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 aso 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 proposed two core mechanisms that are thought to reduce interference-related forgetting: integration and pattern separation. Integration involves 'fusing'overlapping memories into a common representation such that the relationship between these memories is more complementary than competitive. Pattern separation involves the orthogonalization of similar memories such that differences between memories are exaggerated and the potential for interference minimized. While there is general agreement that these mechanisms are theoretically appealing and offer clear computational advantages, clear evidence for how and when these learning mechanisms are invoked- particularly in humans-remains surprisingly limited? 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.
A primary reason we forget is because we store a vast number of memories and interference between these memories becomes inevitable. While forgetting may represent only an occasional annoyance for most people, among older adults and those with dementia-groups that are growing at a disproportionate rate memory confusions and susceptibility to interference-related forgetting can be debilitating problems. The proposed research seeks to identify neural mechanisms that reduce interference-related forgetting and will develop neurodiagnostic tools that can be used to characterize memory processes and predict susceptibility to memory confusions and forgetting.