Modeling knowledge-based inferences in story comprehension

Abstract A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high-dimensional “situation-state space.” This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart’s [Discourse Proc 16 (1993) 203] localist representation. A story taking place in the microworld corresponds to a trajectory through situation-state space. During the inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of processing. An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion.