UC Merced

Similarity and categorization are central phenomena in cognitive science. Despite its relevance, similarity is a poorly understood process. The current work proposes a new theory of similarity and how to unify it with categorization. Similarity is defined as the overlap of neuronal population codes that represent features of objects being compared. The experiments investigate how perceptual and conceptual information help to constrain similarity. A neural network model is designed that accurately predicts human performance on similarity judgments.