The Differences and Similarities between Curiosity and Interest: Meta-analysis and Network Analyses
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.
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.
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 | 0.045 | 0.113 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".