Language is a signature human cognitive skill, but the precise computations that support language understanding remain unknown. This study aims to combine high-quality human neural data obtained through intracranial recordings with advances in computational modeling of human cognition to shed light on the construction and understanding of speech.
The neural architecture of language is the foundation for the highest form of human interaction. Prior work has identified a network of frontal and temporal brain areas that selectively support language processing, but the precise computations that underlie our ability to extract meaning from sequences of words have remained unknown. The standard approaches in human cognitive neuroscience lack the spatial and temporal resolution necessary for precise comparisons to computational models. To bridge this gap in knowledge, neural responses to language stimuli will be collected from epileptic patients undergoing intracranial monitoring. Overall, these data will be used to identify cortical maps of different linguistic manipulations and to better understand properties of the human language network.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
40
Participants will listen to sentences and stories while neural data are recorded through electrodes placed for clinical purposes.
Massachusetts General Hospital
Boston, Massachusetts, United States
RECRUITINGCortical maps of linguistic responses
By using sEEG intracranial recordings of the brain, EEG power in frequency bands will reflect cortical maps of responses to different linguistic manipulations, informing the functional organization of the human language system. Power is measured in arbitrary units; higher power reflects greater activity at the investigated frequency.
Time frame: Throughout intracranial monitoring period, up to approximately 10 days
Neural time-courses during naturalistic language comprehension
Time-courses of neural response to language across diverse parts of the language network. These data will be used to predict across-time variation in response strength from the properties of linguistic input.
Time frame: Throughout intracranial monitoring period, up to approximately 10 days
Brain scores for diverse artificial neural network (ANN) language models
Human neural data will be compared to ANN language models to test how well these models predict human responses to language and why. There are no minimum or maximum scores. Higher values mean better model predictivity (i.e., a better match between model representations and neural responses).
Time frame: Throughout intracranial monitoring period, up to approximately 10 days
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