Revealing the multidimensional mental representations of natural objects underlying human similarity judgments

Objects can be characterized according to a vast number of possible criteria (e.g. animacy, shape, color, function), but some dimensions are more useful than others for making sense of the objects around us. To identify these “core dimensions” of object representations, we developed a data-driven computational model of similarity judgments for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgments and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behavior and reflected typicality judgments of those categories. Further, humans can accurately rate objects along these dimensions, highlighting their interpretability and opening up a way to generate similarity estimates from object dimensions alone. Collectively, these results demonstrate that human similarity judgments can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behavior.