Golden, R. M. and Rumelhart, D. E. (1993).
A parallel distributed processing model of story comprehension and recall,
Discourse Processes, 16,
203-237.
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 readers 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 multi-state 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
which 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.
Semi-final post script draft
(possibly missing figures)
Golden's Text Comprehension and Memory Publications