Early learned words are recognized and produced faster than later learned words. The authors showed that such age of acquisition effects are a natural property of connectionist models trained by back-propagation when patterns are introduced at different points into training and learning of early and late patterns is cumulative and interleaved. Analysis of hidden unit activations indicated that the age of acquisition effect reflects a gradual reduction in network plasticity and a consequent failure to differentiate late items as effectively as early ones. Further simulations examined the effects of vocabulary size, learning rate, sparseness of coding, use of a modified learning algorithm, loss of early items, acquisition of very late items, and lesioning the network. The relationship between age of acquisition and word frequency was explored, including analyses of how the relative influence of these factors is modulated by introducing weight decay.