Moderators of curiosity and information seeking in younger and older adults.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
= 514) rated their curiosity about content before having the opportunity to seek out more information. Experiment 1 examined the impact of social value on curiosity and information seeking about trivia. Online popularity metrics served as social value cues. Metric visibility increased engagement with high-popularity information for older adults, whereas it decreased engagement with low-popularity information for younger adults. Experiment 2 examined the impact of practical value on curiosity and information seeking about science facts. Personal and collective practical value were highlighted by linking the information to the domains of medicine and the environment, respectively. Patterns of curiosity and information seeking revealed greater sensitivity to collective practical value in older than younger adults. In both experiments, the relationship between curiosity and information seeking was stronger in older adults than in younger adults. Overall, these findings suggest that age differences in motivational priorities may lead to age differences in curiosity and information seeking. In addition to highlighting strategies for fostering curiosity in older learners, these findings may also inform digital literacy interventions aimed at reducing engagement with clickbait and misinformation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it