Proceedings of the Annual Meeting of the Cognitive Science Society

How are knowledge intensive text comprehension processes computed? Specifically, how are 1) explicit propositions remembered correctly, 2) pronouns resolved, 3) coherence and prediction inferences drawn, 4) on-going interpretations revised as more information becomes available, and 5) how is information learned in specific contexts generalized to novel texts? The Story Gestalt model, which uses a constraint satisfaction process to compute these processes, is successful because each of the above processes can be seen as examples of the same process of constraint satisfaction, constraints can have strengths to represent the degrees of correlation among information, and the independence of constraints provides insight into generalization. In the model, propositions describing a simple event, such as going to the beach or a restaurant, are sequentially presented to a recurrent P DP network. The model is trained to process the texts by requiring it to answer questions about the texts. Each question is the bare predicate from a proposition in the text or a proposition that is inferrable from the text. The model answers the question by completing the proposition to which the predicate belongs. The model accomplishes the five processing tasks listed above and provides insight into how a constraint satisfaction model can compute knowledge intensive processes in text comprehension.