A unified account of why optimism declines in childhood
Bibliographic record
Abstract
Optimism is linked to a range of positive social and cognitive outcomes across development. Yet decades of work in psychological science has revealed that optimism declines throughout early childhood. Despite this well-documented decline, there is no agreed-upon theory that accounts for developmental changes in optimism. In this Perspective, we synthesize cognitive, computational, social and neural evidence and discuss three candidate mechanisms that might underlie declines in optimism with age: learning from experience, changing theories of success and wishful thinking, and shifts in valenced learning biases. We argue that declining optimism across childhood is best characterized by an account that integrates these theories. Specifically, we suggest that environmental factors impact the pace at which children’s theories and valenced learning biases change with age, and consequently the rate at which their optimism declines. This account suggests that optimism should be conceptualized as an adaptive bias that signals the nature of one’s environment and leads to specific recommendations for future lines of enquiry. Optimism declines across early childhood, but there is no theoretical account for why such changes occur. In this Perspective, Leonard and Sommerville discuss and integrate three candidate causes for age-related declines in optimism: learning from experience, theory development and valenced learning biases.
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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) | 0.001 | 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".