Although there is no real output from the model, the model can be evaluated by directly assessing the internal representation. Proposition nodes form the basis of the representation. Modifier p-nodes have an additional node, for binding relative clauses. Concept nodes allow the representation of input words, and each of these is connected to the input layer with a trainable connection. Furthermore, each action concept is connected, via the binding nodes to each action theme site, and object concepts are similarly bound to agent and patient theme site.
All training is done with a simple Hebbian algorithm.
Provided the network can successfully represent input sentences with words in novel syntactic positions, the model can be said to have achieved the cognitive goal - that of semantic systematicity. Memory Obviously the model needs to retain the structure of the current sentence. Additionally, the model must retain information on the general apparent structure of sentences. Clearly the memory for the bindings of words and concepts is realized in the form of the strengths of connections between the input and semantic layers. The case of recalling general sentence sequence is less clear.