The Cognitive Foundations of Fictional Stories
Bibliographic record
Abstract
Over the past three decades, psychology has documented the recurrence of specific themes in stories across cultures (e.g., love, alliances, monsters, imaginary worlds) and linked them to identifiable motivational mechanisms (e.g., mate choice, cooperation, threat detection, exploration). Yet this body of work has remained fragmented, organized around individual themes rather than integrated into a unified account of what makes narrative content psychologically engaging. Here, we propose the Motivational-Ingredient Framework. We identify a set of motivational ingredients: core narrative features, each defined by the specific motivational mechanism it activates. Drawing on evolutionary psychology, affective neuroscience, and the study of human motivation, we compile a comprehensive table of ingredients grounded in the current understanding of human motivational architecture. This framework offers a theory-driven, cross-culturally applicable foundation for the empirical study of narrative content, with implications for fields ranging from media psychology and computational humanities to education and public policy.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".