Describes a recursive transition network model that analyzes and represents a story as a causal network of categorized clauses and labeled relations. Reports on two experiments using procedural criteria to identify inferences and the assumptions that inferences operate over distances in the text and are transitive, resulting in a network…

Introduces a multistate probabilistic causal chain notation for describing the knowledge structures implicitly represented by the subjective conditional probability distribution. Proposes a psychological process model of how story comprehension and recall processes operate using causal chain representations. Compares the model's story-recall…