Human Language Acquisition

Human language acquisition research seeks to understand how humans learn language, focusing on the interplay of social interaction, multimodal input (visual and auditory), and limited data exposure. Current investigations utilize various neural network architectures, including transformer-based models and generative adversarial networks, to simulate aspects of language learning, often employing techniques like contrastive learning and reinforcement learning to model feedback and interaction. These computational models offer valuable insights into the mechanisms underlying human language development, informing theories of cognitive development and potentially improving the design of educational tools and language-learning technologies.