Interpreting Brain Responses to Language with Sparse Features from Language Models
A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs) have emerged as a powerful tool to address this challenge, but studies relating biological and artificial representations are often criticized as relating one black box to another. The present work introduces Augmented Sparse Encoding Models, an encoding framework that replaces dense LM hidden states with hierarchically-organized sparse autoencoder (SAE) features, while explicitly including surprisal as a predictor. Using this approach, we (i) produce interpretations of neural responses and (ii) test whether model-brain alignment reflects...
| Authors | Michael A. Lepori, Kendrick Kay, Greta Tuckute |
|---|---|
| Primary category | cs.CL |
| Published | 5 June 2026 |
| Last updated at source | 5 July 2026 |
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