People are insensitive to within-category feature correlations in categorization

This work compares two types of psychological similarity in categorization. Similarity is a central component of categorization theories. Exemplar theories, for instance, assume that people categorize new exemplars based on their similarity to previous category members. Traditionally, the underlying psychological similarity is based on the sum of two exemplars' squared feature value differences (Euclidean similarity). The Euclidean similarity, however, ignores the distribution of exemplars within categories by assuming uncorrelated features within categories. The Mahalanobis similarity, in turn, extends the Euclidean similarity by accounting for within-category feature correlations. Results from machine learning have shown that in categorization problems involving correlated features within categories, the Mahalanobis similarity can outperform the Euclidean similarity. On the empirical side, results from psychology indicate that people can be sensitive to within-category feature correlations: Some findings suggest a general sensitivity for within-category feature correlations, yet others have argued that this sensitivity depends on the category structure, task format, and amount of training. The present work rigorously tested the correlation-insensitive Euclidean similarity against the correlation-sensitive Mahalanobis similarity to investigate if people use within-category feature correlations in categorization.