Structuring Knowledge with Cognitive Maps and Cognitive Graphs

Evidence for Cognitive Spatial Models from Ancient Roman Land-Measurement

Influential studies in the history of cartography have argued that map-like representations of space were (virtually) unknown in the Classical Mediterranean world and that the cause of this was an absence of underlying cognitive maps. That is, persons in that time/place purportedly had only route/egocentric-type mental representations, not survey/allocentric ones. The present study challenges that cognitive claim by examining the verbal descriptions of plots of land produced by ancient Roman land-measurers. Despite the prescription of a route-based form, actual representations persistently show a variety of features which suggest the existence of underlying survey-type mental models and the integration of those with the route-type ones. This fits better with current views on interaction between types of spatial representation and of cultural difference in this area. The evidence also suggests a linkage between the two kinds of representations.

Social knowledge about others is anchored to self-knowledge in the hippocampal formation

Mounting evidence suggests the human hippocampal formation (HF) maps how different people's attributes relate to each other. Yet, it's unclear if hippocampal map-like knowledge representations of other people are shaped by self-knowledge. Here, we test if a prominent heuristic involving an implicit reliance on self-knowledge when rating others, egocentric anchoring-and-adjustment, is present in the HF when relational information about different social entities is retrieved. Participants first provided likelihood ratings of partaking in everyday activities for themselves, fictitious individuals, and familiar social groups. During a neuroimaging task that doesn't require using self-knowledge, participants then learned a stranger's preference for an activity relative to one of the fictitious individuals and inferred how the stranger's preference related to the groups' preferences. Isolating the neural representation of egocentric anchoring when retrieving relational social knowledge, the HF and dorsomedial prefrontal cortex (dmPFC) represented group entities' preferences relative to the self. Furthermore, the HF selectively represented group identity over other learned entities, confirming the HF was primarily engaged by social comparisons in the more ample map-like reference frame. Taken together, these results imply that self-knowledge implicitly influences how the HF learns about others.

Learning the layout of different environments: common or dissociated abilities?

People navigate in various types of spaces, including indoor and outdoor environments. These differ in availability of navigational cues, such as distal landmarks, clear boundaries, and regular grid structures. Does learning the layout of different types of environments rely on the same or diverse cognitive abilities? Do separate measures of learning reflect different abilities? In a study of individual differences, 88 people learned the layout of two virtual environments from first person experience: a grid-like maze, and a campus-like open environment. After learning each environment, their knowledge was measured by three tasks; onsite pointing, map-reconstruction, and wayfinding. Performance on these measures was significantly correlated. In confirmatory factor analyses, the best fitting model indicated separate factors for spatial knowledge acquisition of the grid-like maze and the outdoor open environment. However, these two factors also shared considerable variance, indicating that they reflect a common underlying ability. There was no evidence that different measures of learning (pointing, map reconstruction, and wayfinding) defined separate abilities, adding to their validity as alternative measures of configural knowledge. Performance of map-based navigation and path integration in the mobile navigation game Sea Hero Quest was generally not correlated with performance in the environment learning tasks, nor were self-report measures of sense of direction and spatial anxiety. Our research suggests that there is a common ability related to learning spatial layout in different contexts, but this may be distinct from other navigation abilities.

Spaces and sequences in the hippocampus: a homological perspective

Topological techniques have become a popular tool for studying information flows in neural networks. In particular, simplicial homology theory is used to analyze how cognitive representations of space emerge from large conglomerates of independent neuronal contributions. Meanwhile, a growing number of studies suggest that many cognitive functions are sustained by serial patterns of activity. Here, we investigate stashes of such patterns using path homology theory-an impartial, universal approach that does not require a priori assumptions about the sequences' nature, functionality, underlying mechanisms, or other contexts. We focus on the hippocampus-a key enabler of learning and memory in mammalian brains-and quantify the ordinal arrangement of its activity similarly to how its topology has previously been studied in terms of simplicial homologies. The results reveal that the vast majority of sequences produced during spatial navigation are structurally equivalent to one another. Only a few classes of distinct sequences form an ordinal schema of serial activity that remains stable as the pool of sequences consolidates. Importantly, the structure of both maps is upheld by combinations of short sequences, suggesting that brief activity motifs dominate physiological computations. This ordinal organization emerges and stabilizes on timescales characteristic of spatial learning, displaying similar dynamics. Yet, the ordinal maps generally do not reflect topological affinities-spatial and sequential analyses address qualitatively different aspects of spike flows, representing two complementary formats of information processing.

Significance statement

This study employs path homology theory to examine serial patterns of neuronal activity in the hippocampus, a critical region for learning and memory. While the traditional, simplicial homology approaches used to model cognitive maps, path homology provides a universal framework for analyzing the ordinal arrangement of neuronal sequences without presupposing their nature or function. The findings reveal that a limited number of distinct sequence classes, supported by combinations of short activity motifs, form a stable ordinal schema over timescales corresponding to periods of spatial learning. Notably, the ordinal maps derived from these sequences do not capture topological affinities, highlighting that spatial and sequential analyses address distinct but complementary dimensions of neural information processing.

