The Differences and Similarities between Curiosity and Interest: Meta-analysis and Network Analyses
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
The relationship and difference between curiosity and interest have received considerable attention and discussion. Yet, most of the discussions have not been based on empirical evidence. Here we report three studies examining the relationship between curiosity and interest. The first study was a meta-analysis that examined the Pearson correlations between scales assessing curiosity and interest. Based on 24 studies (31 effect sizes), we found that the curiosity scales correlated with the interest scales at a moderate level (r = .53), but they had extremely high heterogeneity, suggesting that the relationship largely depended on how they were conceptualized. The second and third studies applied network analyses (i.e., co-occurrence analysis and correlation-based analysis) to data that was collected using experience sampling method, examining the way in which the subjective feelings of curiosity and interest are related. Across the studies, we found consistent differences between the feelings associated with curiosity and those associated with interest. While the feelings of curiosity reflected feelings of inquisitiveness and eagerness to know more, the feelings of interest were aligned with positive affect such as enjoyment and happiness. Importantly, an asymmetrical pattern was found in curiosity-interest co-occurrences: when the feelings of curiosity occurred, the co-occurrence of the feelings of interest was highly likely, but not so vice versa. That is, when the feelings of interest occurred, the feelings of curiosity did not always co-occur. Theoretical and practical implications of these findings are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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