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CRCNS: Discovering Computational Principles of Language Processing in the Brain

Award Number: R01DC020088
ORGANIZATION: NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/01/2021
PERIOD OF PERFORMANCE END DATE: 05/31/2026

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CRCNS: Discovering Computational Principles of Language Processing in the Brain - A critical characteristic of human language is our ability to understand multi-word sequences whose meaning is greater than the sum of their parts. Recent work from the PIs of this proposal (Toneva and Wehbe, 2019; Jain and Huth, 2018) and others (Schrimpf et al., 2020a; Caucheteux & King, 2020) has shown that cortical representations of multi-word sequences can be modeled much more accurately than before by using neural network language models, a machine learning technique that has revolutionized the natural language processing (NLP) field (Devlin et al., 2019; Radford et al., 2019). However, under the current paradigm these models must first be trained on separate NLP tasks and only then used to model the brain, creating a guess-and-check cycle that is not guaranteed to converge on the actual computations that humans perform. Here we propose to break this cycle by directly training neural network models to estimate the functions that the brain uses to combine words. To be able to optimally predict fMRI and MEG responses, these models will need to capture the composition principles governing which words the brain attends to, and how information is combined across words. These models will help uncover specific computations underlying language processing in the brain, enable computational testing of neurolinguistic theories, and inspire or directly improve models used in NLP. Accomplishing these goals, however, will require overcoming one major obstacle. Training neural net- work language models typically requires orders of magnitude more data than existing neuroimaging datasets. To address this issue, one central goal of this proposed project is to collect a very large fMRI and MEG dataset comprising roughly one million words of natural language stimuli. We plan to use the unique dataset and computational modeling framework to address three scientific aims. Aim 1: Create brain activity prediction benchmarks to foster interaction between neuroscience and NLP. Aim 2: Use data-driven models to test existing neurolinguistic theories & develop new accounts of the computations underlying word composition in the brain. Aim 3: Leverage information in different brain areas to help solve computationally defined language tasks. Successful completion of the proposed work will provide mechanistic insight into language processing, with a computational architecture tracing information flow among brain areas and describing the tasks they perform. Beyond its basic cognitive neuroscience implications, we expect this work will enable better understanding of language impairments and help identify targeted therapies. RELEVANCE (See instructions): Through collecting, analyzing, and disseminating large-scale neuroimaging datasets collected while participants listen to natural, narrative speech, this proposal aims to improve our understanding of the normal function of the language system. Specifically, this work seeks to improve and validate computational models of speech language processing in the human brain.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $365,786 )
2025	2025	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	000	5	6/3/2025	NON-COMPETING CONTINUATION	$365,786
														Subtotal = $365,786

Issue Date FY: 2024 ( Subtotal = $368,083 )
2024	2024	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	000	4	7/17/2024	NON-COMPETING CONTINUATION	$368,083
2024	2024	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	001	4	9/27/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $368,083

Issue Date FY: 2023 ( Subtotal = $370,334 )
2023	2023	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	000	3	6/6/2023	NON-COMPETING CONTINUATION	$370,334
														Subtotal = $370,334

Issue Date FY: 2022 ( Subtotal = $372,541 )
2022	2022	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	001	2	5/24/2022	NON-COMPETING CONTINUATION	$372,541
2022	2021	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR MN 13	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	000	1	10/19/2021	NEW	$0
														Subtotal = $372,541

Issue Date FY: 2021 ( Subtotal = $389,252 )
2021	2021	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR MN 13	AUSTIN	TX	78712	TRAVIS	USA	Research Related to Deafness and Communication Disorders	000	1	7/30/2021	NEW	$389,252
														Subtotal = $389,252

Grand Total All Awards = $1,865,996

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CRCNS: Discovering Computational Principles of Language Processing in the Brain

Award Number: R01DC020088

ORGANIZATION: NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/01/2021

PERIOD OF PERFORMANCE END DATE: 05/31/2026

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