Memory is central to who we are, providing the foundation for our sense of self, understanding of the world, and predictions about the future. While we experience life as a series of events, our ability to build knowledge across events – to abstract common themes and infer relationships between events – enables us to construct internal models of the world, or ‘cognitive maps’, that we use to plan, make decisions, and act (Tolman, 1948; O’Keefe and Nadel, 1978; McClelland et al., 1995; Epstein et al., 2017). One goal of memory science is to understand how neural systems integrate information across experiences, building structured knowledge about the world.

Interactions between a network of brain regions in the medial temporal lobe and neocortex are known to support episodic memory, or memory for events. Central to this network is the hippocampus, whose computations allow us to encode and recall specific episodes from the past and to abstract relationships across experiences that share common elements (McClelland et al., 1995; Eichenbaum, 2004). In subserving rapid learning over co-occurring features, the hippocampus plays a critical role in building episodic memories and structured knowledge by binding together inputs from distributed cortical regions, forming conjunctive representations of experiences (McClelland et al., 1995; Teyler and DiScenna, 1986; Libby et al., 2014). As we learn, neural populations in the hippocampus that represent the traces for individual events can vary in their relations, becoming more distinct (differentiation) or more similar (integration) to each other (Ritvo et al., 2019; Brunec et al., 2020; Wammes et al., 2022). A central question is how these two seemingly contradictory mechanisms of representational change are employed during the construction of structured knowledge.

One hypothesized hippocampal mechanism is pattern separation, in which the hippocampus creates distinct neural representations for highly similar events (O’Reilly and McClelland, 1994; Leutgeb et al., 2007; Yassa and Stark, 2011). Through pattern separation, the hippocampus avoids the blending of similar experiences in memory, reducing across-event interference and forgetting. Tests of the pattern separation hypothesis include functional MRI (fMRI) studies manipulating the extent to which features of events are similar. Such studies have found intriguing evidence for pattern separation when experiences have high overlap or lead to similar outcomes (Bakker et al., 2008; Schlichting et al., 2015; Kyle et al., 2015; Ballard et al., 2019), with links to later remembering (LaRocque et al., 2013). Moreover, some fMRI studies have found evidence for pattern differentiation, where event overlap appears to trigger a ‘repulsion’ of hippocampal representations beyond baseline similarity to reduce interference (Wammes et al., 2022; Favila et al., 2016; Chanales et al., 2017; Schapiro et al., 2012; Kim et al., 2017; Jiang et al., 2020; Hulbert and Norman, 2015).

Despite evidence supporting the hippocampus’s role in forming distinct memories for events, decades of work also suggest it is essential for building structured knowledge and forming cognitive maps that capture relations between spatial, perceptual, and conceptual features that were encountered in separate events that share common elements (Tolman, 1948; O’Keefe and Nadel, 1978; Epstein et al., 2017; Schlichting et al., 2015; Tompary and Davachi, 2017). For example, the hippocampus quickly extracts the temporal structure of an environment as defined by the transition probabilities between items in sequences (Schapiro et al., 2012; Schapiro et al., 2013), and it represents higher order structure when there is no variance in transition probabilities between items but an underlying community structure in a sequence (Schapiro et al., 2016). Moreover, it is essential for inferring indirect relationships between items and events (Schapiro et al., 2014; Zeithamova et al., 2012; Vaidya et al., 2021).

How do overlapping features from separate events lead to integration and generalization? Prior work suggests that retrieval-based learning, recurrent mechanisms within the episodic memory network, and memory replay enable representational integration and allow for the encoding of relationships that have never been directly experienced (Schapiro et al., 2016; Kumaran and McClelland, 2012; Kumaran et al., 2016; Schapiro et al., 2017; Gupta et al., 2010; Wu and Foster, 2014). For example, integrative encoding is thought to occur when a new experience (BC) triggers the recall of a related past episode (AB), and the concurrent activation of the remembered and presently experienced episodes results in the formation of an integrated representation (ABC) that can support future inference and generalization (Shohamy and Wagner, 2008). Integrative encoding may relate to other models that rely on recurrent connections between entorhinal cortex (EC) and the hippocampus to recirculate hippocampal output back into the system as new input, allowing for the discovery of higher order structure at the time of knowledge expression (Kumaran and McClelland, 2012; Kumaran et al., 2016; Schapiro et al., 2017). To the extent that the discovered structure is encoded, then across-event relationships are captured in an integrated representation. Moreover, replay within the episodic memory network is also thought to play an important role in updating mnemonic representations (Ólafsdóttir et al., 2018). Online replay during periods of awake rest has been shown to integrate multiple events, rather than just replaying a single event (Gupta et al., 2010). Offline replay during sharp wave-ripples is associated with memory consolidation and updates to neocortical knowledge structures (Yu et al., 2018; Jadhav et al., 2016; Lewis and Durrant, 2011; Clewett et al., 2022).

