The beneficial role of curiosity on route memory in children
Bibliographic record
Abstract
Introduction: There has been a growing interest in the role of innate curiosity on facets of human cognition, such as in spatial learning and memory. Yet, it is unclear how state level curiosity evoked by the current environment could interact differentially with trait curiosity, to impact spatial memory performance. Methods: We assessed the influence of trait and state curiosity on route memory. Forty-two 10-year-old children with low and high-trait curiosity (20 Females; 22 Males) actively explored virtual environments that elicited varying levels of uncertainty (i.e., state-curiosity). Results: As trait curiosity increased, so did memory performance in low and high uncertainty conditions, suggesting that high-curiosity children can better recruit cognitive resources within non-optimal environments. Children with high compared to low trait curiosity also reported greater feelings of presence during exploration. Importantly, in environments with medium uncertainty, children with low trait curiosity were able to perform as well as those with high curiosity. Discussion: Results show that individual differences in trait curiosity influence route learning and these interact dynamically with state-curiosity invoked within different environments.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".