this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization.

According to the instantiation principle, the representation of a category includes detailed information about its diverse range of instances. Many accounts of categorization, including classical and standard prototype theories, do not follow the instantiation principle, because they assume that detailed, exemplar-level information is filtered out of category representations. Nevertheless, the instantiation principle can be implemented in a wide class of models, including both exemplar and abstraction models. To assess the instantiation principle empirically, a parameter-free exemplarbased model of instantiation was applied to typicality judgments for 16 simple categories (e.g., mammal, beverage) and 14 complex categories (e.g., dangerous mammal) in four superordinates (animal, food, small animal, dangerous animal). Across three studies, the model did an excellent job of predicting mean typicality judgments (correlations generally above .9) and a good job of predicting standard deviati...

Abstract not found

Abstract not found

The new field of developmental robotics faces the formidable challenge of implementing effective learning mechanisms in complex, dynamic environments. We make a case that knowledge-based learning algorithms might help to meet this challenge. A constructive neural learning algorithm, knowledge-based cascade-correlation (KBCC), autonomously recruits previously-learned networks in addition to the single hidden units recruited by ordinary cascade-correlation. This enables learning by analogy when adequate prior knowledge is available, learning by induction from examples when there is no relevant prior knowledge, and various combinations of analogy and induction. A review of experiments with KBCC indicates that recruitment of relevant existing knowledge typically speeds learning and sometimes enables learning of otherwise impossible problems. Some additional domains of interest to developmental robotics are identified in which knowledge-based learning seems essential. The characteristics of KBCC in relation to other knowledge-based neural learners and analogical reasoning are summarized as is the neurological basis for learning from knowledge. Current limitations of this approach and directions for future work are discussed. Keywords: Knowledge-based learning; neural networks; knowledge transfer; developmental robotics. 245 246 T. R. Shultz et al. 1.