A difficulty predictor for perceptual category learning

Predicting human performance in perceptual categorization tasks in which category membership is determined by similarity has been historically difficult. This article proposes a novel biologically motivated difficulty measure that can be generalized across stimulus types and category structures. The new measure is compared to 12 previously proposed measures on four extensive data sets that each included multiple conditions that varied in difficulty. The studies were highly diverse and included experiments with both continuous- and binary-valued stimulus dimensions, a variety of different stimulus types, and both linearly and nonlinearly separable categories. Across these four applications, the new measure was the most successful at predicting the observed rank ordering of conditions by difficulty, and it was also the most accurate at predicting the numerical values of the mean error rates in each condition.