Efforts to understand the brain bases of language face the Mapping Problem: At what level
do linguistic computations and representations connect to human neurobiology? We review …

Large, open datasets have emerged as important resources in the field of human
connectomics. In this review, the evolution of data sharing involving magnetic resonance …

Considerable progress has recently been made in natural language processing: deep
learning algorithms are increasingly able to generate, summarize, translate and classify …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …

Several deep neural networks have recently been shown to generate activations similar to
those of the brain in response to the same input. These algorithms, however, remain largely …

Abstract Representations from transformer-based unidirectional language models are
known to be effective at predicting brain responses to natural language. However, most …

Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process
text, and now constitute the backbone of automatic translation, summarization and dialogue …

Neuroscientific studies exploring real-world dynamic perception often overlook the influence
of continuous changes in narrative content. In our research, we utilize machine learning …

The activations of language transformers like GPT-2 have been shown to linearly map onto
brain activity during speech comprehension. However, the nature of these activations …