Year: 2026
Type: conference-paper
Abstract: Narrative flow emerges from the interplay between memory and expectation, shaping how stories are both produced and understood.To operationalize this construct, Sap et al. (2022) propose sequentiality, a language-model-based measure of sentence-level predictability, and report that imagined stories flow better than recalled ones.We conduct a large-scale replication across multiple language models, examine how modeling choices shape the original findings, and test generalization beyond crowdworker data using passages from published fiction and narrative non-fiction.Although the original contrast replicates under their initial formulation, it diminishes substantially under alternative specifications, suggesting that it reflects properties of the measurement setup rather than a stable feature of narrative flow.By contrast, fiction does appear to exhibit a robust sequentiality advantage over reality-bound genres under a minimal contextonly formulation.However, mixed-effects analyses indicate that this advantage is not reducible to standard coherence measures, underscoring the need for further theoretical and empirical grounding of narrative flow.
Institutions: Cornell University, McGill University
Language: English
Subfield: Literature and Literary Theory
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166856248