Despite a large and rapidly expanding theoretical and empirical literature, our understanding of how differentiation and integration occur across discrete experiences and contribute to the building of coherent, multi-level structured representations is still far from comprehensive. Additional insights may come from examining these processes during the building of structured spatial knowledge, or maps of the external environment. For example, if one moves to a new city, one often first learns individual routes between navigational goals (i.e., from home to work, home to grocery store). Over time, the episodic memory network links individual routes, building a spatial map that enables planning of novel routes, detours, and shortcuts. Spatial knowledge acquired across learning consists of both local and global representations that are used to different extents based on navigational demands (Chrastil, 2013), and the hippocampus and EC are thought to be crucial for the acquisition of such representations. Spatial cells in these regions encode position, head-direction, speed, and other environmental features (O’Keefe and Dostrovsky, 1971; McNaughton et al., 2006; Hafting et al., 2005; Kropff et al., 2015), supporting Tolman’s proposal that navigation is guided by internal cognitive maps and offering insight into how these maps are neurally implemented.

Spatial learning is rich with structure and thus well-suited for exploring the role of differentiation and integration in the formation of local and global knowledge across experiences. Indeed, a previous study in which participants viewed first-person trajectories to real-world destinations found that representations of routes with overlapping sections were differentiated in the hippocampus following learning (Chanales et al., 2017). Another study observed differentiation of hippocampal patterns for landmarks that were common to multiple virtual cities (Zheng et al., 2021). By contrast, other studies found evidence of integration for items that share spatial and temporal context (Deuker et al., 2016) and for objects located in geometrically similar positions across subspaces of segmented environments (Peer et al., 2021). More work is needed to characterize experience-driven changes in population-level neural representations of local and global spaces.

Towards this goal, we developed an experimental paradigm that leverages immersive virtual navigation and fMRI to investigate how neural representations of items encountered during goal-directed navigation evolve across learning. Specifically, pattern similarity analyses and linear mixed-effects models characterized experience-driven changes in EC and hippocampus following ‘local’ and ‘global’ navigation. We hypothesized that we would find evidence for both integration and differentiation emerging at the same time points across learning, as participants build local and global representations of the virtual environment. Moreover, we predicted that early evidence of global map learning during local navigation would depend on integration and predict participants’ ability to subsequently navigate across the environment.

The episodic memory network builds structured knowledge across experiences to form internal models of the world that enable planning, decision-making, and goal-directed behavior. In this study, we investigated how humans build structured knowledge in immersive, goal-directed tasks. Results revealed that (a) learning restructures representations in the hippocampus and EC, reflecting the structure of the virtual environment; (b) the hippocampus begins to build a representational structure extending beyond directly experienced transitions; and (c) changes in the similarity structure of hippocampal representations relate to subsequent navigation performance.

We first characterized learning on two behavioral navigation tasks. In the Local Navigation Task, participants quickly learned goal locations along individual tracks and navigated between goals using the most efficient paths (Figure 2). Prior to the Global Navigation Task, participants were unaware that the tracks would be connected and that they would be required to navigate across tracks to reach cued goals. Nonetheless, some were able to navigate efficiently from the outset of Global Navigation, suggesting they had already formed a global representation of the environment; by contrast, others required extensive experience to do so (Figure 6a). Our findings are consistent with behavioral research examining individual differences in route integration and cognitive map formation. Prior work characterized navigators into groups of ‘integrators’, ‘nonintegrators’, and ‘imprecise navigators’, suggesting that while some individuals build accurate internal maps, others may rely on fragmented maps or route-based strategies during navigation (Weisberg et al., 2014; Weisberg and Newcombe, 2016; Weisberg and Newcombe, 2018). Importantly, individual differences in navigation inefficiency enabled us to probe variability in neural representations and relate neural pattern similarity to behavior. Our work provides an important extension of prior studies in that it identifies potential neural substrates of individual differences in cognitive map formation.

At the neural level, we investigated whether mnemonic regions come to represent the three local tracks following navigation by comparing pattern similarity for items located on the same track vs. different tracks. Motivated by recent studies finding lower hippocampal pattern similarity for events with overlapping vs. distinct features (Schlichting et al., 2015; LaRocque et al., 2013; Favila et al., 2016; Chanales et al., 2017; Hulbert and Norman, 2015), we expected to observe changes in hippocampal pattern similarity for within-track items following local learning. Indeed, we found evidence for differentiation in EC: items on the same track elicited less similar activity patterns when compared to patterns for items on different tracks following local learning (Figure 3c). We observed a quantitatively similar pattern of effects for landmarks in the hippocampus (Figure 3a).

