Semantics: What Else?

The previous chapters have given samples of neurosemantics addressing specific semantic phenomena, using a unified neurocomputational approach. Much of the complexity of real language has been neglected, and in this chapter other developments will be presented, which fill in some of gaps that remain. The models presented in this chapter are not original developments of the authors, their selection is due to their theoretical grounds and their motivations, which are perfectly in line with the neurosemantics enterprise, as we have defined it here. In particular, Friedemann Pulvermüller and his associates have attempted to answer questions in semantics by developing neurocomputational models, based on brain representational mechanisms, as those here described in Chap. 3, and compatible with the organization of brain areas involved in language processing. Section 7.1 has shown how naturally the first instance of syntax emerges from sequences of adjectives and nouns. From that first step into a complete management of syntax, the brain needs to organize circuits to handle higher order combinatorial information, something simulated by Pulvermüller and called discrete combinatorial neuronal assemblies. With this computational architecture it is possible to explain main syntactic structure, such as that of verbal phrases. Much more is needed, but what has been achieved so far, the mathematical frameworks laid down, the definition of the research project, strongly suggest that neurosemantics today is feasible, and is one of the deeper and most appropriate efforts in explaining linguistic meaning.