 Research on nets that clearly demonstrate strong systematicity has continued. Jansen and Watter (2012) provide a good summary of more recent efforts along these lines, and propose an interesting basis for solving the problem. They use a more complex architecture that combines unsupervised self-organizing maps with features of simple recurrent nets. However, the main innovation is to allow codes for the words being processed to represent sensory-motor features of what the words represent. Once trained, their nets displayed very good accuracy in distinguishing the grammatical features of sentences whose words never even appeared in the training set. This may appear to be cheating since the word codes might surreptitiously represent grammatical categories, or at least they may unfairly facilitate learning those categories. Jansen and Watter note however, that the sensory-motor features of what a word represents are apparent to a child who has just acquired a new word, and so that information is not off-limits in a model of language learning. They make the interesting observation that a solution to the systematicity problem may require including sources of environmental information that have so far been ignored in theories of language learning. This work complicates the systematicity debate, since it opens a new worry about what information resources are legitimate in responding to the challenge. However, this reminds us that architecture alone (whether classical or connectionist) is not going to solve the systematicity problem in any case, so the interesting questions concern what sources of supplemental information are needed to make the learning of grammar possible.