The object of my desire: Five-year-olds rapidly reason about a speaker’s desire during referential communication
Bibliographic record
Abstract
Two experiments examined whether 5-year-olds draw inferences about desire outcomes that constrain their online interpretation of an utterance. Children were informed of a speaker's positive (Experiment 1) or negative (Experiment 2) desire to receive a specific toy as a gift before hearing a referentially ambiguous statement ("That's my present") spoken with either a happy or sad voice. After hearing the speaker express a positive desire, children (N=24) showed an implicit (i.e., eye gaze) and explicit ability to predict reference to the desired object when the speaker sounded happy, but they showed only implicit consideration of the alternate object when the speaker sounded sad. After hearing the speaker express a negative desire, children (N=24) used only happy prosodic cues to predict the intended referent of the statement. Taken together, the findings indicate that the efficiency with which 5-year-olds integrate desire reasoning with language processing depends on the emotional valence of the speaker's voice but not on the type of desire representations (i.e., positive vs. negative) that children must reason about online.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".