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Replay-Driven Task Orthogonalization and Abstraction for Continual Learning

Award Number: F32MH143585
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 03/02/2026
PERIOD OF PERFORMANCE END DATE: 03/01/2029

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Replay-Driven Task Orthogonalization and Abstraction for Continual Learning - PROJECT SUMMARY One of the brain’s most remarkable abilities is its capacity to learn and adapt continuously throughout life. This capacity relies on balancing two competing demands: keeping context- and task-specific details distinct while also integrating shared structure across experiences to enable generalization. However, we still do not fully understand how the brain concurrently manages these demands, largely due to the lack of tasks designed for studying them together. We propose that memory replay—the reactivation of brain activity patterns in the absence of overt task demand—support continual learning in the brain by reorganizing neural representations to fulfill these demands. Although past research has identified evidence of replay across animals and humans, its role in learning and behavior remain unclear. To address these gaps, we have developed a new experimental design that examines examines how the brain manages to keep task-specific details separate while also extracting common patterns during continual learning. We will also explore how memory replay enables the brain to strike this balance. Our approach tests two main hypotheses: first, that the brain forms representations that segregate context- and task- specific information while abstracting shared structure across tasks; and second, that replay supports continual learning by helping to orthogonalize and abstract task representations. In our study, participants will first learn simple, one-step transitions before planning longer action sequences across three different graphs, all while their brain activity is recorded using magnetoencephalography (MEG). This design will allow us to assess how well participants retain details specific to the individual graphs and extract a hidden, abstract structure common to all of them. We will analyze both behavior and neural patterns to understand how the brain manages these dual demands, and we will compare human behavior and neural representations with that of neural network models optimized for the same tasks. We will also use advanced MEG decoding techniques to track replay events during both rest and active task phases, examining how these events shape behavior and task representations. Complementary measures, such as eye-tracking, will help us explore how different physiological states influence replay dynamics. By combining behavioral testing, neuroimaging, and computational modeling, this study aims to provide new insights into how the brain continually adapts to changing environments. The findings will deepen our fundamental understanding of human learning and memory, and guide future efforts to enhance cognitive function in educational, clinical, and aging settings.


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: 2026 ( Subtotal = $75,052 )
2026	2026	NEW YORK UNIVERSITY	70 WASHINGTON SQ S FL 11	NEW YORK	NY	10012	NEW YORK	USA	Mental Health Research Grants	001	1	3/2/2026	NEW	$0
2026	2026	NEW YORK UNIVERSITY	70 WASHINGTON SQ S FL 11	NEW YORK	NY	10012	NEW YORK	USA	Mental Health Research Grants	000	1	3/2/2026	NEW	$75,052
														Subtotal = $75,052

Grand Total All Awards = $75,052

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Replay-Driven Task Orthogonalization and Abstraction for Continual Learning

Award Number: F32MH143585

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 03/02/2026

PERIOD OF PERFORMANCE END DATE: 03/01/2029

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