Neural mechanisms for reducing interference during episodic memory formation

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.