Three-year-olds' difficulty with the appearance–reality distinction: Is it real or is it apparent?
Bibliographic record
Abstract
Four experiments investigated 3-year-olds' understanding of the appearance-reality distinction using both J. Flavell, F. Green, and J. Flavell's (1986) typical verbal response paradigm and a new, nonverbal response paradigm. Both paradigms require verbal questioning, but the former involves a verbal response and the latter a nonverbal one. In the nonverbal paradigm, children were shown a deceptive object and asked to respond, nonverbally, to 2 different functional requests, 1 concerning the object's apparent property and 1 its real property. In the verbal paradigm, children were asked to state what the object looked like and what it really was. In the verbal paradigm, children were about 30% correct (a rate matching that in the literature), whereas over 90% of the same children were correct in the nonverbal paradigm. Participating in the verbal paradigm first had a detrimental effect on the children's performance in the nonverbal paradigm, but the reverse order had no effect. These results suggest that 3-year-olds can represent two conflicting properties of a deceptive object and thus understand the appearance-reality distinction in the nonverbal domain.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".