  Communicative interactions involve a kind of procedural knowledge that is
used by the human brain for processing verbal and nonverbal inputs and for
language production. Although considerable work has been done on modeling human
language abilities, it has been difficult to bring them together to a
comprehensive tabula rasa system compatible with current knowledge of how
verbal information is processed in the brain. This work presents a cognitive
system, entirely based on a large-scale neural architecture, which was
developed to shed light on the procedural knowledge involved in language
elaboration. The main component of this system is the central executive, which
is a supervising system that coordinates the other components of the working
memory. In our model, the central executive is a neural network that takes as
input the neural activation states of the short-term memory and yields as
output mental actions, which control the flow of information among the working
memory components through neural gating mechanisms. The proposed system is
capable of learning to communicate through natural language starting from
tabula rasa, without any a priori knowledge of the structure of phrases,
meaning of words, role of the different classes of words, only by interacting
with a human through a text-based interface, using an open-ended incremental
learning process. It is able to learn nouns, verbs, adjectives, pronouns and
other word classes, and to use them in expressive language. The model was
validated on a corpus of 1587 input sentences, based on literature on early
language assessment, at the level of about 4-years old child, and produced 521
output sentences, expressing a broad range of language processing
functionalities.
