# Computational and Robotic Models of Early Language Development: A Review

arXiv: https://arxiv.org/abs/1903.10246 · HTML: https://mlsanity.com/paper/1903.10246/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Building Human-like Communicative Intelligence: A Grounded Perspective](https://mlsanity.com/paper/2201.02734/) (2022)
- [Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors](https://mlsanity.com/paper/2603.08359/) (2026)
- [Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots](https://mlsanity.com/paper/2403.19995/) (2024)

## Abstract (arXiv)

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.