Does reading about fictional minds make us more curious about real ones?
Bibliographic record
Abstract
Abstract Although there is a large body of research assessing whether exposure to narratives boosts social cognition immediately afterward, not much research has investigated the underlying mechanism of this putative effect. This experiment investigates the possibility that reading a narrative increases social curiosity directly afterward, which might explain the short-term boosts in social cognition reported by some others. We developed a novel measure of state social curiosity and collected data from participants (N = 222) who were randomly assigned to read an excerpt of narrative fiction or expository nonfiction. Contrary to our expectations, we found that those who read a narrative exhibited less social curiosity afterward than those who read an expository text. This result was not moderated by trait social curiosity. An exploratory analysis uncovered that the degree to which texts present readers with social targets predicted less social curiosity. Our experiment demonstrates that reading narratives, or possibly texts with social content in general, may engage and fatigue social-cognitive abilities, causing a temporary decrease in social curiosity. Such texts might also temporarily satisfy the need for social connection, temporarily reducing social curiosity. Both accounts are in line with theories describing how narratives result in better social cognition over the long term.
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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 | 0.001 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".