Challenges and insights of transferring animal maze studies principles to human spatial learning research

Maze tasks, originally developed in animal research, have become a popular method for studying human cognition, particularly with the advent of virtual reality. However, these experiments frequently rely on simplified environments and tasks, which may not accurately reflect the complexity of real-world situations. Our pilot study aims to transfer a multi-alternative maze with a complex task structure, previously demonstrated to be useful in studying animal cognition, to studying human spatial cognition. The challenges to be resolved at this stage included developing a virtual maze and selecting an appropriate instruction that will elicit processes similar to those observed in animal models. A virtual maze was developed, and two types of instructions were provided to the participants: (1) to collect coins; (2) to interact with the maze in order to draw its structure after the game. The results indicate that a more structured instruction with a clear attainable goal ("collect") prompted more in-depth exploration and engagement with the key elements of the maze, eliciting processes similar to those of animals. While the maze demonstrates promise as a tool for comparative studies, it also has the potential to uncover different aspects of human cognition.

Unifying Principles of Generalization: Past, Present, and Future

Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances. Each approach has unique advantages: Rules support rapid knowledge transfer, while similarity is computationally simple and flexible. Today, these debates have culminated in the development of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The ongoing success of hybrid models not only bridges past dichotomies but also underscores the importance of integrating both rules and similarity for a comprehensive understanding of human generalization.

Learning dynamic cognitive map with autonomous navigation

Inspired by animal navigation strategies, we introduce a novel computational model to navigate and map a space rooted in biologically inspired principles. Animals exhibit extraordinary navigation prowess, harnessing memory, imagination, and strategic decision-making to traverse complex and aliased environments adeptly. Our model aims to replicate these capabilities by incorporating a dynamically expanding cognitive map over predicted poses within an active inference framework, enhancing our agent's generative model plasticity to novelty and environmental changes. Through structure learning and active inference navigation, our model demonstrates efficient exploration and exploitation, dynamically expanding its model capacity in response to anticipated novel un-visited locations and updating the map given new evidence contradicting previous beliefs. Comparative analyses in mini-grid environments with the clone-structured cognitive graph model (CSCG), which shares similar objectives, highlight our model's ability to rapidly learn environmental structures within a single episode, with minimal navigation overlap. Our model achieves this without prior knowledge of observation and world dimensions, underscoring its robustness and efficacy in navigating intricate environments.

A Fast Algorithm for All-Pairs-Shortest-Paths Suitable for Neural Networks

Given a directed graph of nodes and edges connecting them, a common problem is to find the shortest path between any two nodes. Here we show that the shortest path distances can be found by a simple matrix inversion: if the edges are given by the adjacency matrix Aij, then with a suitably small value of γ, the shortest path distances are Dij=ceil(logγ[(I-γA)-1]ij).We derive several graph-theoretic bounds on the value of γ and explore its useful range with numerics on different graph types. Even when the distance function is not globally accurate across the entire graph, it still works locally to instruct pursuit of the shortest path. In this mode, it also extends to weighted graphs with positive edge weights. For a wide range of dense graphs, this distance function is computationally faster than the best available alternative. Finally, we show that this method leads naturally to a neural network solution of the all-pairs-shortest-path problem.

Retinotopic coding organizes the interaction between internally and externally oriented brain networks

The human brain seamlessly integrates internally generated thoughts with incoming sensory information, yet the networks supporting internal (default network, DN) and external (dorsal attention network, dATN) processing are traditionally viewed as antagonistic. This raises a crucial question: how does the brain integrate information between these seemingly opposed systems? Here, using precision neuroimaging methods, we show that these internal/external networks are not as dissociated as traditionally thought. Using densely-sampled 7T fMRI data, we defined individualized whole-brain networks from participants at rest and calculated the retinotopic preferences of individual voxels within these networks during an visual mapping task. We show that while the overall network activity between the DN and dATN is independent at rest, considering a latent retinotopic code reveals a complex, voxel-scale interaction stratified by visual responsiveness. Specifically, the interaction between the DN and dATN at rest is structured at the voxel-level by each voxel's retinotopic preferences, such that the spontaneous activity of voxels preferring similar visual field locations is more anti-correlated than that of voxels preferring different visual field locations. Further, this retinotopic scaffold integrates with the domain-specific preferences of subregions within these networks, enabling efficient, parallel processing of retinotopic and domain-specific information. Thus, DN and dATN are not independent at rest: voxel-scale interaction between these networks preserves and encodes information in both positive and negative BOLD responses, even in the absence of visual input or task demands. These findings suggest that retinotopic coding may serve as a fundamental organizing principle for brain-wide communication, providing a new framework for understanding how the brain balances and integrates internal cognition with external perception.

Causal and Chronological Relationships Predict Memory Organization for Nonlinear Narratives

While recounting an experience, one can employ multiple strategies to transition from one part to the next. For instance, if the event was learned out of linear order, one can recall events according to the time they were learned (temporal), similar events (semantic), events occurring nearby in time (chronological), or events produced by the current event (causal). To disentangle the importance of these factors, we had participants watch the nonlinear narrative, Memento, under different task instructions and presentation orders. For each scene of the film, we also separately computed semantic and causal networks. We then contrasted the evidence for temporal, semantic, chronological, or causal strategies during recall. Critically, there was stronger evidence for the causal and chronological strategies than semantic or temporal strategies. Moreover, the causal and chronological strategies outperformed the temporal one even when we asked participants to recall the film in the presented order, underscoring the fundamental nature of causal structure in scaffolding understanding and organizing recall. Nevertheless, time still marginally predicted recall transitions, suggesting it operates as a weak signal in the presence of more salient forms of structure. In addition, semantic and causal network properties predicted scene memorability, including a stronger role for incoming causes to an event than its outgoing effects. In summary, these findings highlight the importance of accounting for complex, causal networks in knowledge building and memory.

