Predictive and feedback signals differently shape the formation of group-level and individualized language representations

TL;DR

This study investigates how predictive and feedback signals influence language learning and neural representations, revealing distinct roles in shaping group-level and individual differences through fMRI and transformer models.

Contribution

It introduces a multi-signal learning model showing prediction influences common neural architecture, while feedback explains individual learning differences.

Findings

Prediction-based models explain most neural variance at the group level.

Both prediction and feedback signals show a shift from sensory to higher-order processing.

Feedback-related neural patterns better predict individual learning outcomes.

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.