Young children’s causal explanations are biased by post-action associative information.
Bibliographic record
Abstract
In a series of 4 experiments, we tested children's understanding that the causes of their actions must necessarily be attributed to information known prior to (i.e., "pre-action" information), rather than after (i.e., "post-action" information), the completion of their actions. For example, children were shown a dog, asked to get some cheese to feed the dog, and then returned to discover a mouse. In Experiment 1, the majority of 3-, 4-, and 5-year-olds claimed that they had gotten the cheese to feed the mouse. In Experiments 2 and 3, we ruled out the possibilities that (1) children had forgotten the critical "pre-action" information (e.g., "dog"), and (2) children had merely attributed the cause of their action to the most recent item (e.g., "dog") that they had seen. Finally, in Experiment 4, we determined that 7-year-olds, but not 6-year-olds, correctly attributed the cause of their action to the pre-action information, suggesting that this is the age at which children are no longer influenced by associative post-action information when explaining the causes of their actions. These results are discussed in terms of their relevance for causal reasoning, action explanation, and memory.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".