Task-induced brain functional connectivity as a representation of schema for mediating unsupervised and supervised learning dynamics in language acquisition

• Authors: Hiroyuki Akama, Tokyo Institute of Technology; Yixin Yuan, Children's Healthcare of Atlanta; Shunji Awazu, Jissen Women's University
• Journal Title: Brain and Behavior
• Volume: Volume 11, Number 6
• Publisher: (publisher) | 2021-06-01, Pages e02157-e02157
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1002/brb3.2157
• Subjects: Health Sciences, Human Development; Health Sciences, Rehabilitation and Therapy
• Keywords: artificial language grammar; default mode network; functional connectivity; language learning; schema theory; supervised learning; unsupervised learning; Brain; Brain Mapping; Female; Humans; Language; Language Development; Magnetic Resonance Imaging; Male; Supervised Machine Learning

Abstract

Introduction: Based on the schema theory advanced by Rumelhart and Norman, we shed light on the individual variability in brain dynamics induced by hybridization of learning methodologies, particularly alternating unsupervised learning and supervised learning in language acquisition. The concept of “schema” implies a latent knowledge structure that a learner holds and updates as intrinsic to his or her cognitive space for guiding the processing of newly arriving information. Methods: We replicated the cognitive experiment of Onnis and Thiessen on implicit statistical learning ability in language acquisition but included additional factors of prosodic variables and explicit supervised learning. Functional magnetic resonance imaging was performed to identify the functional network connections for schema updating by alternately using unsupervised and supervised artificial grammar learning tasks to segment potential words. Results: Regardless of the quality of task performance, the default mode network represented the first stage of spontaneous unsupervised learning, and the wrap-up accomplishment for successful subjects of the whole hybrid learning in concurrence with the task-related auditory language networks. Furthermore, subjects who could easily “tune” the schema for recording a high task precision rate resorted even at an early stage to a self-supervised learning, or “superlearning,” as a set of different learning mechanisms that act in synergy to trigger widespread neuro-transformation with a focus on the cerebellum. Conclusions: Investigation of the brain dynamics revealed by functional connectivity imaging analysis was able to differentiate the synchronized neural responses with respect to learning methods and the order effect that affects hybrid learning.