Pattern-directed processing of knowledge from texts

A framework for viewing human text comprehension, memory, and recall is presented that assumes patterns of abstract conceptual relations are used to guide processing. These patterns consist of clusters of knowledge that encode prototypical co-occurrences of situations and events in narrative texts. The patterns are assumed to be a part of a person's world knowledge and can be activated during comprehension to build associations among multiple linguistic propositions in memory according to their higher-order conceptual relations. During text reproduction from memory, these patterns provide retrieval plans for recall and a mechanism for sophisticated "guessing" when retrieval fails. Some data from human text learning tasks are presented as evidence for these higher-order conceptual patterns. Several structural and processing properties of the model are evaluated in light of these data. It is argued that the proposed pattern-directed processing model could be successfully implemented in artificial intelligence systems to provide adaptive error-handling mechanisms such as those observed in human behavior.