Cognitive Maps for a Non-Euclidean Environment: Path Integration and Spatial Memory on a Sphere

Humans build mental models of the world and utilize them for various cognitive tasks. The exact form of cognitive maps is not fully understood, especially for novel and complex environments beyond the flat Euclidean environment. To address this gap, we investigated path integration-a critical process underlying cognitive mapping-and spatial-memory capacity on the spherical (non-Euclidean) and planar (Euclidean) environments in young healthy adults (N = 20) using immersive virtual reality. We observed a strong Euclidean bias during the path-integration task on the spherical surface, even among participants who possessed knowledge of non-Euclidean geometry. Notably, despite this bias, participants demonstrated reasonable navigation ability on the sphere. This observation and simulation suggest that humans navigate nonflat surfaces by constructing locally confined Euclidean maps and flexibly combining them. This insight sheds light on potential neural mechanisms and behavioral strategies for solving complex cognitive tasks.

Interactions between neural representations of the social and spatial environment

Even in our highly interconnected modern world, geographic factors play an important role in human social connections. Similarly, social relationships influence how and where we travel, and how we think about our spatial world. Here, we review the growing body of neuroscience research that is revealing multiple interactions between social and spatial processes in both humans and non-human animals. We review research on the cognitive and neural representation of spatial and social information, and highlight recent findings suggesting that underlying mechanisms might be common to both. We discuss how spatial factors can influence social behaviour, and how social concepts modify representations of space. In so doing, this review elucidates not only how neural representations of social and spatial information interact but also similarities in how the brain represents and operates on analogous information about its social and spatial surroundings.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

A fast algorithm for All-Pairs-Shortest-Paths suitable for neural networks

Given a directed graph of nodes and edges connecting them, a common problem is to find the shortest path between any two nodes. Here we show that the shortest path distances can be found by a simple matrix inversion: If the edges are given by the adjacency matrix A i j then with a suitably small value of γ the shortest path distances areD i j = ceil ( log γ [ ( I - γ A ) - 1 ] i j ) We derive several graph-theoretic bounds on the value of γ , and explore its useful range with numerics on different graph types. Even when the distance function is not globally accurate across the entire graph, it still works locally to instruct pursuit of the shortest path. In this mode, it also extends to weighted graphs with positive edge weights. For a wide range of dense graphs this distance function is computationally faster than the best available alternative. Finally we show that this method leads naturally to a neural network solution of the all-pairs-shortest-path problem.

Improving Team Members' Attention During the OR Briefing or Time Out

Effective coordination among health care professionals is crucial to achieving optimal outcomes. In the OR, even minor errors can have catastrophic consequences. To mitigate the risk of error, health care professionals have adopted a briefing culture like that used in the aviation industry. Briefings are essential to ensure that everyone involved in a procedure knows the plan and potential risks and is prepared to perform their duties safely and effectively. The fundamental human sense involved in briefings is auditory perception; although important, hearing alone does not equate to focused attention. To enhance the efficacy of briefings, engaging the use of a second sense by adding a visual checklist may increase attentiveness and the chances of early error detection and prevention. Using a projection device may enhance all team members' engagement and participation during the briefing or time-out process and can be an effective tool for improving communication and reducing errors.

Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

From task structures to world models: what do LLMs know?

In what sense does a large language model (LLM) have knowledge? We answer by granting LLMs 'instrumental knowledge': knowledge gained by using next-word generation as an instrument. We then ask how instrumental knowledge is related to the ordinary, 'worldly knowledge' exhibited by humans, and explore this question in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science. We discuss ways LLMs could recover degrees of worldly knowledge and suggest that such recovery will be governed by an implicit, resource-rational tradeoff between world models and tasks. Our answer to this question extends beyond the capabilities of a particular AI system and challenges assumptions about the nature of knowledge and intelligence.

Transport makes cities: transit maps as major cognitive frames of metropolitan areas

Spatial representations play a fundamental role in navigation, decision-making, and overall interaction with our environments. Understanding how individuals construct and use them holds significant importance in spatial cognition research, and even bears practical implications for urban planning as it can explain how we interact with the spaces we inhabit. In large urban areas, transit maps stand as prominent visual aids, guiding people through public transportation systems. These maps, while designed for navigational purposes, may influence how individuals perceive and represent their cities. For instance, Vertesi (Social Studies of Science 38:09-35, 2008) showed through a series of interviews including a "sketch mapping" phase, that London Tube Map seems to structure residents' spatial representation of their city. However, thorough quantitative research on this subject have not been carried out yet. Two experimental studies have been conducted to demonstrate how residents' representations of metropolitan areas closely resemble the schematic representations of their public transport networks. First, we show that residents of Greater Paris-public and private transport users alike-plot city landmarks in a layout more closely resembling that of the Parisian transit map than the geographical map. Next, we asked Greater Berlin, London and Paris residents to place landmarks of their cities on different map backgrounds. A similar procedure was followed for landmarks from an unknown city, after a dedicated learning phase. For known cities, the sketch maps produced were closer to transit maps than to the geographical ones, although less so if the test map background presented topographical elements (e.g., rivers, etc.). For learnt cities, participants' sketch maps were almost exclusively dependent on the map provided during the learning phase. These results suggest that familiarity with transit maps has a direct impact on the metric properties of spatial representation in memory, a phenomenon we propose to call the 'schema effect'.

