narrativeencoding

Narrative encoding is the process of representing narrative content—events, characters, settings, and causal relations—in a formal representation suitable for storage, processing, or analysis. The term is used across disciplines, including cognitive psychology, literary studies, and computer science, to describe how stories are captured and organized in memory, texts, or computational models.

In cognitive psychology, narrative encoding refers to how individuals encode and reconstruct stories in memory. Researchers emphasize the roles of event segmentation, causal inference, schema activation, and perspective-taking. Encoding efficiency depends on prior knowledge, attention, and the presence of coherent plot structures, with people often remember causally linked events more accurately than isolated details.

In computational contexts, narrative encoding covers methods for transforming narrative content into machine-readable representations. Techniques include event extraction, coreference resolution, plot graph construction, character networks, and timeline or event sequences. Representations may be symbolic (graphs, frames) or distributed (embeddings, sequence models), and may be enriched with metadata such as sentiment, goals, or motivations.

Applications include digital humanities analyses of literary works, narrative-based data analysis, and support for AI storytelling and interactive fiction. Challenges involve ambiguity, temporal ordering, varying narrative viewpoints, and the subjective nature of interpretation. Evaluation is difficult because narratives serve multiple communicative purposes beyond factual accuracy.

See also: narrative structure, event extraction, knowledge graph, memory encoding, storytelling analytics.