Rethinking the input: Skewed distributions of exemplars result in broad generalization in category learning
What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the effects of skewed input distributions on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than uniform and normal distributions. Our results not only suggest that the current category learning theories may underestimate category generalization but also challenge the current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies.