Infants’ reasoning about samples generated by intentional versus non‐intentional agents
Bibliographic record
Abstract
The current experiments investigate how infants use goal-directed action to reason about intentionally sampled outcomes in a probabilistic inference paradigm. Older infants and young children are flexible in their expectations of sampling: They expect random samples to reflect population statistics and non-random samples to reflect an agent's preferences or goals (Kushnir, Xu, & Wellman, 2010; Xu & Denison, 2009). However, more recent work shows that probabilistic inference comes online at approximately 6 months (Denison, Reed, & Xu, 2013; Kayhan, Gredebäck, & Lindskog, 2017; Ma & Xu, 2011; Wellman, Kushnir, Xu, & Brink, 2016), and thus, these sampling assumptions can be investigated at the age probabilistic reasoning first emerges. Results indicate that 6-month-old infants expect a human agent to sample in accord with their goal and do not expect the same of an unintentional agent-a mechanical claw. By 9.5 months, infants expect the mechanical claw to sample in accord with random sampling. These results suggest that infants use goals to make inferences about intentional sampling, under appropriate conditions at 6 months, and they have expectations of the kinds of samples a mechanical device should obtain by 9.5 months.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".