Part I: Creating Semantic Spaces for Words based on Free Association NormsMethods to Construct Semantic SpacesWord Association SpacesWord Frequency and the Similarity Structure in WASPredicting the Output Order of Free Association NormsSemantic/ Associative Similarity RelationsCapturing Between/Within Semantic Category Differences in WASPredicting Memory PerformancePredicting Results from DeesePredicting Extralist Cued RecallDiscussionAppendixNotesReferencesPart II: Predicting Memory Performance with Word Association SpacesSemantic and Physical Similarity Effects in MemoryWord frequency effects in recognition memoryA memory model for semantic and orthographic similarity effectsOverview of ModelTwo memory judgmentsSemantic featuresOrthographic featuresEpisodic storageCalculating FamiliarityRecognition and Similarity JudgmentsWord frequency effectsPredicting Individual Word Differences.Overview of ExperimentsExperiment 1MethodResultsDiscussion.Model Fits of Experiment 1Experiment 2MethodResultsDiscussionModel Fits of Experiment 2Experiment 3MethodResults and DiscussionModel Fits of Experiment 3General DiscussionNotesReferencesAppendix A Words of Experiment 1Appendix B Words of Experiment 2Appendix C Words of Experiment 3Part III: Feature Frequency Effects in Recognition MemoryExperimentMethodResultsModel FitsModel A, arbitrary featuresModel B: orthographic featuresConclusionFootnotesReferencesAppendix A Words of Experiment 1Appendix B Means and standard deviations of the word frequencies and feature frequencies A and BMODELING SEMANTIC AND ORTHOGRAPHIC SIMILARITYEFFECTS ON MEMORY FOR INDIVIDUAL WORDS Mark SteyversSubmitted to the faculty of the University Graduate Schoolin partial fulfillment of the requirements for the degree Doctor of Philosophyin the Department of PsychologyIndiana UniversitySeptember 2000© 2000Mark SteyversALL RIGHTS RESERVEDAbstractMany 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. AcknowledgmentsFirst 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 graduatecareer 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 helpfuldiscussions. 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.Contact: Mark Steyvers at [email protected] Stanford University. Building 420, Jordan Hall, Stanford, CA 94305-2130, Tel: (650) 725-5487, Fax: (650) 725-5699Part I: Creating Semantic Spaces for Words based on Free Association Norms1Methods to Construct Semantic Spaces 1Word Association Spaces 2Word Frequency and the Similarity Structure in WAS 3Predicting the Output Order of Free Association Norms 4Semantic/ Associative Similarity Relations.........................................................................6Capturing Between/Within Semantic Category Differences in WAS 8Predicting Memory Performance 8Predicting Results from Deese 8Predicting Extralist Cued Recall 9Discussion 9Appendix 10References 11Part II: Predicting Memory Performance with Word Association Spaces 14Semantic and Physical Similarity Effects in Memory 14Word frequency effects in recognition memory 16A memory model for semantic and orthographic similarity effects 16Overview of Model 17Two memory judgments 17Semantic features 18Orthographic features 18Episodic storage 19Calculating Familiarity 20Recognition and Similarity Judgments 21Word frequency effects 21Predicting Individual Word Differences. 22Overview of Experiments 23Experiment 1 23Method 23Results 24Discussion. 25Model Fits of Experiment 1 27Experiment 2 29Method 29Results 31Discussion 33Model Fits of Experiment 2 34Experiment 3 35Method 35Results and Discussion 36Model Fits of Experiment 3 38General Discussion 38References 40Appendix A Words of Experiment 1 42Appendix B Words of Experiment 2 43Appendix C Words of Experiment 3 45Part III: Feature Frequency Effects in Recognition Memory 46Experiment 46Method 47Results 48Model Fits 49Model A, arbitrary features 49Model B: orthographic features 50Conclusion 51Footnotes 52References 52Appendix A Words of Experiment 1 53Appendix B Means and standard deviations of the word frequencies and feature frequencies A and B55Part I:Creating Semantic Spaces for Words based on Free Association NormsIt has been