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.
Human behavioral judgments are used to obtain sparse and interpretable representations of objects that generalize to other tasks This paper describes a large-scale experiment on human object/sematic representations and a model of such representations. This paper develops a new representation system for object representations from training on data collected from odd-one-out human judgements of images. A new approach to learn a sparse, positive, interpretable semantic space that maximizes human similarity judgements by training to specifically maximize the prediction of human similarity judgements.