Tomorrow will be different: Children’s ability to incorporate an intervening event when thinking about the future.
Bibliographic record
Abstract
Future-oriented thought is ubiquitous in humans but challenging to study in children. Adults not only think about the future but can also represent a future state of the world that differs from the present. However, behavioral tasks to assess the development of future thought have not traditionally required children to do so as most can be solved based solely on representations of the present. To overcome this limitation, we modified an existing task such that children could not simply rely on a representation of the present to succeed (i.e., the correct answer for "right now" was different than the correct answer for "tomorrow"). A sample of 117 4- to 7-year-olds (64 girls and 53 boys) from Ottawa, Canada, and surrounding area, who were predominantly European Canadian (78.6% of sample) and had a family income of over $100,000 CAN (66.1% of sample) participated. Children remembered the information required to solve our task, and there were age-related changes in performance, but only 7-year-olds made an adaptive future-oriented decision significantly more often than chance. With the task modification removed (so the correct answer for the present and the future was the same), even 4-year-olds were above chance. Our work challenges the notion that starting at age 4, children solve behavioral tasks of future thinking by acting on their representations of the future. (PsycInfo Database Record (c) 2021 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".