arXiv subjects

Gangyi Feng

Publications and source records attributed to Gangyi Feng.

5 recordsLinked to original sources

arXiv2026

Large language models selectively converge with human-shared neural semantic representations

Interpersonal communication requires building shared semantics that enable listeners to understand speakers' meanings from their unfolding language, but the dimensional structure of this shared neural representation remains unclear. LLMs increasingly approximate human language capability and neural responses, raising the question of whether they capture the same semantic structure shared between human brains. Here, we combined storytelling-listening pseudo-hyperscanning MEG with dimension-resolved interbrain encoding modeling to compare human- and LLM-derived accounts of shared neural semantic representations. Content words from the speaker's narratives were rated by humans and five recent LLMs along ten semantic dimensions (i.e., perception, motor, space, time, socialness, animacy, emotion, attention, causality, and drive). We tested whether these dimensions explained speaker-listener neural synchronization (NS) beyond acoustic and phonological features. Both human- and LLM-derived semantic spaces explained NS, but these shared semantics are better characterized as a multidimensional neural structure rather than a single global signal. These patterns also predicted individual differences in listeners' story comprehension, linking neural alignment to cognition. However, comparable overall prediction concealed systematic differences in representational geometry. Larger LLMs aligned more closely and showed greater overlap with humans in semantic structure and NS, but this was incomplete and dimension-dependent. The largest divergences emerged for dimensions closely tied to agency, affect, and social experience. These findings show that LLMs capture substantial components of human shared neural semantics, but their alignment is selective. Larger or more capable models improve the approximation, whereas socially and affectively grounded dimensions are captured only partially.

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arXiv2026

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

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.

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arXiv2025

Multi-network Topology Underlying Individual Language Learning Success

Adult language learning varies greatly among individuals. Traditionally associated with frontotemporal language regions, this variability is increasingly seen as stemming from distributed brain networks. However, the role of these networks and their topological organization in explaining these differences remains unclear. We hypothesize that graph-theory-based network analysis of intrinsic multimodal connectivities across multiple networks explains overall and component-specific variations in language learning. We tested this in 101 healthy adults who underwent resting-state fMRI, structural MRI, and diffusion tensor imaging before seven days of six artificial language training tasks. We identified one dominant general learning component shared across tasks and five task-specific ones. Cross-validated predictive models used multimodal multi-network graph-theoretic metrics to predict final learning outcomes (LO) and rates (LR). We significantly predicted the LO and LR of the general component, which were primarily contributed by dorsal attention and frontoparietal networks. Nodal local efficiency was the most consistent predictor, with additional contributions from node clustering coefficient and network centrality for LR, highlighting local robustness, mesoscale network segregation, and global influence in explaining individual differences. Only task-specific word learning LO was predictable, relying on default mode and frontoparietal hubs with high betweenness centrality and efficiency. These findings demonstrate that intrinsic network topologies underlie differences in language learning success, supporting a multiple-systems hypothesis in which attentional-control networks interact with default and subcortical systems to shape learning trajectories. This advances mechanistic understanding and paves the way for personalized language education.

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arXiv2025

Driving factors of auditory category learning success

Our brain learns to update its mental model of the environment by abstracting sensory experiences for adaptation and survival. Learning to categorize sounds is one essential abstracting process for high-level human cognition, such as speech perception, but it is also challenging due to the variable nature of auditory signals and their dynamic contexts. To overcome these learning challenges and enhance learner performance, it is essential to identify the impact of learning-related factors in developing better training protocols. Here, we conducted an extensive meta-analysis of auditory category learning studies, including a total of 111 experiments and 4,521 participants, and examined to what extent three hidden factors (i.e., variability, intensity, and engagement) derived from 12 experimental variables contributed to learning success (i.e., effect sizes). Variables related to intensity and training variability outweigh others in predicting learning effect size. Activation likelihood estimation (ALE) meta-analysis of the neuroimaging studies revealed training-induced systematic changes in the frontotemporal-parietal networks. Increased brain activities in speech and motor-related auditory-frontotemporal regions and decreased activities in cuneus and precuneus areas are associated with increased learning effect sizes. These findings not only enhance our understanding of the driving forces behind speech and auditory category learning success, along with its neural changes, but also guide researchers and practitioners in designing more effective training protocols that consider the three key aspects of learning to facilitate learner success.

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arXiv2025

Language-specific Tonal Features Drive Speaker-Listener Neural Synchronization

Verbal communication transmits information across diverse linguistic levels, with neural synchronization (NS) between speakers and listeners emerging as a putative mechanism underlying successful exchange. However, the specific speech features driving this synchronization and how language-specific versus universal characteristics facilitate information transfer remain poorly understood. We developed a novel content-based interbrain encoding model to disentangle the contributions of acoustic and linguistic features to speaker-listener NS during Mandarin storytelling and listening, as measured via magnetoencephalography (MEG). Results revealed robust NS throughout frontotemporal-parietal networks with systematic time lags between speech production and perception. Crucially, suprasegmental lexical tone features (tone categories, pitch height, and pitch contour), essential for lexical meaning in Mandarin, contributed more significantly to NS than either acoustic elements or universal segmental units (consonants and vowels). These tonal features generated distinctive spatiotemporal NS patterns, creating language-specific neural "communication channels" that facilitated efficient representation sharing between interlocutors. Furthermore, the strength and patterns of NS driven by these language-specific features predicted communication success. These findings demonstrate the neural mechanisms underlying shared representations during verbal exchange and highlight how language-specific features can shape neural coupling to optimize information transfer during human communication.

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