Narrative thinking lingers in spontaneous thought
Bibliographic record
Abstract
What we think about at any moment is shaped by what preceded it. Why do some experiences, such as reading an immersive story, feel as if they linger in mind for longer than others? In this study, we hypothesize that the stream of our thinking is especially affected by "deeper" forms of processing, emphasizing the meaning and implications of a stimulus rather than its immediate physical properties or low-level semantics (e.g., reading a story vs. reading disconnected words). To test this idea, we presented participants with short stories that preserved different levels of coherence (word-level, sentence-level, or intact narrative), and we measured participants’ self-reports of lingering and spontaneous word generation. Participants reported that stories lingered in their minds after reading, but this effect was greatly reduced when the same words were read with sentence or word-order randomly shuffled. Furthermore, the words that participants spontaneously generated after reading shared semantic meaning with the story’s central themes, particularly when the story was coherent (i.e., intact). Crucially, regardless of the objective coherence of what each participant read, lingering was strongest amongst participants who reported being ‘transported’ into the world of the story while reading. We further generalized this result to a non-narrative stimulus, finding that participants reported lingering after reading a list of words, especially when they had sought an underlying narrative or theme across words. We conclude that recent experiences are most likely to exert a lasting mental context when we seek to extract and represent their deep situation-level meaning.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".