Events that overlap with previous experience may trigger reactivation of existing memories. However, such reactivation may have different representational consequences within the hippocampal circuit. Computational theories of hippocampal function suggest that dentate gyrus and CA 2,3 (DG/CA 2,3 ) are biased to differentiate highly similar memories, whereas CA 1 may integrate related events by representing them with overlapping neural codes. Here, we tested whether the formation of differentiated or integrated representations in hippocampal subfields depends on the strength of memory reactivation during learning. Human participants of both sexes learned associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while they encoded overlapping associations (BC shape-object pairs). Both before and after learning, participants were also scanned while viewing indirectly related elements of the overlapping memories (A and C images) in isolation. We used multivariate pattern analyses to measure reactivation of initial pair memories (A items) during overlapping pair (BC) learning, as well as learning-related representational change for indirectly related memory elements in hippocampal subfields. When prior memories were strongly reactivated during overlapping pair encoding, DG/CA 2,3 and subiculum representations for indirectly related images (A and C) became less similar, consistent with pattern differentiation. Simultaneously, memory reactivation during new learning promoted integration in CA 1 , where representations for indirectly related memory elements became more similar after learning. Furthermore, memory reactivation and subiculum representation predicted faster and more accurate inference (AC) decisions. These data show that reactivation of related memories during new learning leads to dissociable coding strategies in hippocampal subfields, in line with computational theories.

Recent evidence demonstrates that new events are learned in the context of their relationships to existing memories. Within the hippocampus and medial prefrontal cortex, related memories are represented by integrated codes that connect events experienced at different times and places. Integrated codes form the basis of spatial, temporal, and conceptual maps of experience. These maps represent information that goes beyond direct experience and support generalization behaviors that require knowledge be used in new ways. The degree to which an individual memory is integrated into a coherent map is determined by its spatial, temporal, and conceptual proximity to existing knowledge. Integration is observed over a wide range of scales, suggesting that memories contain information about both broad and fine-grained contexts.

Neural circuitry in the medial temporal lobe (MTL) is critically involved in mental time travel, which involves the vivid retrieval of the details of past experience. Neuroscientific theories propose that the MTL supports memory of the past by retrieving previously encoded episodic information, as well as by reactivating a temporal code specifying the position of a particular event within an episode. However, the neural computations supporting these abilities are underspecified. To test hypotheses regarding the computational mechanisms supported by different MTL subregions during mental time travel, we developed a computational model that linked a blood oxygenation level-dependent signal to cognitive operations, allowing us to predict human performance in a memory search task. Activity in the posterior MTL, including parahippocampal cortex, reflected how strongly one reactivates the temporal context of a retrieved memory, allowing the model to predict whether the next memory will correspond to a nearby moment in the study episode. A signal in the anterior MTL, including perirhinal cortex, indicated the successful retrieval of list items, without providing information regarding temporal organization. A hippocampal signal reflected both processes, consistent with theories that this region binds item and context information together to form episodic memories. These findings provide evidence for modern theories that describe complementary roles of the hippocampus and surrounding parahippocampal and perirhinal cortices during the retrieval of episodic memories, shaping how humans revisit the past.

Retrieved-context models of human memory propose that as material is studied, retrieval cues are constructed that allow one to target particular aspects of past experience. We examined the neural predictions of these models by using electrocorticographic/depth recordings and scalp electroencephalography (EEG) to characterize category-specific oscillatory activity, while participants studied and recalled items from distinct, neurally discriminable categories. During study, these category-specific patterns predict whether a studied item will be recalled. In the scalp EEG experiment, category-specific activity during study also predicts whether a given item will be recalled adjacent to other same-category items, consistent with the proposal that a category-specific retrieval cue is used to guide memory search. Retrieved-context models suggest that integrative neural circuitry is involved in the construction and maintenance of the retrieval cue. Consistent with this hypothesis, we observe category-specific patterns that rise in strength as multiple same-category items are studied sequentially, and find that individual differences in this category-specific neural integration during study predict the degree to which a participant will use category information to organize memory search. Finally, we track the deployment of this retrieval cue during memory search: Category-specific patterns are stronger when participants organize their responses according to the category of the studied material.

Prior work has shown that the brain represents memories within a cognitive map that supports inference about connections between individual related events. Real-world adaptive behavior is also supported by recognizing common structure among numerous distinct contexts; for example, based on prior experience with restaurants, when visiting a new restaurant one can expect to first get a table, then order, eat, and finally pay the bill. We used a neurocomputational approach to examine how the brain extracts and uses abstract representations of common structure to support novel decisions. Participants learned image pairs (AB, BC) drawn from distinct triads (ABC) that shared the same internal structure and were then tested on their ability to infer indirect (AC) associations. We found that hippocampal and frontoparietal regions formed abstract representations that coded cross-triad relationships with a common geometric structure. Critically, such common representational geometries were formed despite the lack of explicit reinforcement to do so. Furthermore, we found that representations in parahippocampal cortex are hierarchical, reflecting both cross-triad relationships and distinctions between triads. We propose that representations with common geometric structure provide a vector space that codes inferred item relationships with a direction vector that is consistent across triads, thus supporting faster inference. Using computational modeling of response time data, we found evidence for dissociable vector-based retrieval and pattern-completion processes that contribute to successful inference. Moreover, we found evidence that these processes are mediated by distinct regions, with pattern completion supported by hippocampus and vector-based retrieval supported by parahippocampal cortex and lateral parietal cortex.