The human language processing system straightens natural speech

Authors

Xu, J., Nguyen, T. D., Tang, J., Huth, A. G., Goris, R. L. T.

Abstract

Large language models trained on next-word prediction have impressive linguistic capabilities. This suggests that the goal of temporal prediction is essential to language processing, but how this goal impacts the structure of speech representations in the human brain remains unknown. Here, we test the hypothesis that prediction is facilitated by the temporal straightening of representational trajectories along the speech processing hierarchy. We developed a methodology for measuring the curvature of these trajectories using fMRI. Our method exploits a previously unknown connection between the timescale of single-unit responses and the curvature of population trajectories. We examined brain responses of subjects listening to natural speech. Response trajectories were most curved in lower-level auditory areas and progressively straightened along the cortical hierarchy. We presented the same speech stimuli and perturbed versions thereof to wavLM---a speech representation model that is well aligned with human brain responses---and found that hierarchical straightening effects are strongest for stimuli whose statistical structure resembles natural speech. Together, our results establish a direct connection between the goal of temporal prediction, the geometry of neural speech representations, and the cortical hierarchy of representational timescales.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 03 Jul 2026.

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