Labeling Guides Object Individuation in 12-Month-Old Infants
Bibliographic record
Abstract
A new manual search method was used to investigate the impact of naming on object individuation in 12-month-old infants. In Experiment 1, on a two-word trial, an experimenter looked into a box while the infant was watching and provided two labels (e.g., "Look, a fep!" and "Look, a wug!"). On a one-word trial, the experimenter instead repeated the same label (e.g., "Look, a zav!"). After the infant retrieved one object from the box, subsequent search behavior was recorded. Infants searched more persistently (i.e., for a longer duration) after hearing two labels than one, suggesting that hearing two labels led the infants to expect two objects inside the box. In Experiment 2, infants' search behavior did not differ depending on whether they heard one or two emotional expressions, suggesting that the facilitation effect observed in Experiment 1 may be specific to linguistic expressions. Thus, we provide the first evidence that infants as young as 12 months are able to use intentional and referential cues to guide their object representations. These findings also suggest that a rudimentary version of the mutual-exclusivity constraint may be functional by the end of the first year.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".