Reading fiction and reading minds: the role of simulation in the default network
Bibliographic record
Abstract
Research in psychology has suggested that reading fiction can improve individuals' social-cognitive abilities. Findings from neuroscience show that reading and social cognition both recruit the default network, a network which is known to support our capacity to simulate hypothetical scenes, spaces and mental states. The current research tests the hypothesis that fiction reading enhances social cognition because it serves to exercise the default subnetwork involved in theory of mind. While undergoing functional neuroimaging, participants read literary passages that differed along two dimensions: (i) vivid vs abstract and (ii) social vs non-social. Analyses revealed distinct subnetworks of the default network respond to the two dimensions of interest: the medial temporal lobe subnetwork responded preferentially to vivid passages, with or without social content; the dorsomedial prefrontal cortex (dmPFC) subnetwork responded preferentially to passages with social and abstract content. Analyses also demonstrated that participants who read fiction most often also showed the strongest social cognition performance. Finally, mediation analysis showed that activity in the dmPFC subnetwork in response to the social content mediated this relation, suggesting that the simulation of social content in fiction plays a role in fiction's ability to enhance readers' social cognition.
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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 | 0.000 | 0.005 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".