Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. We analyze the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. In this talk, we will highlight the application of this probabilistic framework in psychology, showing how the topic model performs well in predicting word association, false memory, as well as the effects of semantic association and ambiguity on a variety of language processing and memory tasks.
Presentation (PDF File)
Presentation (PowerPoint File)
Copyright. All Rights Reserved.