Thesis (Ph.D.) - Indiana University, Department of Psychological & Brain Sciences and the Cognitive Science Program, 2022Categorization and old-new recognition memory are closely linked topics in the psychological literature, and have both benefitted from extensive formal modelling efforts. However, the existing literature examining their relationship has almost exclusively used simplified artificial stimuli. The present work extends this literature by collecting both categorization and old-new recognition judgments on a set of naturalistic stimuli: namely, a set of 540 images of rocks. The abilities of different models to fit the categorization and recognition data are discussed in detail, as are various efforts at improving the feature space representation of the stimuli. Ultimately, the categorization data was fit well by an exemplar and clustering model, but not a prototype model. Only the exemplar model was able to provide an account of the recognition data; however, the model had difficulty capturing variability in participants’ recognition judgments of previously seen stimuli.