Language acquisition and category discrimination in the Modeling Field Theory framework

We propose a categorization 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 make up the environment. The agent is endowed with the modeling field theory (MFT) categorization mechanism, which enables it to identify overlapping categories from the exposition to hundreds of examples. Rather remarkably, 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.