Young Children Use Conversational Timing as a Cue for Prosocial Commitment
Bibliographic record
Abstract
This study explores the emergence of these abilities in young children (104 Chinese children, 41-77 months, 44 female). Children watched cartoon vignettes in which a cartoon character requested help from two friends whose respective responses followed either a short (600 ms) or long (4000 ms) delay.After the main task, participants completed the ITI discrimination task in which a squirrel asked two animals questions (e.g., “What’s your favorite color/food”) and each animal responded with a different ITI (600 ms vs. 4000 ms). Participants indicated which animal answered faster. Next, children completed the MBEMA rhythm test, followed by the false location and false content tasks. After completing the session, each child received an eraser as a gift. On the same day, caregivers were asked to complete the ToMI questionnaire online.This study used RStudio (2025.09.2+418, macOS version) for data processing and analysis, running all statistical procedures within the R language environment. Two datasets were required for the analysis: a long-format dataset and a wide-format dataset. Before running the code, please update the file paths in the first two read.csv() commands according to the actual location of the data files to ensure that R can correctly load the datasets.Variable DescriptionsgroupBetween-subject grouping variable used to balance participants across experimental conditions.classKindergarten class level of each participant: 1 = Junior Class, 2 = Middle Class, 3 = Senior ClassgenderParticipant’s self-reported gender.IDUnique participant identification number.tomiMean score on the Theory of Mind Inventory (ToMI) questionnaire.ageParticipant’s months converted to years.commit_1 – commit_4Scores for each trial (Trial 1–4) in the Commitment Judgment Task (commitment judgment).pre_1 – pre_4Scores for each trial (Trial 1–4) in the Commitment Judgment Task for (Preference).discrimination_1 – discrimination_4Scores for each trial (Trial 1–4) in the ITI Discrimination Task.rhythmTotal score on the MBEMA Rhythm Test.commitment_totalSum of the four trials in the Commitment Judgment Task(commit_1 + commit_2 + commit_3 + commit_4)discrimination_totalSum of the four trials in the ITI Discrimination Task(discrimination_1 + discrimination_2 + discrimination_3 + discrimination_4)locationScore on the False-Belief (Location Change) Task.contentScore on the False-Belief (Unexpected Content) Task.false_beliefTotal false-belief score, calculated as the sum of location + content.preference_totalSum of the four trials in the Preference Task(pre_1 + pre_2 + pre_3 + pre_4)monthParticipant’s age in years.fosterparticipants’ fosters who finished the ToMI: 1=mother, 2=father.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".