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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".