Category dimensionality and feature knowledge: When more features are learned as easily as fewer

  • Hoffman A
  • Murphy G
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Abstract

Three experiments compared the learning of lower-dimensional family resemblance categories (4 dimensions) with the learning of higher-dimensional ones (8 dimensions). Category-learning models incorporating error-driven learning, hypothesis testing, or limited capacity attention predict that additional dimensions should either increase learning difficulty or decrease learning of individual features. Contrary to these predictions, the experiments showed no slower learning of high-dimensional categories; instead, subjects learned more features from high-dimensional categories than from low-dimensional categories. This result obtained both in standard learning with feedback and in noncontingent, observational learning. These results show that rather than interfering with learning, categories with more dimensions cause individuals to learn more. The authors contrast the learning of family resemblance categories with learning in classical conditioning and probability learning paradigms, in which competition among features is well documented.

Author-supplied keywords

  • Category learning
  • Concepts
  • Error-driven learning
  • Unsupervised learning

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Authors

  • Aaron B. Hoffman

  • Gregory L. Murphy

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