Year: 2026
Type: preprint
Abstract: Surprisal and contextual embeddings are both derived from large language models and are widely used to predict neural responses during language comprehension, but it is unclear whether surprisal adds information beyond embeddings. We test this directly: does word surprisal improve out-of-sample prediction of high-gamma ECoG responses after GPT-2 XL contextual embeddings are included? Using public ECoG recordings from natural speech, we fit word-aligned ridge encoding models with baseline stimulus features, GPT-2 XL embeddings, and GPT-2 XL surprisal. Adding one surprisal predictor left held-out correlation essentially unchanged at the primary response window, and the increment stayed within an equivalence margin across alternative lags, residualized surprisal, and frequency-band targets; it did not uniformly meet the same criterion under banded ridge regularization or within labeled temporal subsets. This small increment suggests that surprisal does not act as an independent predictor. It is better understood as a compressed readout of the same broader predictive state that the embeddings already capture.
Author: Takayuki Sakuma
Institution: Soka University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7168622779