Extending the ALCOVE model of category learning to featural stimulus domains
AbstractThe ALCOVE model of category learning, despite its considerable success in accounting for human performance across a wide range of empirical tasks, is limited by its reliance on spatial...
View ArticleCommon and distinctive features in stimulus similarity: A modified version of...
AbstractFeatural representations of similarity data assume that people represent stimuli in terms of a set of discrete properties. In this article, we consider the differences in featural...
View ArticleSimilarity, distance, and categorization: A discussion of Smith’s (2006)...
AbstractThe idea that categorization decisions rely on subjective impressions of similarities between stimuli has been prevalent in much of the literature over the past 30 years and has led to the...
View ArticleDoes response scaling cause the generalized context model to mimic a...
AbstractSmith and Minda (1998, 2002) argued that the response scaling parameter γ in the exemplar-based generalized context model (GCM) makes the model unnecessarily complex and allows it to mimic the...
View ArticleBetter explanations of lexical and semantic cognition using networks derived...
AbstractIn this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three...
View ArticleLearning time-varying categories
AbstractMany kinds of objects and events in our world have a strongly time-dependent quality. However, most theories about concepts and categories either are insensitive to variation over time or treat...
View ArticleErroneous Gambling-Related Beliefs as Illusions of Primary and Secondary...
AbstractDifferent classification systems for erroneous beliefs about gambling have been proposed, consistently alluding to ‘illusion of control’ and ‘gambler’s fallacy’ categories. None of these...
View ArticleThe helpfulness of category labels in semi-supervised learning depends on...
AbstractThe study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is...
View ArticleNot every credible interval is credible: Evaluating robustness in the...
AbstractAs Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide...
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