A Semantic Model to Study Neural Organization of Language in Bilingualism

Abstract EN

A neural network model of object semantic representation is used to simulate learning of new words from a foreign language.

The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation.

Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization.

Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule.

In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase.

Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one.

A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons.

Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language.

Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se.

In this case, a conflict between words can occur, requiring a higher-level decision mechanism.