About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
clinical indications to proceed with intracranial monitoring involving the left cerebral hemisphere, as determined by a multidisciplinary epilepsy surgery team
the ability to comply with test directions and provide informed consent
between ages 18 - 85
Disqualifiers
inability to understand or perform the task outlined in the protocol, or who are unwilling or unable to participate
Trial design
Single group
Treatments tested in this trial
Behavioral tasks during intracranial monitoring
Other interventionParticipants will listen to sentences and stories while neural data are recorded through electrodes placed for clinical purposes.
Treatment groups
Trial outcomes
Primary outcomes
Cortical 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.
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.
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).
Sponsors and contacts
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Massachusetts General Hospital
Lead sponsor
Massachusetts Institute of Technology
Collaborator