Infants' understanding of relational goals
Bibliographic record
Abstract
Infants understand others' goals and use them to predict others' actions. Do 9.5-month-olds understand that others can act on the basis of goals not tied to specific objects? Particularly, do infants understand that a person's goal could be to select either the bigger (smaller) of two objects, a goal based on object relations? Across 4 familiarization trials, each with a different pair of objects differing only in size, an Experimenter selected either the bigger (Big object condition) or smaller (Small object condition) of two objects. In test trials with new objects, in the Big object condition, infants looked longer when the Experimenter selected the smaller than the bigger object, but in the Small object condition they looked about equally at the two test events (Experiment 1). Conditions under which the goal of smaller could be understood were further explored. Infants provided with additional information about the Experimenter's goal still looked equally at the two test events (Experiment 2), while those encouraged to compare object size both within and between pairs, looked longer when the Experimenter selected the bigger object than the smaller object (Experiment 3). 9.5-month-olds seem to understand that a person's goal can be to select either the bigger or smaller of two objects. The goal of smaller seems to be more difficult, perhaps due to infants' own preference for larger quantities or because their understanding of the size concept of small. The results suggested that infants' understanding of size relational goals involves the comprehension of the relational category of the object (big or small) and the ability to use that information to make sense of others' actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".