A common format for representing spatial location in visual and motor working memory

Does the mind rely on similar systems of spatial representation for both perception and action? Here, we assessed the format of location representations in two simple spatial localization tasks. In one task, participants simply remembered the location of an item based solely on visual input. In another, participants remembered the location of a point in space based solely on kinesthetic input. Participants' recall errors were more consistent with the use of polar coordinates than Cartesian coordinates in both tasks. Moreover, measures of spatial bias and performance were correlated across modalities. In a subsequent study, we tested the flexibility with which people use polar coordinates to represent space; we show that the format in which the information is presented to participants influences how that information is encoded and the errors that are made as a result. We suggest that polar coordinates may be a common means of representing location information across visual and motor modalities, but that these representations are also flexible in form.

The spread of affective and semantic valence representations across states

In many decision problems, outcomes are not reached after a single action but rather after a series of events or states. To optimize decisions over multiple states, representations of how good or bad the outcomes are, that is, the outcomes' valence, should spread across states. One mechanism for valence spreading is a temporal, state-independent process in which a single valence representation is updated when an outcome is experienced and fades away afterwards. Each state's valence is based on its temporal proximity to the experienced outcome. An alternative, state-dependent mechanism relies on the structure of transitions between states, updating a separate valence representation for each state according to its spatial distance from the outcomes. We examined how these mechanistic accounts shape the spread of two formats of valence representation, feelings (affective valence) and knowledge (semantic valence), between states. In two pre-registered experiments (N = 585), we used a novel task in which participants move in a four-state maze, one of which contains an outcome. The participants provide self-reports of affective and semantic valence throughout the maze and after finishing it. Results show that the affective representation of negative valence is more localized in state-space than the semantic representation. We also found evidence for the relative reliance of the affective valence on a temporal, state-independent mechanism and of the semantic valence on a structured, state-dependent mechanism. Our findings provide mechanistic accounts for the differences between affective and semantic valence representations and indicate how such representations may play a role in associative learning and decision-making.

Getting LOST: A conceptual framework for supporting and enhancing spatial navigation in aging

Spatial navigation is more difficult and effortful for older than younger individuals, a shift which occurs for a variety of neurological, physical, and cognitive reasons associated with aging. Despite a large body of evidence documenting age-related deficits in spatial navigation, comparatively less research addresses how to facilitate more effective navigation behavior for older adults. Since navigation challenges arise for a variety of reasons in old age, a one-size-fits-all solution is unlikely to work. Here, we introduce a framework for the variety of spatial navigation challenges faced in aging, which we call LOST-Location, Orientation, Spatial mapping, and Transit. The LOST framework builds on evidence from the cognitive neuroscience of spatial navigation, which reveals distinct components underpinning human wayfinding. We evaluate research on navigational aids-devices and depictions-which help people find their way around; and we reflect on how navigation aids solve (or fail to solve) specific wayfinding difficulties faced by older adults. In summary, we emphasize a bespoke approach to improving spatial navigation in aging, which focuses on tailoring navigation solutions to specific navigation challenges. Our hope is that by providing precise support to older navigators, navigation opportunities can facilitate independence and exploration, while minimizing the danger of becoming lost. We conclude by delineating critical knowledge gaps in how to improve older adults' spatial navigation capacities that the novel LOST framework could guide to address. This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition Neuroscience > Behavior.

Unpacking the navigation toolbox: insights from comparative cognition

The study of navigation is informed by ethological data from many species, laboratory investigation at behavioural and neurobiological levels, and computational modelling. However, the data are often species-specific, making it challenging to develop general models of how biology supports behaviour. Wiener et al. outlined a framework for organizing the results across taxa, called the 'navigation toolbox' (Wiener et al. In Animal thinking: contemporary issues in comparative cognition (eds R Menzel, J Fischer), pp. 51-76). This framework proposes that spatial cognition is a hierarchical process in which sensory inputs at the lowest level are successively combined into ever-more complex representations, culminating in a metric or quasi-metric internal model of the world (cognitive map). Some animals, notably humans, also use symbolic representations to produce an external representation, such as a verbal description, signpost or map that allows communication of spatial information or instructions between individuals. Recently, new discoveries have extended our understanding of how spatial representations are constructed, highlighting that the hierarchical relationships are bidirectional, with higher levels feeding back to influence lower levels. In the light of these new developments, we revisit the navigation toolbox, elaborate it and incorporate new findings. The toolbox provides a common framework within which the results from different taxa can be described and compared, yielding a more detailed, mechanistic and generalized understanding of navigation.

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.

Spatial and Temporal Hierarchy for Autonomous Navigation Using Active Inference in Minigrid Environment

Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of the surroundings. This cognitive map is believed to exhibit a hierarchical structure, allowing efficient planning when solving complex navigation tasks. Inspired by human behaviour, this paper presents a scalable hierarchical active inference model for autonomous navigation, exploration, and goal-oriented behaviour. The model uses visual observation and motion perception to combine curiosity-driven exploration with goal-oriented behaviour. Motion is planned using different levels of reasoning, i.e., from context to place to motion. This allows for efficient navigation in new spaces and rapid progress toward a target. By incorporating these human navigational strategies and their hierarchical representation of the environment, this model proposes a new solution for autonomous navigation and exploration. The approach is validated through simulations in a mini-grid environment.

