Tall is typical: Central tendency, ideal dimensions, and graded category structure among tree experts and novices

Many accounts of categorization equate goodness-of-example with central tendency for common taxonomic categories; the best examples of a category are average members--those that are most similar to most other category members. In the present study, we asked 24 tree experts and 20 novices to rate goodness-of-example for a sample of 48 trees and found (1) that the internal structure of the category tree differed between novices and experts and (2) that central tendency did not determine goodness-of-example ratings for either group. For novices, familiarity determined goodness-of-example ratings. For experts, the ideal dimensions of height and weediness, rather than average similarity to other trees, were the primary predictors of goodness-of-example ratings for experts. The best examples of tree were not species of average height, but of extreme height. The worst examples were the weediest trees. We also found systematic differences in predictors of goodness-of-example as a function of type of expertise. We argue that the internal structure of taxonomic categories can be shaped by goal-related experience and is not necessarily a reflection of the attributional structure of the environment. Implications for models of category structure and category learning are discussed.