Using an episodic specificity induction to improve children’s future thinking
Bibliographic record
Abstract
Episodic future thinking (EFT) is the ability to subjectively pre-experience a specific future event. Future-oriented cognition in young children positively predicts physical health and financial status later in life. Can EFT be improved in children, even temporarily? Developmental research emphasizes the importance of thinking about one's own near future to enhance EFT, whereas research in adults suggests benefits reside in constructing a richly detailed event. We bridged the two perspectives to examine whether a procedure, the "episodic specificity induction" (ESI), could be adapted to encourage an episodic mode of thinking in children, benefitting performance on a variety of subsequent EFT tasks. The present study implemented a child-friendly ESI in which children mentally simulated a future event and were probed for specific details about it. We randomly assigned 66 children aged 6 and 7 years to one of two conditions: (1) ESI, in which children imagined "having breakfast tomorrow" in detail, describing surroundings, people, and actions, or (2) a Control condition (i.e., no construction), in which children simply viewed and described a picture of another child having breakfast. Children then completed a series of future thinking tasks assessing prospective memory, recollection/imagination of events, delay of gratification, and planning. Our ESI was successful in promoting the construction of a detailed event, and subsequently increasing the number of details of recollected and imagined events on an outcome task as compared to a control condition. Nonetheless, the effect of ESI was smaller than expected - a finding that fits with recent work suggesting that such interventions may be too cognitively taxing for young children and/or that benefits may hinge on further development in episodic processes. We discuss possible modifications to the induction and implications for EFT amelioration in young 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".