The role of empathy in children's costly prosocial lie‐telling behaviour
Bibliographic record
Abstract
Abstract The aim of the present study was to examine the role of induced empathy and parent‐reported empathy (i.e., affective and cognitive) as underlying motives for children's prosocial lie‐telling tendencies. An experimental paradigm was used to elicit prosocial lies in children ( N = 146, 7–11 years) in varying cost (low‐cost/high‐cost) and induction (empathy/neutral) conditions. Results indicate that induced empathy predicts prosocial lie likelihood and maintenance in low‐cost conditions, and that cognitive empathy is a predictor of lie‐likelihood. Post‐hoc analyses revealed that a large portion of children chose to prosocially share with the distressed confederate, regardless of whether they lied for them. Individuals who shared were more likely to share in low‐cost conditions, and also had higher cognitive empathy. Overall, this study provides unique insights into the role of empathy as an underlying cognitive process for children's prosocial decision‐making. Highlights The role of empathy was examined in relation to children's prosocial lying and sharing behaviour in low‐ and high‐cost conditions. Parent‐reported cognitive empathy predicted both lying and sharing in an experimental paradigm; induced empathy only predicted lying in low‐cost conditions. Overall, empathy proved to be an important underlying motive for children's prosocial decision‐making.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".