Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning
This addresses the open problem of understanding early language development in infants, particularly for mono- and bilingual contexts, though it is incremental as it builds on existing predictive coding and continual learning methods.
The paper tackles the problem of modeling infant speech sound acquisition by proposing a small generative neural network with continual learning based on predictive coding, demonstrating that second language acquisition after the critical period amplifies challenges, replicating perceptual narrowing effects.
Understanding how infants perceive speech sounds and language structures is still an open problem. Previous research in artificial neural networks has mainly focused on large dataset-dependent generative models, aiming to replicate language-related phenomena such as ''perceptual narrowing''. In this paper, we propose a novel approach using a small-sized generative neural network equipped with a continual learning mechanism based on predictive coding for mono-and bilingual speech sound learning (referred to as language sound acquisition during ''critical period'') and a compositional optimization mechanism for generation where no learning is involved (later infancy sound imitation). Our model prioritizes interpretability and demonstrates the advantages of online learning: Unlike deep networks requiring substantial offline training, our model continuously updates with new data, making it adaptable and responsive to changing inputs. Through experiments, we demonstrate that if second language acquisition occurs during later infancy, the challenges associated with learning a foreign language after the critical period amplify, replicating the perceptual narrowing effect.