 Another influential early connectionist model was a net trained by Rumelhart and McClelland (1986) to predict the past tense of English verbs. The task is interesting because although most of the verbs in English (the regular verbs) form the past tense by adding the suffix “-ed”, many of the most frequently verbs are irregular (“is” / “was”, “come” / “came”, “go” / “went”). The net was first trained on a set containing a large number of irregular verbs, and later on a set of 460 verbs containing mostly regulars. The net learned the past tenses of the 460 verbs in about 200 rounds of training, and it generalized fairly well to verbs not in the training set. It even showed a good appreciation of “regularities” to be found among the irregular verbs (“send” / “sent”, “build” / “built”; “blow” / “blew”, “fly” / “flew”). During learning, as the system was exposed to the training set containing more regular verbs, it had a tendency to overregularize, i.e., to combine both irregular and regular forms: (“break” / “broked”, instead of “break” / “broke”). This was corrected with more training. It is interesting to note that children are known to exhibit the same tendency to overregularize during language learning. However, there is hot debate over whether Rumelhart and McClelland’s is a good model of how humans actually learn and process verb endings. For example, Pinker and Prince (1988) point out that the model does a poor job of generalizing to some novel regular verbs. They believe that this is a sign of a basic failing in connectionist models. Nets may be good at making associations and matching patterns, but they have fundamental limitations in mastering general rules such as the formation of the regular past tense. These complaints raise an important issue for connectionist modelers, namely whether nets can generalize properly to master cognitive tasks involving rules. Despite Pinker and Prince’s objections, many connectionists believe that generalization of the right kind is still possible (Niklasson &amp; van Gelder 1994).