Navigating cognition: Spatial codes for human thinking

Hippocampal-entorhinal cognitive maps and cortical motor system represent action plans and their outcomes

Efficiently interacting with the environment requires weighing and selecting among multiple alternative actions based on their associated outcomes. However, the neural mechanisms underlying these processes are still debated. We show that forming relations between arbitrary action-outcome associations involve building a cognitive map. Using an immersive virtual reality paradigm, participants learned 2D abstract motor action-outcome associations and later compared action combinations while their brain activity was monitored with fMRI. We observe a hexadirectional modulation of the activity in entorhinal cortex while participants compared different action plans. Furthermore, hippocampal activity scales with the 2D similarity between outcomes of these action plans. Conversely, the supplementary motor area represents individual actions, showing a stronger response to overlapping action plans. Crucially, the connectivity between hippocampus and supplementary motor area is modulated by the similarity between the action plans, suggesting their complementary roles in action evaluation. These findings provide evidence for the role of cognitive maps in action selection, challenging classical models of memory taxonomy and its neural bases.

Visuomotor Navigation for Embodied Robots With Spatial Memory and Semantic Reasoning Cognition

The fundamental prerequisite for embodied agents to make intelligent decisions lies in autonomous cognition. Typically, agents optimize decision-making by leveraging extensive spatiotemporal information from episodic memory. Concurrently, they utilize long-term experience for task reasoning and foster conscious behavioral tendencies. However, due to the significant disparities in the heterogeneous modalities of these two cognitive abilities, existing literature falls short in designing effective coupling mechanisms, thus failing to endow robots with comprehensive intelligence. This article introduces a navigation framework, the hierarchical topology-semantic cognitive navigation (HTSCN), which seamlessly integrates both memory and reasoning abilities within a singular end-to-end system. Specifically, we represent memory and reasoning abilities with a topological map and a semantic relation graph, respectively, within a unified dual-layer graph structure. Additionally, we incorporate a neural-based cognition extraction process to capture cross-modal relationships between hierarchical graphs. HTSCN forges a link between two different cognitive modalities, thus further enhancing decision-making performance and the overall level of intelligence. Experimental results demonstrate that in comparison to existing cognitive structures, HTSCN significantly enhances the performance and path efficiency of image-goal navigation. Visualization and interpretability experiments further corroborate the promoting role of memory, reasoning, as well as their online learned relationships, on intelligent behavioral patterns. Furthermore, we deploy HTSCN in real-world scenarios to further verify its feasibility and adaptability.

Embedding emotion concepts in cognitive maps

Emotion knowledge is organized in a two-dimensional space known as the affective circumplex, which is thought to develop from core affective feelings and the co-occurrence of emotional events. Neural studies reveal that emotion concepts and cognitive maps of space and abstract concepts are represented in hippocampal-prefrontal systems. We propose that the circumplex is formed by learning the transitions between emotion concepts, a process mediated by a reciprocal network involving hippocampal cells that encode emotion concepts and grid cells in medial entorhinal and ventral prefrontal cortices that encode the relations between them. We anticipate that testing this hypothesis will shed light on the debate about whether emotions are biologically basic or constructed from core affective dimensions.

Shared spectral fingerprints of temporal memory precision and representation of the temporal structure of complex narratives

The ability to date events is fundamental to episodic memory. Separate lines of fMRI research have explored the neurobiological mechanisms underlying temporal precision and the representation of temporal structure in complex events. The present EEG study examined the oscillatory dynamics of both processes in participants performing a timeline positioning task with movie scenes. Multivariate analyses identified a high-beta/low-gamma electrophysiological signature of temporal precision during timeline presentation, involving a right-lateralized network. An independent representation similarity analysis revealed a strong coupling between behavioral and neural distance between pairs of movie parts in the same time-frequency band as the precision effect. Crucially, participants with higher temporal precision showed a stronger correlation between behavioral and neural distance, reinforcing the link between brain signals related to precision and temporal structure representation. These findings support the idea of a systematic temporal organization of experiences, which plays a role in guiding inferential processes.

