Learning nonlinearly separable categories by inference and classification.

  title={Learning nonlinearly separable categories by inference and classification.},
  author={Takashi Yamauchi and Bradley C. Love and Arthur B. Markman},
  journal={Journal of experimental psychology. Learning, memory, and cognition},
  volume={28 3},
Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous… CONTINUE READING
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