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MODELING SEMANTIC AND ORTHOGRAPHIC SIMILARITY
EFFECTS ON MEMORY FOR INDIVIDUAL WORDS

Mark Steyvers


Submitted to the faculty of the University Graduate School


in partial fulfillment of the requirements for the degree
Doctor of Philosophy
in the Department of Psychology
Indiana University

September 2000

© 2000

Mark Steyvers



ALL RIGHTS RESERVED

Abstract


Many memory models assume that the semantic and physical features of words can be represented by collections of features abstractly represented by vectors. Most of these memory models are process oriented; they explicate the processes that operate on memory representations without explicating the origin of the representations themselves; the different attributes of words are typically represented by random vectors that have no formal relationship to the words in our language. In Part I of this research, we develop Word Association Spaces (WAS) that capture aspects of the meaning of words. This vector representation is based on a statistical analysis of a large database of free association norms. In Part II, this representation along with a representation for the physical aspects of words such as orthography is combined with REM, a process model for memory. Three experiments are presented in which distractor similarity, the length of studied categories and the directionality of association between study and test words were varied. With only a few parameters, the REM model can account qualitatively for the results. Developing a representation incorporating features of actual words makes it possible to derive predictions for individual test words. We show that the moderate correlations between observed and predicted hit and false alarm rates for individual words are larger than can be explained by models that represent words by arbitrary features. In Part III, an experiment is presented that tests a prediction of REM: words with uncommon features should be better recognized than words with common features, even if the words are equated for word frequency.

Acknowledgments

First and foremost, I would like to thank Rich Shiffrin who has been a great advisor and mentor. His influence on this dissertation work has been substantial and his insistence on aiming for only the best scientific research will stay with me forever. Also, Rob Goldstone has been an integral part of my graduate career with our many collaborations and stimulating conversations. I would also like to acknowledge my collaborators Ken Malmberg and Joseph Stephens in the research presented in part III of the dissertation and Tom Busey who provided both ideas and encouragement of any project of shared interest. I would also like to thank Eric-Jan Wagenmakers, Rob Nosofsky, and Dan Maki for their support and many helpful discussions. Last but not least, my friends Peter Grünwald, Mischa Bonn, and Dave Huber have always been supportive and I can highly recommend going out with these guys.






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