# Neural mechanisms for reducing interference during episodic memory formation

> **NIH NIH R01** · UNIVERSITY OF OREGON · 2022 · $483,775

## Abstract

Project Summary/Abstract
One of the biggest challenges to successful remembering is the potential for confusion or interference
between similar memories. For every password, name, or parking space that we store in memory, there are
many other passwords, names or parking spaces that we have already learned or will learn in the future.
While interference is a factor in relatively benign—yet annoying—examples of ‘normal’ forgetting, it is also a
major factor in clinically significant examples of forgetting that occur with aging and/or dementia. Thus, there
is a fundamental need to understand the neural mechanisms that support the acquisition/retrieval of similar
memories while minimizing interference and corresponding forgetting.
Computational models of episodic memory have emphasized the critical role of pattern separation in
reducing memory interference. Pattern separation involves coding similar memories such that differences
between memories are exaggerated and the potential for confusion is thereby minimized. While there is
general agreement that pattern separation is implemented by the hippocampus—and that pattern
separation is important for reducing memory interference—there remain several fundamental gaps in our
understanding of how, when, and why pattern separation occurs. In particular, there remains ambiguity as
far as (a) the learning contexts in which each mechanism might be recruited, (b) what the corresponding
neural signatures of each mechanism are, and (c) the specific behavioral consequences associated with the
engagement of each mechanism.
We propose a systematic investigation of the contexts in which integration and pattern separation occur with
the goal of using sophisticated, cutting-edge neuroimaging (fMRI) techniques to identify distributed patterns
of neural activity that are diagnostic of each mechanism. Critically, we also plan to use these observed
patterns of neural activity—that is, neural evidence for integration vs. separation—to predict behavioral
memory phenomena, including interference-related forgetting. The research represents a strong synthesis
of psychology and neuroscience questions with an emphasis on learning mechanisms inspired by
computational models and analysis approaches that draw from the fields of machine learning and data
mining.

## Key facts

- **NIH application ID:** 10319075
- **Project number:** 5R01NS089729-07
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** BRICE Alan KUHL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $483,775
- **Award type:** 5
- **Project period:** 2014-09-30 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10319075

## Citation

> US National Institutes of Health, RePORTER application 10319075, Neural mechanisms for reducing interference during episodic memory formation (5R01NS089729-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10319075. Licensed CC0.

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