The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain's linguistic capabilities. ECoG offers high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas.

The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain's linguistic capabilities. ECoG offers a high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas.

A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations.

Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM).

A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations.

Effective communication hinges on a mutual understanding of word meaning in different contexts. The embedding space learned by large language models can serve as an explicit model of the shared, context-rich meaning space humans use to communicate their thoughts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients.

Shared computational principles for language processing in humans and deep language models.

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG).

The "Narratives" fMRI dataset for evaluating models of naturalistic language comprehension.

The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words).

Neural alignment predicts learning outcomes in students taking an introduction to computer science course.

Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals.

Infant and Adult Brains Are Coupled to the Dynamics of Natural Communication.

Infancy is the foundational period for learning from adults, and the dynamics of the social environment have long been considered central to children's development. Here, we reveal a novel, naturalistic approach for studying live interactions between infants and adults. Using functional near-infrared spectroscopy (fNIRS), we simultaneously and continuously measured the brains of infants ( = 18; 9-15 months of age) and an adult while they communicated and played with each other.

Temperament and peer problems from early to middle childhood: Gene-environment correlations with negative emotionality and sociability.

Based in a transactional framework in which children's own characteristics and the social environment influence each other to produce individual differences in social adjustment, we investigated relationships between children's peer problems and their temperamental characteristics, using a longitudinal and genetically informed study of 939 pairs of Israeli twins followed from early to middle childhood (ages 3, 5, and 6.5). Peer problems were moderately stable within children over time, such that children who appeared to have more peer problems at age 3 tended to have also more peer problems at age 6.