Is False Belief Skin-Deep? The Agent's Eye Status Influences Infants' Reasoning in Belief-Inducing Situations
Bibliographic record
Abstract
Two experiments were conducted to determine if infants attribute false beliefs to others when tested with the violation-of-expectancy procedure. In Experiment 1, the false-belief task was administered to 14- and 18-month-old infants. The procedure was identical to the one used by Onishi and Baillargeon (2005 Onishi , K. H. , & Baillargeon , R. ( 2005 ). Do 15-month-old infants understand false beliefs? Science , 308 , 255 – 258 . doi: 10.1126/science.1107621 [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), except that two transparent boxes replaced the opaque boxes. Results revealed that infants looked longer when the agent searched in the empty box, a pattern of looking time opposite to the one typically observed with opaque boxes. It is, however, unclear whether infants' behavior was simply guided by their own visual access to the toy's location visible through the transparent box. To test this alternative explanation, Experiment 2 was conducted with 14- and 18-month-old infants. Infants were administered the same modified nonverbal false-belief task with transparent boxes, except that the agent wore a blindfold during the test phase. Contrary to Experiment 1, results revealed that infants were not surprised when the agent searched in either of the two transparent boxes and therefore appeared to treat her as ignorant. These findings demonstrate that infants' psychological reasoning system is more rigid than later forms when predicting an agent's future 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 | 0.002 | 0.018 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 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".