Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Simultaneous Noun and Category Learning via Cross-Situational Statistics

Abstract Previous research shows that people can acquire an impressive number of word-referent pairs after viewing a series of ambiguous trials by accumulating co-occurrence statistics (e.g., Yu & Smith, 2006). The present study extends the cross-situational word learning paradigm, which has previously dealt only with noun acquisition, and shows that humans can concurrently acquire nouns and adjectives (i.e., a natural category with a distinctive, unifying feature). Furthermore, participants are able to learn ad hoc categories of referents consistently cooccurring with a label, while simultaneously learning instance labels. Thus, humans demonstrate an impressive ability to simultaneously apprehend regularities at multiple levels in their environment

Computational and Robotic Models of Early Language Development: A Review

International audienceWe review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments

Learning Nouns with Domain-General Associative Learning Mechanisms

Associative learning has been meticulously studied in many species, and diverse effects have been explained using a handful of basic assumptions and mechanisms. Human language acquisition proceeds remarkably quickly and is of great interest, but is arguably more difficult to capture under the microscope. Nonetheless, empirical investigations have led researchers to theorize a variety of language learning principles and constraints. While there may indeed be language-specific learning mechanisms that are distinct from more universal associative learning mechanisms, we seek to explain some basic principles of language acquisition using domain-general mechanisms. Using an experiment and a model, we show how the principles of mutual exclusivity—an assumption of 1-to-1 word-object mappings, contrast, and other constraints related to fast mapping may stem from attention mechanisms attributed to associative learning effects such as blocking and highlighting, but directed by competing biases for familiar and unfamiliar pairs instead of surprise

Intervention Dialogue

Scripted dialogue for intervention meant to improve children's diversity-based inductive reasoning about natural categories by demonstrating the presence of correlated hidden and observable features in the main clusters of birds (i.e., songbirds, water birds, and raptors)