The format of the cognitive map depends on the structure of the environment

Humans and animals form cognitive maps that allow them to navigate through large-scale environments. Here we address a central unresolved question about these maps: whether they exhibit similar characteristics across all environments, or-alternatively-whether different environments yield different types of maps. To investigate this question, we examined spatial learning in three virtual environments: an open courtyard with patios connected by paths (open maze), a set of rooms connected by corridors (closed maze), and a set of isolated rooms connected only by teleporters (teleport maze). All three environments shared the same underlying topological graph structure. Postlearning tests showed that participants formed representations of the three environments that varied in accuracy, format, and individual variability. The open maze was most accurately remembered, followed by the closed maze, and then the teleport maze. In the open maze, most participants developed representations that reflected the Euclidean structure of the space, whereas in the teleport maze, most participants constructed representations that aligned more closely with a mental model of an interconnected graph. In the closed maze, substantial individual variability emerged, with some participants forming Euclidean representations and others forming graph-like representations. These results indicate that an environment's features shape the quality and nature of the spatial representations formed within it, determining whether spatial knowledge takes a Euclidean or graph-like format. Consequently, experimental findings obtained in any single environment may not generalize to others with different features. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Wayfinding across ocean and tundra: what traditional cultures teach us about navigation

Research on human navigation by psychologists and neuroscientists has come mainly from a limited range of environments and participants inhabiting western countries. By contrast, numerous anthropological accounts illustrate the diverse ways in which cultures adapt to their surrounding environment to navigate. Here, we provide an overview of these studies and relate them to cognitive science research. The diversity of cues in traditional navigation is much higher and multimodal compared with navigation experiments in the laboratory. It typically involves an integrated system of methods, drawing on a detailed understanding of the environmental cues, specific tools, and forms part of a broader cultural system. We highlight recent methodological developments for measuring navigation skill and modelling behaviour that will aid future research into how culture and environment shape human navigation.

Metric information in cognitive maps: Euclidean embedding of non-Euclidean environments

The structure of the internal representation of surrounding space, the so-called cognitive map, has long been debated. A Euclidean metric map is the most straight-forward hypothesis, but human navigation has been shown to systematically deviate from the Euclidean ground truth. Vector navigation based on non-metric models can better explain the observed behavior, but also discards useful geometric properties such as fast shortcut estimation and cue integration. Here, we propose another alternative, a Euclidean metric map that is systematically distorted to account for the observed behavior. The map is found by embedding the non-metric model, a labeled graph, into 2D Euclidean coordinates. We compared these two models using data from a human behavioral study where participants had to learn and navigate a non-Euclidean maze (i.e., with wormholes) and perform direct shortcuts between different locations. Even though the Euclidean embedding cannot correctly represent the non-Euclidean environment, both models predicted the data equally well. We argue that the embedding naturally arises from integrating the local position information into a metric framework, which makes the model more powerful and robust than the non-metric alternative. It may therefore be a better model for the human cognitive map.

A neural code for time and space in the human brain

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable-yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written.

Abstract cognitive maps of social network structure aid adaptive inference

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

Collective interactions, collaborative inhibition, and shared spatial knowledge

Research on spatial mental representations focuses on individual mental maps and spatial knowledge. This exploratory study investigates instead collective interactions, collaborative memory, and the sharing of spatial knowledge. Based on the principle of collaborative inhibition (i.e., people recall information less effectively in groups), we posed the following research question: How do collective interactions, occurring during environmental exploration and group drawing sessions, affect collaborative inhibition, and the quality of sketch maps designed collectively? We conducted in situ explorations in Plaine St-Denis (France) with real-time tracking, followed by individual and group drawing sessions. This experiment involved 118 participants divided into three groups: (1) solo explorations without devices; (2) solo explorations with a mobile mapping application; (3) collective explorations using the same application enhanced with interaction features (viewing collective routes and photos of visited places). The comparison of the total number of entities found on individual mental maps with those included in collective sketch maps reveals that collaborative inhibition applies to spatial memory. Additional findings indicate that the use of a map, combined with collective interactions, mitigates collaborative inhibition and increases the accuracy of the sketch maps. However, the effect of such interactions on group dynamics remains unclear as of now.

Generative replay underlies compositional inference in the hippocampal-prefrontal circuit

Human reasoning depends on reusing pieces of information by putting them together in new ways. However, very little is known about how compositional computation is implemented in the brain. Here, we ask participants to solve a series of problems that each require constructing a whole from a set of elements. With fMRI, we find that representations of novel constructed objects in the frontal cortex and hippocampus are relational and compositional. With MEG, we find that replay assembles elements into compounds, with each replay sequence constituting a hypothesis about a possible configuration of elements. The content of sequences evolves as participants solve each puzzle, progressing from predictable to uncertain elements and gradually converging on the correct configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry and a role for generative replay in compositional inference and hypothesis testing.

