2008 Special Issue: How language can help discrimination in the Neural Modelling Fields framework

Authors:
José F. Fontanari;Leonid I. Perlovsky
Affiliations:
Instituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970 São Carlos SP, Brazil;Harvard University, 33 Oxford St, Rm 336, Cambridge MA 02138, United States and Air Force Research Laboratory, 80 Scott Drive, Hanscom Air Force Base, MA, United States
Venue:
Neural Networks
Year:
2008

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Abstract

The relationship between thought and language and, in particular, the issue of whether and how language influences thought is still a matter of fierce debate. Here we consider a discrimination task scenario to study language acquisition in which an agent receives linguistic input from an external teacher, in addition to sensory stimuli from the objects that exemplify the overlapping categories that make up the environment. Sensory and linguistic input signals are fused using the Neural Modelling Fields (NMF) categorization algorithm. We find that the agent with language is capable of differentiating object features that it could not distinguish without language. In this sense, the linguistic stimuli prompt the agent to redefine and refine the discrimination capacity of its sensory channels.