Do counterfactual scenarios prime a mental simulation mindset in children?
Bibliographic record
Abstract
Counterfactual reasoning—the process of considering alternative possibilities—is a cognitive tool that helps individuals understand experiences and adjust behaviour for better outcomes. The simulation mindset hypothesis posits that exposure to counterfactual scenarios stimulates the generation of additional imaginary alternatives, enhancing planning, problem-solving, and behaviour adjustment. This study investigated whether counterfactual storybooks could prime a simulation mindset in children and how this might influence their problem-solving abilities. Ninety six- and eight-year-olds were assigned to either a counterfactual condition, with storybooks featuring close counterfactual events, or a factual condition, with storybooks describing factual events. Participants then completed two problem-solving tasks requiring the generation of alternative solutions. Results revealed that 8-year-olds exhibited better problem-solving abilities than 6-year-olds. Although counterfactual scenarios did not significantly affect older children's problem-solving skills, they showed potential benefits for the younger children. These findings provide valuable insights into using counterfactual storybooks to enhance divergent thinking and problem-solving skills in children.
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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".