Lincar separability in classification learning.

Four experiments were performed to determine whether linearly separable categories are easier to learn than categories that are not linearly separable. Linearly separable categories are categories that can be perfectly partitioned on the basis of a weighted, additive combination of component information. There was no evidence found that linear separability is a major constraint on human classification performance.