SCRIBE is a framework that uses a neuroscience-informed semantic space and an ensemble of large language models to generate interpretable, word-level semantic representations of arbitrary text. In a behavioral study with 921 participants, SCRIBE’s representations varied systematically with context and captured 75‑81% of the estimated human noise ceiling. When applied to brain activity recorded while participants listened to naturalistic stories, SCRIBE predicted neural responses in language-selective brain regions better than non-contextual representations and explained unique brain-predictive information beyond syntactic features.
arXiv:2609.10296v1 Announce Type: new Abstract: Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phon...
The study investigates how a large language model (LLaMA2‑7B) aligns with human brain activity during naturalistic language comprehension by using SCRIBE, an interpretable semantic representation of 65 neurocognitively grounded properties. Variance partitioning on fMRI data from seven participants reveals that shared variance between LLaMA2‑7B and SCRIBE dominates across the brain, yet both representations also contribute unique variance. Further analysis shows that concrete semantic properties correlate more strongly with shared variance, while abstract properties do not show increased unique variance, and ablation experiments demonstrate that multiple semantic dimensions cumulatively influence both shared and unique brain-predictive variance.
arXiv:2606. 26880v1 Announce Type: cross Abstract: Language-model representations provide structured, high-dimensional annotations of naturalistic language stimuli and can serve as informative neural predictors during comprehension.
arXiv:2502. 14671v4 Announce Type: replace-cross Abstract: Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what drives this alignment.
arXiv:2610.29162v1 Announce Type: new Abstract: There is a widespread sense that AI-generated text has recently become harder to understand. In this study, we investigate a set of psycholinguistic fe...
Brain2Qwerty v2 is a model that decodes natural sentences from real‑time magnetoencephalography (MEG) recordings, achieving an average word error rate of 39% across 22,000 sentences typed by nine subjects. The model uses character, word, and sentence‑level representations and shows that decoding accuracy improves log‑linearly with more data, narrowing the gap to intracranial brain‑computer interfaces. AI contributes by replacing hand‑crafted event detection with deep learning, fine‑tuning large language models for semantic extraction, and employing AI agents to refine the decoding pipeline through automated code development.
arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.
arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.
The paper explores whether language models encode components that represent narrativity. By examining GPT‑2‑Small, Gemma‑2‑2B, and Llama‑3.1‑8B, the authors identify specific attention heads, MLP neurons, and sparse autoencoder features whose activations correlate with narrative properties. Ablating these components raises next‑token prediction loss more for high‑narrativity texts and diminishes narrativity in open‑ended generations, with the strongest effects seen in story completion and narrative question answering tasks.
arXiv:2605. 28969v2 Announce Type: replace-cross Abstract: If an AI agent makes decisions on a person's behalf, those decisions must align with its user.
arXiv:2610.26927v1 Announce Type: cross Abstract: Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content. The neural basis of this cross-language semant...
The paper investigates how semantic meaning is arranged locally within large language models. By using two controlled probes—one cross‑lingual and one refinement probe—on four decoder‑only LLMs, it finds that after removing dominant variance directions, sentence pairs rank by semantic relatedness, semantic factors cluster around an invariant core with structured variation, refinement tightens neighborhoods, and sentence‑level versus token‑local readouts reveal complementary geometric patterns. These results suggest that LLM semantics form local anisotropic neighborhoods that contract when meaning is refined.