Measuring configural spatial knowledge: Individual differences in correlations between pointing and shortcutting

People use environmental knowledge to maintain a sense of direction in daily life. This knowledge is typically measured by having people point to unseen locations (judgments of relative direction) or navigate efficiently in the environment (shortcutting). Some people can estimate directions precisely, while others point randomly. Similarly, some people take shortcuts not experienced during learning, while others mainly follow learned paths. Notably, few studies have directly tested the correlation between pointing and shortcutting performance. We compared pointing and shortcutting in two experiments, one using desktop virtual reality (VR) (N = 57) and one using immersive VR (N = 48). Participants learned a new environment by following a fixed route and were then asked to point to unseen locations and navigate to targets by the shortest path. Participants' performance was clustered into two groups using K-means clustering. One (lower ability) group pointed randomly and showed low internal consistency across trials in pointing, but were able to find efficient routes, and their pointing and efficiency scores were not correlated. The others (higher ability) pointed precisely, navigated by efficient routes, and their pointing and efficiency scores were correlated. These results suggest that with the same egocentric learning experience, the correlation between pointing and shortcutting depends on participants' learning ability, and internal consistency and discriminating power of the measures. Inconsistency and limited discriminating power can lead to low correlations and mask factors driving human variation. Psychometric properties, largely under-reported in spatial cognition, can advance our understanding of individual differences and cognitive processes for complex spatial tasks.

A map of spatial navigation for neuroscience

Spatial navigation has received much attention from neuroscientists, leading to the identification of key brain areas and the discovery of numerous spatially selective cells. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between behavioral and neuroscientific researchers. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.

Investigating the different domains of environmental knowledge acquired from virtual navigation and their relationship to cognitive factors and wayfinding inclinations

When learning an environment from virtual navigation people gain knowledge about landmarks, their locations, and the paths that connect them. The present study newly aimed to investigate all these domains of knowledge and how cognitive factors such as visuospatial abilities and wayfinding inclinations might support virtual passive navigation. A total of 270 participants (145 women) were tested online. They: (i) completed visuospatial tasks and answered questionnaires on their wayfinding inclinations; and (ii) learnt a virtual path. The environmental knowledge they gained was assessed on their free recall of landmarks, their egocentric and allocentric pointing accuracy (location knowledge), and their performance in route direction and landmark location tasks (path knowledge). Visuospatial abilities and wayfinding inclinations emerged as two separate factors, and environmental knowledge as a single factor. The SEM model showed that both visuospatial abilities and wayfinding inclinations support the environmental knowledge factor, with similar pattern of relationships in men and women. Overall, factors related to the individual are relevant to the environmental knowledge gained from an online virtual passive navigation.

Using a picture (or a thousand words) for supporting spatial knowledge of a complex virtual environment

External representations powerfully support and augment complex human behavior. When navigating, people often consult external representations to help them find the way to go, but do maps or verbal instructions improve spatial knowledge or support effective wayfinding? Here, we examine spatial knowledge with and without external representations in two studies where participants learn a complex virtual environment. In the first study, we asked participants to generate their own maps or verbal instructions, partway through learning. We found no evidence of improved spatial knowledge in a pointing task requiring participants to infer the direction between two targets, either on the same route or on different routes, and no differences between groups in accurately recreating a map of the target landmarks. However, as a methodological note, pointing was correlated with the accuracy of the maps that participants drew. In the second study, participants had access to an accurate map or set of verbal instructions that they could study while learning the layout of target landmarks. Again, we found no evidence of differentially improved spatial knowledge in the pointing task, although we did find that the map group could recreate a map of the target landmarks more accurately. However, overall improvement was high. There was evidence that the nature of improvement across all conditions was specific to initial navigation ability levels. Our findings add to a mixed literature on the role of external representations for navigation and suggest that more substantial intervention-more scaffolding, explicit training, enhanced visualization, perhaps with personalized sequencing-may be necessary to improve navigation ability.

Generalization of cognitive maps across space and time

Prominent theories posit that associative memory structures, known as cognitive maps, support flexible generalization of knowledge across cognitive domains. Here, we evince a representational account of cognitive map flexibility by quantifying how spatial knowledge formed one day was used predictively in a temporal sequence task 24 hours later, biasing both behavior and neural response. Participants learned novel object locations in distinct virtual environments. After learning, hippocampus and ventromedial prefrontal cortex (vmPFC) represented a cognitive map, wherein neural patterns became more similar for same-environment objects and more discriminable for different-environment objects. Twenty-four hours later, participants rated their preference for objects from spatial learning; objects were presented in sequential triplets from either the same or different environments. We found that preference response times were slower when participants transitioned between same- and different-environment triplets. Furthermore, hippocampal spatial map coherence tracked behavioral slowing at the implicit sequence transitions. At transitions, predictive reinstatement of virtual environments decreased in anterior parahippocampal cortex. In the absence of such predictive reinstatement after sequence transitions, hippocampus and vmPFC responses increased, accompanied by hippocampal-vmPFC functional decoupling that predicted individuals' behavioral slowing after a transition. Collectively, these findings reveal how expectations derived from spatial experience generalize to support temporal prediction.

Exploration patterns shape cognitive map learning

Spontaneous, volitional spatial exploration is crucial for building up a cognitive map of the environment. However, decades of research have primarily measured the fidelity of cognitive maps after discrete, controlled learning episodes. We know little about how cognitive maps are formed during naturalistic free exploration. Here, we investigated whether exploration trajectories predicted cognitive map accuracy, and how these patterns were shaped by environmental structure. In two experiments, participants freely explored a previously unfamiliar virtual environment. We related their exploration trajectories to a measure of how long they spent in areas with high global environmental connectivity (integration, as assessed by space syntax). In both experiments, we found that participants who spent more time on paths that offered opportunities for integration formed more accurate cognitive maps. Interestingly, we found no support for our pre-registered hypothesis that self-reported trait differences in navigation ability would mediate this relationship. Our findings suggest that exploration patterns predict cognitive map accuracy, even for people who self-report low ability, and highlight the importance of considering both environmental structure and individual variability in formal theory- and model-building.