Human neural dynamics of real-world and imagined navigation

The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and envisioning of the future. While rodent studies suggest the medial temporal lobe, especially the hippocampus, is involved in these functions, its role in human imagination remains uncertain. In human participants, imaginations can be explicitly instructed and reported. Here we investigate hippocampal theta oscillations during real-world and imagined navigation using motion capture and intracranial electroencephalographic recordings from individuals with chronically implanted medial temporal lobe electrodes. Our results revealed intermittent theta dynamics, particularly within the hippocampus, encoding spatial information and partitioning navigational routes into linear segments during real-world navigation. During imagined navigation, theta dynamics exhibited similar patterns despite the absence of external cues. A statistical model successfully reconstructed real-world and imagined positions, providing insights into the neural mechanisms underlying human navigation and imagination, with implications for understanding memory in real-world settings.

Transitive inference as probabilistic preference learning

Transitive inference (TI) is a cognitive task that assesses an organism's ability to infer novel relations between items based on previously acquired knowledge. TI is known for exhibiting various behavioral and neural signatures, such as the serial position effect (SPE), symbolic distance effect (SDE), and the brain's capacity to maintain and merge separate ranking models. We propose a novel framework that casts TI as a probabilistic preference learning task, using one-parameter Mallows models. We present a series of simulations that highlight the effectiveness of our novel approach. We show that the Mallows ranking model natively reproduces SDE and SPE. Furthermore, extending the model using Bayesian selection showcases its capacity to generate and merge ranking hypotheses as pairs with connecting symbols. Finally, we employ neural networks to replicate Mallows models, demonstrating how this framework aligns with observed prefrontal neural activity during TI. Our innovative approach sheds new light on the nature of TI, emphasizing the potential of probabilistic preference learning for unraveling its underlying neural mechanisms.

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.

Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

Schemas, reinforcement learning and the medial prefrontal cortex

Schemas are rich and complex knowledge structures about the typical unfolding of events in a context; for example, a schema of a dinner at a restaurant. In this Perspective, we suggest that reinforcement learning (RL), a computational theory of learning the structure of the world and relevant goal-oriented behaviour, underlies schema learning. We synthesize literature about schemas and RL to offer that three RL principles might govern the learning of schemas: learning via prediction errors, constructing hierarchical knowledge using hierarchical RL, and dimensionality reduction through learning a simplified and abstract representation of the world. We then suggest that the orbitomedial prefrontal cortex is involved in both schemas and RL due to its involvement in dimensionality reduction and in guiding memory reactivation through interactions with posterior brain regions. Last, we hypothesize that the amount of dimensionality reduction might underlie gradients of involvement along the ventral-dorsal and posterior-anterior axes of the orbitomedial prefrontal cortex. More specific and detailed representations might engage the ventral and posterior parts, whereas abstraction might shift representations towards the dorsal and anterior parts of the medial prefrontal cortex.

Integration of Euclidean and path distances in hippocampal maps

The hippocampus is a key region for forming mental maps of our environment. These maps represent spatial information such as distances between landmarks. A cognitive map can allow for flexible inference of spatial relationships that have never been directly experienced before. Previous work has shown that the human hippocampus encodes distances between locations, but it is unclear how Euclidean and path distances are distinguished. In this study, participants performed an object-location task in a virtual environment. We combined functional magnetic resonance imaging with representational similarity analysis to test how Euclidean and path distances are represented in the hippocampus. We observe that hippocampal neural pattern similarity for objects scales with distance between object locations, and suggest that the hippocampus integrates Euclidean and path distances. One key characteristic of cognitive maps is their adaptive and flexible nature. We therefore subsequently modified path distances between objects using roadblocks in the environment. We found that hippocampal pattern similarity between objects adapted as a function of these changes in path distance, selectively in route learners but not in map learners. Taken together, our study supports the idea that the hippocampus creates integrative and flexible cognitive maps.

