RECENTS ADVANCES IN NEUROBIOLOGICAL DOMAINS: STATISTICAL LEARNING AND PREDICTIVE PROCESSING FOR LANGUAGE

Abstract

Research in language neurobiology in early childhood is key to understanding mature language networks. Thanks to recent non-invasive neuroimaging techniques, such as fNIRS, we have an upgraded knowledge about the developing brain that allows for the identification of early markers of language outcomes. This narrative review aims to look at different domains to unveil experiments and techniques that pave the way for predictive processing in language development. In the domain of statistical learning, we need to specify patterns of change that allow for the ability to extract environmental regularities in early development, while being aware that the long-term storage of statistical learning in children remains poorly understood. In relation to contextual variables, there is recent research that bears insights into how predictability of caregiver behavior scaffolds the neurodevelopment of specific learning processes. And regarding maturational and developmental constraints, we explore recent findings that expose dissent as to whether children and adults have similar linguistic statistical learning abilities. Henceforth, domains, variables, and constraints are crucial to integrate research with foundational aspects of how segregation tends to develop into integration in high cognitive domains, like memory and language. Of note, the neurodevelopment of core learning, like linguistic abilities, relies on infants’ experiences during key developmental windows. This validates deeper exploration of neurobiological processes that undergird development. In the end, we hope to enable a more thorough appraisal of recent advances on how statistical learning and prediction frameworks influence language outcomes.