  To study how mental object representations are related to behavior, we
estimated sparse, non-negative representations of objects using human
behavioral judgments on images representative of 1,854 object categories. These
representations predicted a latent similarity structure between objects, which
captured most of the explainable variance in human behavioral judgments.
Individual dimensions in the low-dimensional embedding were found to be highly
reproducible and interpretable as conveying degrees of taxonomic membership,
functionality, and perceptual attributes. We further demonstrated the
predictive power of the embeddings for explaining other forms of human
behavior, including categorization, typicality judgments, and feature ratings,
suggesting that the dimensions reflect human conceptual representations of
objects beyond the specific task.
