Chuanji Gao | Searcharxiv

arXiv2026

Multifaceted neural representation of words in naturalistic language

Understanding how the brain represents the multifaceted properties of words in context is essential for explaining the neural architecture of human language. Here, we combine large-scale psycholinguistic modeling with naturalistic fMRI to uncover the latent structure of word properties and their neural representations during narrative comprehension. By analyzing 106 psycholinguistic variables across 13,850 English words, we identified eight interpretable latent dimensions spanning lexical usage, word form, phonology orthography mapping, sublexical regularity, and semantic organization. These factors robustly predicted behavioral performance across lexical decision, naming, recognition, and semantic judgment tasks, demonstrating their cognitive relevance. Parcel-based and multivariate fMRI analyses of narrative listening revealed that these latent dimensions are encoded in overlapping yet functionally differentiated cortical systems. Multidimensional scaling and hierarchical clustering analyses further identified four interacting subsystems supporting sensorimotor grounding, controlled semantic retrieval, resolution of lexical competition, and contextual episodic integration. Together, these findings provide a unified neurocognitive framework linking fundamental lexical psycholinguistic dimensions to distributed cortical systems engaged during naturalistic language comprehension.

q-bio.NC↗

arXiv2025

Model Selection with Regression and Representational Similarity Analysis for Linear and Nonlinear Data

In cognitive psychology and neuroscience, adjudicating between competing theoretical models is a common methodological challenge. Researchers often rely on either first-order direct mapping approaches (e.g., linear regression) or second-order abstraction methods (e.g., Representational Similarity Analysis [RSA]). However, it remains unclear whether or how the nature of the underlying data and feature characteristics affect the performance of these methods. Here, we systematically evaluated regression, RSA, and Pattern Component Modeling (PCM) across distinct data-generating schemes, including first-order linear mappings and geometry-to-first-order transformations with either linear or nonlinear sigmoid readouts, using both univariate behavioral and multivariate fMRI spatial-pattern simulations. Our results suggest that the relative performance of these methods depends on the underlying generative mechanism. Under linear generative assumptions, regression and PCM showed higher model-selection accuracy than RSA. Under nonlinear but order-preserving transformations, rank-based RSA showed an advantage over regression and PCM. We also found that feature multicollinearity affected these methods differently across generative schemes, and that orthogonalizing the predictor space via principal component analysis (PCA) reduced several collinearity-related differences. Finally, analyses of empirical datasets were consistent with the simulation results under approximately linear conditions, with regression showing clearer model discrimination than RSA. Overall, these findings suggest that the relative performance of regression, RSA, and PCM depends on the form of the mapping between features and responses, as well as on the structure of the feature space.

stat.ME↗