A parallel distributed processing model of story comprehension and recall

Abstract

An optimal control theory of story comprehension and recall is proposed within the framework of a “situation”‐state space. A point in situation‐state space is specified by a collection of propositions, each of which can have the values of either “present” or “absent.” A trajectory in situation‐state space is a temporally ordered sequence of situations. A reader's knowledge that the occurrence of one situation is likely to cause the occurrence of another situation is represented by a subjective conditional probability distribution. A multistate probabilistic (MSP) causal chain notation is also introduced for conveniently describing the knowledge structures implicitly represented by the subjective conditional probability distribution. A story is represented as a partially specified trajectory in situation‐state space, and thus, story comprehension is defined as the problem of inferring the most probable missing features of the partially specified story trajectory. The story‐recall process is also viewed as a procedure that solves the problem of estimating the most probable missing features of a partially specified trajectory, but the partially specified trajectory in this latter case is an episodic memory trace of the reader's understanding of the story. A parallel distributed processing (PDP) model whose connection strengths are derived from the MSP causal chain representation is then introduced. The PDP model is shown to solve the problem of estimating the missing features of a partially specified trajectory in situation‐state space, and the model's story‐recall performance is then qualitatively compared to known performance characteristics of human memory for stories.