ABSTRACT
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).
