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 asWilson-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.

M.Ursino, C. Cuppini, E.Magosso (2010). A Semantic Model to Study Neural Organization of Language in Bilingualism. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2010, 1-10 [10.1155/2010/350269].

A Semantic Model to Study Neural Organization of Language in Bilingualism

URSINO, MAURO;CUPPINI, CRISTIANO;MAGOSSO, ELISA
2010

Abstract

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 asWilson-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.
2010
M.Ursino, C. Cuppini, E.Magosso (2010). A Semantic Model to Study Neural Organization of Language in Bilingualism. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2010, 1-10 [10.1155/2010/350269].
M.Ursino; C. Cuppini; E.Magosso
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/94119
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