturation and individual costs appear to uniquely influence high-cost helping.

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)0.004
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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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

Citations4
Published2024
Admission routes3
Has abstractyes

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