Project Summary/Abstract One of the biggest challenges to successful remembering is the potential for 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 examples of ‘normal’ forgetting, it is also a major factor in clinically-significant examples of forgetting that occur with aging or dementia. Thus, there is a fundamental need to understand the neural mechanisms that support the acquisition 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 agreement that pattern separation is implemented by the hippocampus—and that it 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 will conduct a systematic investigation of the contexts in which pattern separation occurs, leveraging sophisticated neuroimaging (fMRI) techniques to characterize patterns of neural activity and to link these patterns of activity to behavioral expressions of memory. 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. In this diversity supplement, we request funding to support Mr. Julian Gamez who recently graduated (B.S. degree) from the University of Oregon and is preparing to apply to Ph.D. programs. Mr. Gamez will specifically focus on establishing how memory interference influences the dimensionality of neural representations of memories and corresponding behavioral expressions. This novel approach will address competing theoretical accounts and will strongly complement the overall goals of the parent award. It will also provide an outstanding opportunity for Mr. Gamez to gain expertise learning sophisticated computational methods and applying these methods to human fMRI and behavioral data.