Several factors may explain why we did not find stronger hippocampal differentiation effects. The demands of our navigation tasks differ substantially from those of prior tasks eliciting strong differentiation effects (Schlichting et al., 2015; Kyle et al., 2015; Favila et al., 2016; Chanales et al., 2017; Brown et al., 2010). In prior studies, participants explicitly learned overlapping associations that had to be distinguished at a later test; for instance, the same item was paired with two different associates, or similar items or routes led to distinct outcomes. In the present study, overlapping features were incidental to task demand; thus individuals may have adopted different navigational strategies with varying consequences for within-track item pattern similarity (for instance, some participants may have learned the sequence of fractals for each track, while others may have formed associations between fractals and nearby landmarks). Tracks had a number of features in common, including shared visual features, a similar spatial layout, and common landmarks. Further, our findings from across-track analyses examining individual differences in navigation suggest that some participants began to build an integrated, global representation of the environment prior to the Global Task, with associated effects in the hippocampus. Given these dynamics, additional variance inherent in our paradigm may explain the nature of the observed effects in hippocampus and EC.

While we did not observe particularly strong differentiation of hippocampal patterns for all items located on the same track, we hypothesized that participants would need to form distinct representations of local environmental landmarks to perform well. Accordingly, we examined local spatial representations by comparing the similarity between nearest neighboring landmarks to second nearest neighbors on the same track, and found that hippocampal patterns for nearest neighboring landmarks were differentiated following both Local and Global Navigation (Figure 4b). This finding is consistent with the idea that mnemonic representations – and in particular, representations of large-scale spaces – are hierarchical (McKenzie et al., 2014; Milivojevic and Doeller, 2013; Chrastil and Warren, 2014). That is, we found differentiated representations of local space, whereas representations of track context or of the spatial relationships between the tracks were perhaps more coarse.

We next investigated global representations of map knowledge within the virtual environment. Here, we examined hippocampal pattern similarity for landmarks encountered on different tracks at increasing distances in the virtual environment, expecting to see the emergence of a relationship between hippocampal pattern similarity and distance. We were particularly interested in whether this relationship would be observed after the Local Task, which would suggest that participants were starting to build a global map of the environment prior to direct experience navigating the connected tracks.

Our results provide support for this prediction – we found a significant interaction between distance and scan (Pre-Learning vs. Post Local Navigation; Figure 5b, center). Notably, we also observed significant variability in the slope of this function across individuals (Figure 6b). We hypothesized that this variability in the neural data might relate to the behavioral variability we observed on the subsequent Global Navigation Task, and explored this relationship in two ways. First, we ran a mixed effects model predicting hippocampal pattern similarity Post Local Navigation, with across-track path inefficiency at the start of Global Navigation and link distance as predictors. Here, we observed a significant interaction between path inefficiency and link distance, but the effect was not in the direction we expected. Our results indicate that the interaction between distance and scan (the negative slope in the center panel of Figure 5b) was driven by participants who were less efficient at the outset of Global Navigation, whereas we predicted a negative slope would be more apparent in highly efficient navigators. We initially hypothesized a negative distance-related similarity function would reflect global map learning, and thus expected to observe such a function in efficient navigators at the start of the Global Task on Day 2 and across all participants on Day 3. Such a function was absent following Global Navigation (the right panel of Figure 5b).

Why do we observe a flat distance-related similarity function after global map learning? It is possible that individual differences in navigational strategy or the particular learning processes utilized by efficient navigators in the present task organized the map in such a way. It is also possible our findings reflect a change in event boundaries. Evidence from a study examining temporal context found that hippocampal pattern similarity did not differ between items located nearby vs. further apart within a temporal event (Ezzyat and Davachi, 2014). Hippocampal pattern similarity differed, however, between items that crossed an event boundary, such that nearby items had increased pattern similarity compared to items located further apart in time. While this study examined temporal events, spatial event boundaries may function similarly in that during retrieval, representations of other within-event items are reinstated. The flat slope observed on Day 3 could thus be a signature of an integrated map.

Our second approach to relating variability in the neural data to behavioral variability used a model to predict path inefficiency for each trial across the first four test runs of the Global Navigation Task (Day 2). While the present study was not designed to optimize power to detect trial-level effects, here we found trend-level evidence that hippocampal pattern similarity (Post Local Navigation) for landmark pairs predicted trial-level subsequent Global Navigation performance (Appendix 1—figure 4), providing additional suggestive evidence for a relationship between hippocampal pattern similarity and subsequent behavior.

