Children (and many adults) use perceptual similarity to assess relative impossibility.
Bibliographic record
Abstract
People see some impossible events as more impossible than others. For example, walking through a solid wall seems more impossible if it is made of stone rather than wood. Across four experiments, we investigated how children and adults assess the relative impossibility of events, contrasting two kinds of information they may use: perceptual information and causal knowledge. In each experiment, participants were told about a wizard who could magically transform target objects into other things. Participants then assessed which of the two transformation spells would be easier or harder, a spell transforming a target object into a perceptual match (i.e., a similar-looking thing) or one transforming it into a causal match (e.g., an item made of similar materials). In Experiments 1-3, children aged 4-7 mainly thought that transformations into the perceptual match would be easier, though this tendency varied with age. Adults were overall split when choosing which spell would be easier. In Experiment 1, this was because of variations in their judgments across different pairs of spells; in Experiments 2 and 4, the split resulted because different subsets of adults preferred either the perceptual or causal match. Overall, these findings show that children, and many adults, use perceptual reasoning to assess relative impossibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".