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How language can help discrimination in the Neural Modelling Fields framework

Authors
Journal
Neural Networks
0893-6080
Publisher
Elsevier
Publication Date
Volume
21
Identifiers
DOI: 10.1016/j.neunet.2007.12.007
Keywords
  • Acquisition Of Language
  • Clustering Algorithms
  • Neural Modeling Fields
Disciplines
  • Computer Science
  • Linguistics

Abstract

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.

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