“Mental maps”: Between memorial transcription and symbolic projection

“The mental map” is a concept that has been used and defined in numerous ways. The cognitive map, and the concept map–also known as the “heuristic” or “mind” map–are the two distinct contextual meanings covered by the term mental map in the present article. In the mental map domain, the first major field of study is geography, spatial cognition, and neurophysiology and it aims to understand how the route taken by a subject (or a set of subjects) in space leads to memorization and internal representation(s). In general, the externalization of these representations takes the form of drawings, positioning in a graph, or oral/textual narratives, but it is primarily reflected as a behavior in space that can be recorded as tracking items. A second field of study, one which is geared more toward exploratory and combinatorial uses, is the concept (also heuristic or mind) map which consists in organizing notions, concepts, and information in the form of tree graphs or graphs that can be used to produce diagrams and flowcharts. The aim is projective, for clarification and discovery purposes or for data organization and visualization. To date, very few studies in the literature have examined the similar, overlapping and oppositional features in what is broadly referred to as “representation(s) of space” and “space(s) of representation.” How can we better apprehend the complex notion of “mental map?” The question of memorial transcription? Of “symbolic projection?” Can we identify meeting points between these two polarities and, if possible, a continuum? Through the notion of cognitive graph, recent advances in the understanding of brain mechanisms enable us to approach the distinctions between cognitive map and conceptual map as an articulated and continuous whole.

Formalising social representation to explain psychiatric symptoms

Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.

Representational integration and differentiation in the human hippocampus following goal-directed navigation

As we learn, dynamic memory processes build structured knowledge across our experiences. Such knowledge enables the formation of internal models of the world that we use to plan, make decisions, and act. Recent theorizing posits that mnemonic mechanisms of differentiation and integration - which at one level may seem to be at odds - both contribute to the emergence of structured knowledge. We tested this possibility using fMRI as human participants learned to navigate within local and global virtual environments over the course of 3 days. Pattern similarity analyses on entorhinal cortical and hippocampal patterns revealed evidence that differentiation and integration work concurrently to build local and global environmental representations, and that variability in integration relates to differences in navigation efficiency. These results offer new insights into the neural machinery and the underlying mechanisms that translate experiences into structured knowledge that allows us to navigate to achieve goals.

Different computational relations in language are captured by distinct brain systems

A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.

Rethinking retrosplenial cortex: Perspectives and predictions

The last decade has produced exciting new ideas about retrosplenial cortex (RSC) and its role in integrating diverse inputs. Here, we review the diversity in forms of spatial and directional tuning of RSC activity, temporal organization of RSC activity, and features of RSC interconnectivity with other brain structures. We find that RSC anatomy and dynamics are more consistent with roles in multiple sensorimotor and cognitive processes than with any isolated function. However, two more generalized categories of function may best characterize roles for RSC in complex cognitive processes: (1) shifting and relating perspectives for spatial cognition and (2) prediction and error correction for current sensory states with internal representations of the environment. Both functions likely take advantage of RSC's capacity to encode conjunctions among sensory, motor, and spatial mapping information streams. Together, these functions provide the scaffold for intelligent actions, such as navigation, perspective taking, interaction with others, and error detection.

Spatial memory deficits in Alzheimer's disease and their connection to cognitive maps' formation by place cells and grid cells

Whenever we navigate through different contexts, we build a cognitive map: an internal representation of the territory. Spatial navigation is a complex skill that involves multiple types of information processing and integration. Place cells and grid cells, collectively with other hippocampal and medial entorhinal cortex neurons (MEC), form a neural network whose activity is critical for the representation of self-position and orientation along with spatial memory retrieval. Furthermore, this activity generates new representations adapting to changes in the environment. Though there is a normal decline in spatial memory related to aging, this is dramatically increased in pathological conditions such as Alzheimer's disease (AD). AD is a multi-factorial neurodegenerative disorder affecting mainly the hippocampus-entorhinal cortex (HP-EC) circuit. Consequently, the initial stages of the disease have disorientation and wandering behavior as two of its hallmarks. Recent electrophysiological studies have linked spatial memory deficits to difficulties in spatial information encoding. Here we will discuss map impairment and remapping disruption in the HP-EC network, as a possible circuit mechanism involved in the spatial memory and navigation deficits observed in AD, pointing out the benefits of virtual reality as a tool for early diagnosis and rehabilitation.

Social value at a distance: Higher identification with all of humanity is associated with reduced social discounting

How much we value the welfare of others has critical implications for the collective good. Yet, it is unclear what leads people to make more or less equal decisions about the welfare of those from whom they are socially distant. The current research sought to explore the psychological mechanisms that might underlie welfare judgements across social distance. Here, a social discounting paradigm was used to measure the tendency for the value of a reward to be discounted as the social distance of its recipient increased. Across two cohorts (one discovery, one replication), we found that a more expansive identity with all of humanity was associated with reduced social discounting. Additionally, we investigated the specificity of this association by examining whether this relationship extended to delay discounting, the tendency for the value of a reward to be discounted as the temporal distance to its receipt increases. Our findings suggest that the observed association with identity was unique to social discounting, thus underscoring a distinction in value-based decision-making processes across distances in time and across social networks. As data were collected during the COVID-19 pandemic, we also considered how stress associated with this global threat might influence welfare judgements across social distances. We found that, even after controlling for COVID-19 related stress, correlations between identity and social discounting held. Together, these findings elucidate the psychological processes that are associated with a more equal distribution of generosity.

