Able and willing: Infants selectively seek help from competent and benevolent others.
Bibliographic record
Abstract
Young children often encounter unsolvable problems with which they require others' help. To receive adequate assistance, children must be savvy about whom they seek help from: Effective helpers must possess both the ability to help (e.g., competence) and a willingness to do so (e.g., benevolence). Although past work suggests that information about competence and benevolence can inform young children's help-seeking behavior, it remains unclear how and whether children utilize said factors independently of each other. Furthermore, it is unclear whether they can generalize potential helpers' competence from one task to another. The current experiments examined whether 22- to 23-month-olds confronted with a broken toy selectively sought help from agents who had previously demonstrated either competence (Experiment 1) or benevolence (Experiment 2). In Experiment 1, infants preferred to seek help from a competent agent who successfully opened a closed box over one who failed to do so. In Experiment 2, infants selectively sought help from a benevolent agent who helped a third party by returning a lost ball, over an agent who stole the ball instead. These patterns of selectivity were not driven by associative valence matching; in Experiment 3, infants showed no preference for an agent who was itself helped versus an agent who was hindered. These results suggest that before their second birthday, infants independently utilize cues to both competence and benevolence to inform their help seeking, using information generalized from novel contexts. We discuss the potential nature of this generalization as well as directions for future work. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".