A probabilistic model of text recall is proposed which assigns a probability mass to a given recall protocol. Knowledge analyses of semantic relationships among events identified in the text are used to specify the architecture of the probability model. Twelve subjects (the training data group) were then asked to recall twelve texts from memory. The recall protocols generated by the twelve subjects were then used to estimate the strengths of the semantic relationships in the probabilistic model. The Gibbs Sampler algorithm (a connectionist-like algorithm) was then used to sample from the probabilistic model in order to generate synthesized recall protocols. These synthesized recall protocols were then compared with the original set of recall data and recall data collected from an additional group of twelve human subjects (the test data group).

An optimal control theory of story comprehension and recall is proposed within the framework of a "situation" stale 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". Story comprehension is viewed as finding a temporally-ordered sequence of situations or "trajectory" which is consistent with story-imposed constraints. Story recall is viewed as finding a trajectory consistent with episodic memory constraints. A multistate probabilistic (MSP) machine representational scheme is then introduced for compactly and formally assigning a "degree of belief (i.e., a probability value) to each trajectory in the slate space. A connectionist model is also introduced which searches for trajectories which arc highly probable with respect lo a set of constraints and an M S P machine representation. Like human subjects, the model (i) recalls propositions with greater causal connectivity as retention interval is increased, and (ii) demonstrates how misordered propositions tend to "drift" more towards their canonical position in a text as retention interval is increased.

A new parallel distributed processing (PDP) model possessing a statistical interpretation is proposed for ex- tracting critical psychological regularities from the tem- poral structure of human free recall data. The model is essentially a non-linear five parameter Jordan sequen- tial network for predicting categorical time-series data. T h e model consists of five parameters: an episodic strength parameter (t/), a causal strength parameter {0), a shared causal/episodic strength parameter (7), a work- ing memory span parameter (//), and a number of items recalled parameter (A). T h e "psychological validity" of the model's parameter estimates were then evaluated with respect to the existing experimental literature us- ing children and adult free recall data from four stories. The model's parameter estimates replicated and ex- tended several previously known experimental findings. In particular, the model showed: (i) effects of causal structure /3, (ii) showed a decrease in (7/4-7) while /? remained constant as retention interval increased, and (iii) an increase in {r]+y) whileftremained constant as subject age increased.