What Almost Happened? Using Close-Counterfactuals to Prime a Simulation Mindset in Children
Bibliographic record
Abstract
Counterfactual reasoning, the ability to reason about how events could have turned out differently, helps individuals understand the causes of events and prepare for the future. The simulation mindset hypothesis posits that exposure to counterfactual scenarios stimulates the generation of imaginary alternatives, enhancing planning, problem-solving, and behaviour adjustment. This study investigated whether close-counterfactual scenarios prime a simulation mindset in children leading to better problem-solving abilities. Ninety six- and eight-year-olds were assigned to either a counterfactual condition, with storybooks featuring close-counterfactual events, or a control condition, with storybooks describing factual events. Participants then completed two problem-solving tasks requiring the generation of alternative solutions. Results showed that 8-year-olds exhibited better problem-solving abilities than 6-year-olds. Counterfactual scenarios did not significantly affect older children's problem-solving skills, however they showed benefits for the younger children. These findings provide emerging evidence that engaging in counterfactual reasoning can enhance divergent thinking and problem-solving skills in children.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".