Rational analysis as a link between human memory and information retrieval
Mark Steyvers, Thomas L. Griffiths · 2008
Abstract This chapter provides a complementary Bayesian analysis of the problem of memory retrieval. A Bayesian model that is able both to classify words into semantically coherent groups, merely from observing their co-occurrence patterns in texts, is used as the basis for understanding aspects not only of how some linguistic categories might be created, but also how relevant information can be retrieved, using probabilistic principles. This work can be viewed as a natural follow-on from Anderson and colleagues' pioneering rational analyses of memory (Anderson & Milson, 1989; Anderson & Schooler, 1991). This chapter uses innovations in information retrieval as a way to explore the connections between research on human memory and information retrieval systems. It also provides an example of how cognitive research can help information retrieval research by formalizing theories of knowledge and memory organization that have been proposed by cognitive psychologists.