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Found 8 papers, 1 papers with code
Probing Brain Context-Sensitivity with Masked-Attention Generation
Two fundamental questions in neurolinguistics concerns the brain regions that integrate information beyond the lexical level, and the size of their window of integration.
Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context
A fundamental question in neurolinguistics concerns the brain regions involved in syntactic and semantic processing during speech comprehension, both at the lexical (word processing) and supra-lexical levels (sentence and discourse processing).
Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps
Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impressive performance on various linguistic tasks.
Toward a realistic model of speech processing in the brain with self-supervised learning
These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain.
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal
This paper describes a method of variable beam size inference for Recurrent Neural Network Grammar (rnng) by drawing inspiration from sequential Monte-Carlo methods such as particle filtering.
Entropy Reduction correlates with temporal lobe activity
Using the Entropy Reduction incremental complexity metric, we relate high gamma power signals from the brains of epileptic patients to incremental stages of syntactic analysis in English and French.