Replay and recurrence within the hippocampal network is thought to enable the hippocampus to learn relationships between items that have never been directly experienced together (Zeithamova et al., 2012; Kumaran and McClelland, 2012; Kumaran et al., 2016; Gupta et al., 2010; Wu and Foster, 2014). The present study provides complementary evidence that the hippocampus begins to build a cognitive map extending beyond directly experienced transitions in a goal-directed task. Overlapping features within the virtual environment (the landmarks shared between tracks) provide the hippocampus with the links it needs to begin building a global map, despite participants not yet experiencing the connected environment. Crucially, not all participants demonstrated integration early in the Global Task, suggesting that cognitive map formation may not be a ‘default’ that incidentally emerges during performance of immersive, goal-directed local navigation. An open question is: How does this variability in whether and how individuals build global knowledge of the environment during local navigation relate to individual differences in spatial and mnemonic processing, in navigational strategy, and/or in other cognitive and contextual factors (Weisberg and Newcombe, 2016; Weisberg and Newcombe, 2018; Wolbers and Hegarty, 2010; Brunec et al., 2022)? The current study was limited in its investigation of individual differences due to sample size. Future work is needed to characterize the differences between performance groups and examine how representations of individual tracks and particular spatial positions within tracks covary with performance on subsequent global navigation. Moreover, some of our findings do not survive correction for multiple comparisons, and thus should be interpreted with caution.

The present findings appear to diverge from work finding hippocampal pattern similarity scaled with perceived spatiotemporal distance in a virtual environment, such that patterns for nearby items were integrated (Deuker et al., 2016). In that study, participants were scanned before and after a task with a duration of ~80 min; thus, the findings may reflect hippocampal representations at an earlier stage of learning. In the present paradigm, fMRI took place 21.5–31 hr after extensive navigational practice, raising the possibility that hippocampal differentiation requires extensive learning, potentially including learning that takes place via replay and consolidation processes occurring during sleep. Future studies are necessary to pinpoint when integration occurs across the broader global map.

While we expected similar results for both fractal and landmark stimuli throughout our analyses, the null findings we observed across ROIs when local and global distance models were run with fractal stimuli were not completely surprising. Fractal and landmark stimuli differ in several key ways, which we believe explain the observed pattern of findings. For example, fractals are only visible in the environment for a minority of trials. During the majority of navigation, participants must rely on the landmark buildings to guide them. Fractals serve as pointers to the goal location to which a participant must navigate on a particular trial, but fractal information may not be used during route planning. Further, fractals are not necessary for participants to learn the layout of the environment; local and global maps can be built from landmarks alone.

Mnemonic integration depends in part on retrieval and interactions between the hippocampus and vmPFC (Zheng et al., 2021; Preston and Eichenbaum, 2013; Kuhl et al., 2012; Zeithamova and Preston, 2010). Our a priori analyses focused on the hippocampus and EC, but we conducted exploratory analyses in to determine whether and how vmPFC would come to represent the virtual environment, given its proposed role as a storage site for integrated memory representations (Frankland and Bontempi, 2005; Takashima et al., 2006; Takehara-Nishiuchi and McNaughton, 2008; see Appendix). In the present work, we did not observe robust effects in vmPFC, but it remains possible that this region may represent features of the environment that were not explicit to our modeling approach.

An emergent body of work is advancing understanding of fundamental mechanisms in the medial temporal lobe and connected cortical structures that build knowledge across events. Part of this work documents pattern separation and pattern differentiation in the hippocampus, which serves to minimize confusion between related memories. At the same time, other theoretical and empirical findings are elucidating the role of these structures in mnemonic integration and cross-event generalization. It has been unclear whether and how these two seemingly opposing mechanisms—differentiation and integration—work together to facilitate the emergence of structured knowledge. Here, we provide complementary evidence that both differentiation and integration occur concurrently within the hippocampus and can emerge at the same points in learning. Our data suggest that hippocampal differentiation provides a structure necessary for participants to distinguish between nearby locations, while integration processes serve to build a global map of the environment. Moreover, integration relates to individual differences in the ability to efficiently navigate between locations in the global map that have not been previously traversed. Future research promises to reveal whether individual differences in global knowledge building are strategic, relate to differences in other neural systems, and/or are differentially sensitive to dysfunction or disease.

Raw fMRI data is available at https://doi.org/10.18112/openneuro.ds004406.v1.0.0. Analytical code and behavioral data for reproducing analyses, results, and figures shown in the paper are available at https://github.com/coreyfernandez/RID, (copy archived at swh:1:rev:f4a2d4915f1922c8a74f1f1a86469cf13789abb5). Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Corey Fernandez (coreyf@stanford.edu).

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Author details

1. Corey Fernandez

   1. Graduate Program in Neurosciences, Stanford University, Stanford, United States
   2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   coreyf@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3901-5552

2. Jiefeng Jiang

   Department of Psychological and Brain Sciences, University of Iowa, Iowa City, United States

   Contribution

   Conceptualization, Software, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Shao-Fang Wang

   Department of Psychology, Stanford University, Stanford, United States

   Contribution

   Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

4. Hannah Lee Choi

   Department of Psychology, Stanford University, Stanford, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0556-8351

5. Anthony D Wagner

   1. Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States
   2. Department of Psychology, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0624-4543

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