Building a Cognitive Science of Human Variation: Individual Differences in Spatial Navigation

The aim of this issue is to take stock of cognitive science of human variation in the field of spatial navigation, an important domain in which debates have often assumed an invariant human mind. Addressing the challenge of individual differences requires cognitive scientists to change their practices in several ways. First, we need to consider how to design measures and paradigms that have adequate psychometric characteristics. Second, using reliable, efficient, and valid measures, we need to examine how people vary from time to time, both in the short run due to emotions, such as stress or time pressure, and in the longer run, due to training or living in physical environments that require wayfinding skills. Third, we need to study people different from the traditional college participants, including variations in age, gender, education, culture, physical environment, and possible interactions among these variables.

Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing

The brain encodes the statistical regularities of the environment in a task-specific yet flexible and generalizable format. Here, we seek to understand this process by bridging two parallel lines of research, one centered on sensorimotor timing, and the other on cognitive mapping in the hippocampal system. By combining functional magnetic resonance imaging (fMRI) with a fast-paced time-to-contact (TTC) estimation task, we found that the hippocampus signaled behavioral feedback received in each trial as well as performance improvements across trials along with reward-processing regions. Critically, it signaled performance improvements independent from the tested intervals, and its activity accounted for the trial-wise regression-to-the-mean biases in TTC estimation. This is in line with the idea that the hippocampus supports the rapid encoding of temporal context even on short time scales in a behavior-dependent manner. Our results emphasize the central role of the hippocampus in statistical learning and position it at the core of a brain-wide network updating sensorimotor representations in real time for flexible behavior.

Studying the Development of Navigation Using Virtual Environments

Research on spatial navigation is essential to understanding how mobile species adapt to their environments. Such research increasingly uses virtual environments (VEs) because, although VE has drawbacks, it allows for standardization of procedures, precision in measuring behaviors, ease in introducing variation, and cross-investigator comparability. Developmental researchers have used a wide range of VE testing methods, including desktop computers, gaming consoles, virtual reality, and phone applications. We survey the paradigms to guide researchers' choices, organizing them by their characteristics using a framework proposed by Girard (2022) in which navigation is reactive or deliberative, and may be tied to sensory input or not. This organization highlights what representations each paradigm indicates. VE tools have enriched our picture of the development of navigation, but much research remains to be done, e.g., determining retest reliability, comparing performance on different paradigms, validating performance against real-world behavior and open sharing. Reliable and valid assessments available on open-science repositories are essential for work on the development of navigation, its neural bases, and its implications for other cognitive domains.

Spatial Representations Without Spatial Computations

Cognitive maps are assumed to be fundamentally spatial and grounded only in perceptual processes, as supported by the discovery of functionally dedicated cell types in the human brain, which tile the environment in a maplike fashion. Challenging this view, we demonstrate that spatial representations-such as large-scale geographical maps-can be as well retrieved with high confidence from natural language through cognitively plausible artificial-intelligence models on the basis of nonspatial associative-learning mechanisms. More critically, we show that linguistic information accounts for the specific distortions observed in tasks when college-age adults have to judge the geographical positions of cities, even when these positions are estimated on real maps. These findings indicate that language experience can encode and reproduce cognitive maps without the need for a dedicated spatial-representation system, thus suggesting that the formation of these maps is the result of a strict interplay between spatial- and nonspatial-learning principles.

Neural basis of topographical disorientation in the primate posterior cingulate gyrus based on a labeled graph

Patients with lesions in the posterior cingulate gyrus (PCG), including the retrosplenial cortex (RSC) and posterior cingulate cortex (PCC), cannot navigate in familiar environments, nor draw routes on a 2D map of the familiar environments. This suggests that the topographical knowledge of the environments (i.e., cognitive map) to find the right route to a goal is represented in the PCG, and the patients lack such knowledge. However, theoretical backgrounds in neuronal levels for these symptoms in primates are unclear. Recent behavioral studies suggest that human spatial knowledge is constructed based on a labeled graph that consists of topological connections (edges) between places (nodes), where local metric information, such as distances between nodes (edge weights) and angles between edges (node labels), are incorporated. We hypothesize that the population neural activity in the PCG may represent such knowledge based on a labeled graph to encode routes in both 3D environments and 2D maps. Since no previous data are available to test the hypothesis, we recorded PCG neuronal activity from a monkey during performance of virtual navigation and map drawing-like tasks. The results indicated that most PCG neurons responded differentially to spatial parameters of the environments, including the place, head direction, and reward delivery at specific reward areas. The labeled graph-based analyses of the data suggest that the population activity of the PCG neurons represents the distance traveled, locations, movement direction, and navigation routes in the 3D and 2D virtual environments. These results support the hypothesis and provide a neuronal basis for the labeled graph-based representation of a familiar environment, consistent with PCG functions inferred from the human clinicopathological studies.