 the singular “man” must agree with the verb “runs” despite the intervening plural nouns (“dogs”, “cats”) which might cause the selection of “run”. One of the important features of Elman’s model is the use of recurrent connections. The values at the hidden units are saved in a set of so called context units, to be sent back to the input level for the next round of processing. This looping back from hidden to input layers provides the net with a rudimentary form of memory of the sequence of words in the input sentence. Elman’s nets displayed an appreciation of the grammatical structure of sentences that were not in the training set. The net’s command of syntax was measured in the following way. Predicting the next word in an English sentence is, of course, an impossible task. However, these nets succeeded, at least by the following measure. At a given point in an input sentence, the output units for words that are grammatical continuations of the sentence at that point should be active and output units for all other words should be inactive. After intensive training, Elman was able to produce nets that displayed perfect performance on this measure including sentences not in the training set. The work of Christiansen and Chater (1999a) and Morris, Cottrell, and Elman (2000) extends this research to more complex grammars. For a broader view of progress in connectionist natural language processing see summaries by Christiansen and Chater (1999b), and Rohde and Plaut (2003).