Language acquisition and category discrimination in the Modeling Field Theory framework.
ABSTRACT 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.
Article: Spontaneous Lexicon Change[show abstract] [hide abstract]
ABSTRACT: The paper argues that language change can be explained through the stoehasticity observed in real-world natural language use. This the- sis is demonstrated by modeling language use through language games played in an evolv- ing population of agents. We show that the artificial languages which the agents sponta- neously develop based on self-organisation, do not evolve even if the population is changing. Then we introduce stochasticity in language use and show that this leads to a constant innova- tion (new forms and new form-meaning associ- ations) and a maintenance of variation in the population, if the agents are tolerant to varia- tion. Some of these variations overtake existing linguistic conventions, particularly in changing populations, thus explaining lexicon change.01/1998;
Conference Proceeding: Meaning creation and communication in a community of agents.[show abstract] [hide abstract]
ABSTRACT: The emergence of communication is studied in a scenario where agents endowed with distinct object-meaning mappings learn from scratch signal-meaning associations (i.e., communication codes) that allow them to identify the objects in their environment. Meanings are created through the Modeling Field Theory categorization mechanism, and learning is based on two variants of the obverter procedure, in which the agents may or may not receive feedback about the success of the communication episodes. We show that in the unsupervised learning scheme the agents fail to develop ideal communication codes, whereas success is guaranteed in the supervised scheme provided the size of the repertoire of signals is sufficiently large, though only a few signal are actually used in the code. Thus the mere ability to produce and observe different signals bears on the quality of the evolved communication codes.Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16-21 July 2006; 01/2006
Conference Proceeding: Integrated Emotions, Cognition, and Language.[show abstract] [hide abstract]
ABSTRACT: Recent developments in neural and cognitive sciences resulted in appreciation of emotions as inseparable part of intelligence. Emotions evaluate conceptual contents of cognition for instinctual satisfactions. This paper describes aesthetic emotions related to the knowledge instinct. It analyzes the role of emotions in language, develops a hypothesis that aesthetic emotions integrate cognition and language within a wholeness of psyche, and discusses possible brain mechanisms.Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16-21 July 2006; 01/2006