Bringing Computational Models of Word Naming down to the Item Level

Early noncomputational models of word recognition have typically attempted to account for effects of categorical factors such as word frequency (high vs. low) and spelling-to-sound regularity (regular vs. irregular). More recent computational models that adhere to general connectionist principles hold the promise of being sensitive to underlying item differences that are only approximated by these categorical factors. In contrast to earlier models, these connectionist models provide predictions of performance for individual items. In the present study, we used the item-level estimates from two connectionist models (Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg & McClelland, 1989) to predict naming latencies on the individual items on which the models were trained. The results indicate that the models capture, at best, slightly more variance than simple log frequency and substantially less than the combined predictive power of log frequency, neighborhood density, and orthographic length. The discussion focuses on the importance of examining the item-level performance of word-naming models and possible approaches that may improve the models' sensitivity to such item differences.