Liat Hasenfratz

The paper develops an agent-based model of early language acquisition using the First 1,000 Days dataset, showing that children's language learning can be simulated with a learning agent that discovers speech units and words, mirroring developmental trajectories.

The 1kD project collects and analyzes ultra-dense longitudinal data of infant development, revealing stable household-specific lexical structures not captured by sparse sampling.

The paper introduces a dataset of ECoG recordings from nine participants listening to a podcast, along with linguistic features and tutorials for analysis.

The paper shows that linguistic embedding spaces can capture neural alignment during conversations, revealing shared meaning between speakers and listeners.

This study introduces a neural measure called neural alignment to predict learning outcomes by comparing students' brain activity to experts and peers in a computer science course.

The paper introduces a benchmark dataset of fMRI data from human subjects listening to naturalistic stories for evaluating language and narrative comprehension models.

In nine epilepsy patients, cortical activity carries next-word information up to 800 ms before onset, tracks GPT-2 surprise after onset, and fits GPT-2 contextual embeddings better than static ones.

This study examines neural coupling during real-time interactions between infants and adults using fNIRS, finding increased synchronization and tracking of mutual gaze, emotion, and speech prosody.