Predictive and feedback signals differently shape the formation of group-level and individualized language representations
Shuguang Yang, Shaoyun Yu, Xin Jiang, Suiping Wang, Gangyi Feng
TLDR
This study shows that prediction shapes group-level language learning, while feedback explains individual differences in adult language acquisition.
Key contributions
- Tracked 102 adults learning an artificial language over 7 days with fMRI and corrective feedback.
- Found prediction-focused models best explained group-level neural variance in language learning.
- Showed feedback-related neural patterns were most predictive of individual learning outcomes.
- Both prediction and feedback models showed brain-model alignment shift to higher-order networks.
Why it matters
This paper provides a novel multi-signal model for adult language learning, distinguishing the roles of prediction and feedback. It offers insights into the neural mechanisms underlying both common learning architectures and individual differences, which could inform more effective language education strategies.
Original Abstract
Adults vary greatly in how effectively they learn a new language, but the signals driving the learning processes and individual differences remain unclear. Over seven days, we tracked behavioral learning and collected fMRI data from 102 adults as they learned an artificial language with corrective feedback. We trained matched transformer models with prediction, feedback, or combined objectives and compared their internal representations to brain activity. Representations derived from the prediction-focused model accounted for the largest share of unique neural variance at the group level, despite the human task being feedback-based. Throughout model training, both objectives showed a shift in brain-model alignment from sensory to higher-order language and associative networks, indicating abstraction processing. Conversely, neural patterns related to the feedback model were most useful for predicting individual generalization outcomes on Day 7. These findings support a multi-signal model of adult language learning, in which prediction shapes a common neural learning architecture across learners, whereas feedback-related mechanisms better explain individual differences over time.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.