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

Theory-based Bayesian models of inductive reasoning 2 Theory-based Bayesian models of inductive reasoning

The rational model of categorization (RMC; Anderson, 1990) assumes that categories are learned by clustering similar stimuli together using Bayesian inference. As computing the posterior distribution over all assignments of stimuli to clusters is intractable, an approximation algorithm is used. The original algorithm used in the RMC was an incremental procedure that had no guarantees for the quality of the resulting approximation. Drawing on connections between the RMC and models used in nonparametric Bayesian density estimation, we present two alternative approximation algorithms that are asymptotically correct. Using these algorithms allows the effects of the assumptions of the RMC and the particular inference algorithm to be explored

Previous research has shown that background knowledge affects the ease of concept learning, but little research has examined its effects on speeded categorization of instances after the category is well learned. Subjects in 4 experiments first learned novel categories. At test, they categorized a new set of novel stimuli that were either consistent or inconsistent with background knowledge given about the categories. Background knowledge affected catego-rization responses in an untimed task, with usual reaction time instructions, with a response deadline, or when the stimuli were presented for 50 ms followed by a mask. Three other experiments using a part-detection task showed that subjects were more likely to notice missing parts that were critical than noncritical according to background knowledge. The mechanisms by which background knowledge affects categorization and part detection are discussed. Human categorization is a cognitive proceSs in which people decide whether an instance is a member of a cate-gory by comparing the instance with their conceptual rep-resentations. Categorization research in the 1970s and early

Abstract not found

Abstract not found

How do consumers learn about and develop preferences for new products that do not fit neatly into any existing category? These so-called really new products (Lehmann 1994) are innovations that defy straightforward classification in terms of existing product concepts (Gregan-Paxton and Roedder John 1997, p. 275) and thus “create, or at least substantially expand, a category rather than reallocate shares ” within an existing one (Marketing Science Institute 1994, p. 6). From a marketer’s perspective, the significant learning costs that these innovations impose on consumers present not only a challenge, but also an opportunity. In the process of educating consumers about a new product, marketers have the chance to influence how consumers structure their representations of it. *C. Page Moreau is assistant professor of marketing, Edwin L. Cox

www.elsevier.com/locate/cogpsych

Two experiments investigated how people perform category-based induction for items that have uncertain categorization. Whereas normative considerations suggest that people should consider multiple relevant categories, much past research has argued that people focus on only the most likely category. A new method is introduced in which responses on individual trials can be classified as using single or multiple categories, an improvement on past methods that relied on null effects as evidence for single-category use. Experiment 1 found that people did use multiple categories when the most likely category gave an ambiguous induction but that few people did so when it gave an unambiguous induction. Experiment 2 suggested that the reluctance to use multiple categories arose from a cognitive shortcut, in which only one source of information is consulted. The experiments revealed significant individual differences, suggesting that use of multiple categories is one of a number of strategies that can be used rather than being the basis for most category-based induction.

Abstract not found