Epistemic Emotions and the Number of Sources Explored
Bibliographic record
Abstract
Several theories postulate that epistemic emotions, such as curiosity and surprise, motivate information-seeking. In these theories, information-seeking is treated as a unitary, singular construct, with the motivation assumed to be consistent across the various forms of exploration. However, there have been few empirical studies which actually test the emotion-exploration link, and the question of whether epistemic emotions affect the number of sources individuals explore (i.e., the breadth of search) has not been examined. The goal of the present study was thus to investigate the emotion-exploration link in the context of the breadth of exploration. To examine this question, university students were presented with trivia questions and asked to answer them. Participants were then shown an answer submitted by another participant, asked to rate how surprised and curious they felt in response, and then given the option of exploring up to three more responses submitted by different participants. In line with previous work, we found that the model with best fit to the data consisted of a serial mediation, with certainty predicting surprise, surprise predicting curiosity, and curiosity predicting the number of sources explored. However, in contrast to previous work, we did not consistently find that high-certainty errors resulted in greater epistemic emotions or exploration than low-certainty errors. The current findings support the many theories that argue for a role of emotions in motivating information-seeking behaviours, and demonstrated that this emotion-exploration link extends to the breadth of search as well.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".