Memories, like the internal representation of space, can be recalled at different resolutions ranging from detailed events to more comprehensive, multi-event narratives. Single-cell recordings in rodents indicate that different spatial scales are represented as a gradient along the hippocampal axis. Here, we show that a similar organisation holds for human episodic memory: memory representations systematically vary in scale along the hippocampal long-axis, which may enable the formation of mnemonic hierarchies.

Narratives may provide a general context, unrestricted by space and time, which can be used to organize episodic memories into networks of related events. However, it is not clear how narrative contexts are represented in the brain. Here we test the novel hypothesis that the formation of narrative-based contextual representations in humans relies on the same hippocampal mechanisms that enable formation of spatiotemporal contexts in rodents. Participants watched a movie consisting of two interleaved narratives while we monitored their brain activity using fMRI. We used representational similarity analysis, a type of multivariate pattern analysis, which uses across-voxel correlations as a proxy for neural-pattern similarity, to examine whether the patterns of neural activity can be used to differentiate between narratives and recurring narrative elements, such as people and locations. We demonstrate that the neural activity patterns in the hippocampus differentiate between event nodes (people and locations) and narratives (different stories) and that these narrativecontext representations diverge gradually over time akin to remapping-induced spatial maps represented by rodent place cells.

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We all possess a mental library of schemas that specify how different types of events unfold. How are these schemas acquired? A key challenge is that learning a new schema can catastrophically interfere with (i.e., overwrite) old knowledge. One solution to this dilemma is to use interleaved training to learn a single representation that accommodates all schemas. However, another class of models posits that catastrophic interference can be avoided by splitting off new representations when large prediction errors occur. A key differentiating prediction is that, according to splitting models, catastrophic interference can be prevented even under blocked training curricula. We conducted a series of semi-naturalistic experiments and simulations with Bayesian and neural network models to compare the predictions made by the “splitting” versus “non-splitting” hypotheses of schema learning. We found better performance in blocked compared to interleaved curricula, and explain these results using a Bayesian model that incorporates representational splitting in response to large prediction errors. In a follow-up experiment, we validated the model prediction that inserting blocked training early in learning leads to better learning performance than inserting blocked training later in the learning process. Our results suggest different learning environments (i.e., curricula) play an important role in shaping schema composition. We discuss the different roles prediction errors have for “carving nature at its joints”.

Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.