SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into

The authors examined inductive reasoning among experts in a domain. Three types of tree experts (landscapers, taxonomists, and parks maintenance personnel) completed 3 reasoning tasks. In Experi-ment 1, participants inferred which of 2 novel diseases would affect "more other kinds of trees " and provided justifications for their choices. In Experiment 2, the authors used modified instructions and asked which disease would be more likely to affect "all trees. " In Experiment 3, the conclusion category was eliminated altogether, and participants were asked to generate a list of other affected trees. Among these populations, typicality and diversity effects were weak to nonexistent. Instead, experts ' reasoning was influenced by "local " coverage (extension of the property to members of the same folk family) and causal-ecological factors. The authors concluded that domain knowledge leads to the use of a variety of reasoning strategies not captured by current models of category-based induction. Cognitive psychologists are increasingly interested in concep-tual functions beyond categorization (e.g., Barsalou & Hale, 1992; Markman, Yamauchi, & Makin, 1997; Pazzani, 1991; Ross, 1996, 1997; Wisniewski, 1995). Particularly, they have focused on the use of categories in reasoning and have proposed a number of formal models of category-based reasoning (e.g., Heit, 1998; Mc-

world, that the human conceptual system is designed to create systematic categories, and that people have theories about the world that bind together seemingly unrelated features. The authors have suggested that the need to establish reference in communication also influences category coherence. This proposal was tested in 2 studies involving a referential communica-tion task. In these studies, consistency was promoted between individuals by communication, which synchronized the category structures of different people. Further, people were focused on the commonalities of objects and on the differences related to the commonalities by communication--a pattern that is compatible with what has been observed in existing categories. These results suggest that categorization research must incorporate communication tasks into the canon of methodologies used to study category structure. The human conceptual system is notable both for its rich structure and its profound flexibility. For example, studies of taxonomic categories demonstrate that people have hierarchi-cally organized category structures and that they often name pictures of objects with a term at a middle level of

An ongoing goal in the field of categorization has been to determine how objects ’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed. It is generally accepted that people’s concepts include not only the features and attributes of the entity being represented, but also the ways in which those features are related to one another. For example, we know that hormones can alter a person’s behavior, that chemical structure can affect a substance’s hardness, and that processor

To Dandelion Kaczmarczyk, who always reminded me about the most important things in life. Acknowledgments There are so many people who supported, encouraged and mentored me while I worked on this dissertation. Most important, I would like to thank my advisor, Risto Miikkulainen. First, for supervising this interdisciplinary research. Second, for teaching me so much about conducting rigorous research and expressing my results with confidence. Third, for being a nice person. I would also like to thank the other members of my committee. Andrew Bernat, for his excellent advice on many occasions; Anthony Petrosino, for directing me to important resources on cognition and learning; Raymond Mooney, for his perspective on machine learning; Bradley Love for his perspective from cognitive psychology; Lowell Bethel, for his encouragement, especially during my early years at UT. Many other people supported me and my work at critical junctures. I want to especially thank Marilla Svinicki for her support during my comprehensive exams, and when I was developing my human subject study. Also, Jim Bednar, for his technical advice on numerous occasions, and Elaine Rich for her understanding of the importance of teaching and learning. Many of the staff in

Previous studies have suggested better learning when people actively intervene rather than when they passively observe the stimuli in a judgment task. In 4 experiments, the authors investigated the hypothesis that this improvement is associated with a shift from exemplar memory to cue abstraction. In a multiple-cue judgment task with continuous cues, the data replicated the improvement with intervention and participants who experimented more actively produced more accurate judgments. In a multiple-cue judgment task with binary cues, intervention produced poorer accuracy and participants who experimented more actively produced poorer judgments. These results provide no support for a representational shift but suggest that the improvement with active intervention may be limited to certain tasks and environments.

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