The goal of this research is to develop and test a mechanistically explicit theory of how the brain gives rise to episodic memory: our ability to recall previously experienced events, and to recognize events as having been experienced previously. Researchers have known for years that the hippocampus is the key neural substrate of recall; more recently, several studies have found that -- after focal hippocampal damage -- medial temporal lobe cortex (MTLC) can support some degree of spared recognition performance on its own. To explore how the contributions of hippocampus and MTLC differ, the research proposed here uses neural network models of these structures to simulate patterns of memory performance from normal and brain-damaged subjects. The first specific aim is to test the model's predictions regarding when item and associative recognition performance will be affected by knocking out the hippocampal contribution; """"""""knockout"""""""" will be operationalized by using patients with focal hippocampal damage, and also normal subjects who are forced to respond quickly. Other experiments will test model predictions regarding how context change and interference manipulations affect recognition in the two systems. The second specific aim is to run simulations using a combined cortico-hippocampal model to address how the two processes conjointly determine recognition performance (e.g., in paradigms that place the two processes in opposition), and to run experiments in amnesic patients and controls to test these predictions. Extant formal models of recognition memory do not make contact with the underlying neurobiology; as such, this research constitutes a major step forward in recognition memory modeling. This link to neurobiology provides extra constraints that can be leveraged to gain new insights into fundamental puzzles in the memory literature. Regarding health benefits: The model's ability to address lesion data will bolster our understanding of what kinds of learning are spared after different kinds of brain damage, which (in turn) will help doctors develop more effective rehabilitation regimes. Furthermore, as researchers start to develop therapies that change the underlying parameters of learning in the brain, we will need some way of assessing how these changes scale up and affect behavioral memory performance; the research proposed here is ideally positioned to meet this growing need.
| Ellis, Cameron T; Turk-Browne, Nicholas B (2018) Infant fMRI: A Model System for Cognitive Neuroscience. Trends Cogn Sci 22:375-387 |
| Schapiro, Anna C; McDevitt, Elizabeth A; Rogers, Timothy T et al. (2018) Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance. Nat Commun 9:3920 |
| Kok, Peter; Turk-Browne, Nicholas B (2018) Associative prediction of visual shape in the hippocampus. J Neurosci : |
| Rafidi, Nicole S; Hulbert, Justin C; Brooks, Paula P et al. (2018) Reductions in Retrieval Competition Predict the Benefit of Repeated Testing. Sci Rep 8:11714 |
| Antony, James W; Ferreira, Catarina S; Norman, Kenneth A et al. (2017) Retrieval as a Fast Route to Memory Consolidation. Trends Cogn Sci 21:573-576 |
| Poppenk, Jordan; Norman, Kenneth A (2017) Multiple-object Tracking as a Tool for Parametrically Modulating Memory Reactivation. J Cogn Neurosci 29:1339-1354 |
| Cohen, Jonathan D; Daw, Nathaniel; Engelhardt, Barbara et al. (2017) Computational approaches to fMRI analysis. Nat Neurosci 20:304-313 |
| Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M et al. (2017) Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci 372: |
| Schapiro, Anna C; McDevitt, Elizabeth A; Chen, Lang et al. (2017) Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information. Sci Rep 7:14869 |
| Kim, Ghootae; Norman, Kenneth A; Turk-Browne, Nicholas B (2017) Neural Differentiation of Incorrectly Predicted Memories. J Neurosci 37:2022-2031 |
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