Abstract

IntroductionBased 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.MethodsWe 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.ResultsRegardless 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.ConclusionsInvestigation 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.

Highlights

  • 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, alternating unsupervised learning and supervised learning in language acquisition

  • According to tionships between unsupervised learning (USL) and supervised learning (SL) have been defined according to the Rumelhart and Norman (1976), a schema can be updated according theory of “schema” (Bartlett, 1932; Schank & Abelson, 1977), which to three modes of learning: “accretion,” “tuning,” and “restructuring.”

  • By exposing participants fMRI sessions should be designed to meet requirements of simplicto a continuous stream of language stimuli, we explore what synity and distinctness in terms of syntactic rule and phonetic regularchronization across brain areas supports more or less self-­paced ity

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Summary

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
Conclusions
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