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Found 7 papers, 1 papers with code
MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories.
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.
Long-range and hierarchical language predictions in brains and algorithms
Predictive coding theory offers a potential explanation to this discrepancy: while deep language algorithms are optimized to predict adjacent words, the human brain would be tuned to make long-range and hierarchical predictions.
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics.
Disentangling Syntax and Semantics in the Brain with Deep Networks
The activations of language transformers like GPT-2 have been shown to linearly map onto brain activity during speech comprehension.
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Third, learning to process phonetically-related speech inputs (i. e., Dutch vs English) leads deep nets to reach higher levels of brain-similarity than learning to process phonetically-distant speech inputs (i. e. Dutch vs Bengali).
Measuring causal influence with back-to-back regression: the linear case
Identifying causes from observations can be particularly challenging when i) potential factors are difficult to manipulate individually and ii) observations are complex and multi-dimensional.
