Young children infer feelings of ownership from habitual use.
Bibliographic record
Abstract
People sometimes feel as if they own items that do not actually belong to them. These feelings of ownership affect people in diverse contexts and provide a striking example of how feelings can conflict with reality. Across 6 experiments, we investigated young children's (N = 614) and adults' (N = 243) understanding of these feelings. In Experiment 1, children aged 4 to 7 inferred that an agent who habitually used a publicly owned item would have feelings of ownership for it, and children distinguished these feelings from actual ownership. Experiments 2 and 3 replicated these findings and also found that children were less likely to attribute feelings of ownership when the agent used the item nonhabitually. Experiments 4 and 5 further found that children and adults also distinguish feelings of ownership from false beliefs of ownership. Finally, in Experiment 6, even younger children showed signs of understanding feelings of ownership. Children aged 3 and 4 predicted that an agent who habitually used an item would be upset to discover someone else using it. Together, these findings suggest that young children are aware of the psychological component of ownership. The findings are also informative about their understanding of habits and repeated actions and the potential for feelings to conflict with beliefs, knowledge, and reality. (PsycInfo Database Record (c) 2021 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 | 0.001 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".