Global-Before-Basic Object Categorization in Connectionist Networks and 2-Month-Old Infants

A 3-layered backpropagation connectionist network, configured as an autoassociator, learned to form global (e.g., mammal) before basic-level (e.g., cat) category representations from perceptual input. To test the predicted global-to-basic order of category learning of the network, 2-month-olds were administered the familiarization/novelty-preference procedure and examined for representation of global and basic-level categories. Infants formed a global category representation for mammals that excluded furniture but not a basic-level representation for cats that excluded elephants, rabbits, or dogs. The empirical results are consistent with the global-to-basic learning sequence observed in the network simulations.