Real-World Episodic Memory

Episodic memory allows us to project ourselves back in time to mentally re-experience the past in vivid detail. This remarkable feat helps to define our personal identities and enables context-appropriate predictions that guide adaptive behavior. Unfortunately, episodic memory loss is a common cognitive consequence of neurological disease, psychiatric disorders, and normative aging, that reflects dysfunction in the hippocampus and the neocortical network to which it connects. This decline can present along a range of severity that spans an impoverished ability to recollect fine-grained event details, e.g., the color of the shirt your spouse was wearing at a birthday party, to forgetting course-grained event details, e.g., having attended a birthday party last weekend. Treating these kinds of memory loss remains challenging because interventions must target information that is definitionally idiosyncratic. However, recent methodological and theoretical advances in cognitive neuroscience reveal novel strategies that may solve this problem. Evidence from behavioral, neuroimaging, and computational modeling studies suggest that the degree to which episodic memories overlap in the hippocampus can be modified in a predictable, experience-dependent manner. Specifically, strong concurrent reactivation of multiple memories can enhance recall of coarse-grained episodic information by increasing hippocampal integration. Conversely, coupling strong and moderate reactivation of memories can enhance recall of fine-grained episodic information be increasing hippocampal differentiation. Consistent with this idea, recent evidence suggests that using a smartphone to record and subsequently replay real-world memory cues can indeed improve episodic memory by reducing representational overlap among memories in the hippocampus. Against this background, this proposal is organized around two primary aims. First, we seek to establish smartphone-guided memory reactivation protocols that selectively promote integration and differentiation of real-world episodic memories in the hippocampus. Experiments in this aim will specifically ask whether unstructured and structured (in terms of narrative, space, and time) reactivation can be used to achieve these outcomes. Second, we will ask whether the benefits of memory reactivation are associated with behavioral and representational costs. Experiments in this aim will probe for undesirable downsides that accompany, or potentially explain, reactivation-based improvements in episodic memory. Our approach combines smartphone technology, pattern-based analysis of functional neuroimaging data, and careful characterization of recall data with neurocognitive theory inspired by computational models of learning mechanisms. Achieving our aims will establish an empirical foundation on which future interventions can be built to systematically target real-world episodic memories at a level of abstraction, i.e., fine-grained vs. coarse-grained detail, that is appropriate for the severity of memory loss.