Although there is wide evidence on young children's category learning, questions concerning how cognitive mechanisms and social mediation work collaboratively in this process remain sparse. Here, we study the impact of pedagogy in young children's categorization of novel artifacts. A before-and-after micro-genetic study compared 58 3-year-old…

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the…

In judgment and categorization, the task is to infer the criterion value of an object based on cues. The cognitive mechanisms underlying such inferences are often distinguished in terms of whether they rely on an abstracted cue-criterion rule or on retrieving exemplars. The use of cue-based and exemplar-based strategies (and the associated…

We apply the "wisdom of the crowd" idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals…

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in…

Children must be willing to accept some of what they hear "on faith," even when that testimony conflicts with their own expectations. The study reported here investigated the relation among vocabulary size, object recognition, and 24-month-olds' (N = 40) willingness to accept potentially surprising testimony about the category to which an object…

Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ…

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas…

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN 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…

Suggests that in learning and development, the process of comparison can act as a bridge between similarity-based and rule-based processing. A structure-sensitive comparison process, triggered by experiential or symbolic juxtapositions can: (1) facilitate understanding of structural commonalities and the abstraction of rules; and (2) facilitate…

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning,…

Four studies examined the tendency of preschoolers to use verbal labels versus appearance information in making novel inductive inferences. Results revealed that preschoolers tended to use trait labels of "shy" or "outgoing" rather than superficial resemblance in making psychological inferences. These results could not be attributed to biases on…