Higher Precision of Memory for Time Using Verbal-Numerical Versus Visuospatial Reports

A reliable method for exploring the temporal representation of past events is to assess the time of occurrence of discrete stimuli extracted from the original experience on a horizontal timeline representing the event duration. Previous studies using a visual analog scale (VAS) indicate that temporal judgments about movie segments from a commercial movie can be very precise, even when using long audiovisual material at encoding. This result might reflect the benefit of relying on an explicit visuospatial representation of the encoded material when making temporal judgments. To test this hypothesis, the present study compared the temporal memory performance when human observers reported their judgments using either a visual analog scale or verbal-numerical reports, using both a between-subject and a within-subject design. Participants encoded a ∼90 min movie and the next day provided time-of-occurrence judgments on a series of video clips extracted from the movie. In both the within- and the between-subject design, we observed a higher memory precision in the verbal-numeral compared to the visuospatial VAS condition. The results do not align with the idea of a spatial representational bias for temporal memory judgments and suggest a previously unknown memory facilitation effect when temporal memory is assessed using verbal-numerical reports.

Cognitive maps for hierarchical spaces in the human brain

Many of the environments that we navigate through every day are hierarchically organized-they consist of spaces nested within other spaces. How do our mind/brains represent such environments? To address this question, we familiarized participants with a virtual environment consisting of a building within a courtyard, with objects distributed throughout the courtyard and building interior. We then scanned them with fMRI while they performed a memory task that required them to think about spatial relationships within and across the subspaces. Behavioral responses were less accurate and response times were longer on trials requiring integration across the subspaces compared to trials not requiring integration. fMRI response differences between integration and non-integration trials were observed in scene-responsive and medial temporal lobe brain regions, which were correlated the behavioral integration effects in retrosplenial complex, occipital place area, and hippocampus. Multivoxel pattern analyses provided additional evidence for representations in these brain regions that reflected the hierarchical organization of the environment. These results indicate that people form cognitive maps of nested spaces by dividing them into subspaces and using an active cognitive process to integrate the subspaces. Similar mechanisms might be used to support hierarchical coding in memory more broadly.

The medial and lateral orbitofrontal cortex jointly represent the cognitive map of task space

A cognitive map is an internal model of the world's causal structure, crucial for adaptive behaviors. The orbitofrontal cortex (OFC) is central node to decision-making and cognitive map representation. However, it remains unclear how the medial OFC (mOFC) and lateral OFC (lOFC) contribute to the formation of cognitive maps in humans. By performing a multi-step sequential task and multivariate analyses of functional magnetic resonance imaging (fMRI) data, we found that the mOFC and lOFC play complementary but dissociable roles in this process. Specifically, the mOFC represents all hidden task state components. The lOFC and dorsolateral prefrontal cortex (dlPFC) encode abstract rules governing structure knowledge across task states. Furthermore, the two orbitofrontal subregions are functionally connected to share state-hidden information for constructing a representation of the task structure. Collectively, these findings provide an account that can increase our understanding of how the brain constructs abstract cognitive maps in a task-relevant space.

Simultaneous tACS-fMRI reveals state- and frequency-specific modulation of hippocampal-cortical functional connectivity

Non-invasive indirect hippocampal-targeted stimulation is of broad scientific and clinical interest. Transcranial alternating current stimulation (tACS) is appealing because it allows oscillatory stimulation to study hippocampal theta (3-8 Hz) activity. We found that tACS administered during functional magnetic resonance imaging yielded a frequency-, mental state- and topologically-specific effect of theta stimulation (but not other frequencies) enhancing right (but not left) hippocampal-cortical connectivity during resting blocks but not during task blocks. Control analyses showed that this effect was not due to possible stimulation-induced changes in signal quality or head movement. Our findings are promising for targeted network modulations of deep brain structures for research and clinical intervention.

Cognitive maps and schizophrenia

Structured internal representations ('cognitive maps') shape cognition, from imagining the future and counterfactual past, to transferring knowledge to new settings. Our understanding of how such representations are formed and maintained in biological and artificial neural networks has grown enormously. The cognitive mapping hypothesis of schizophrenia extends this enquiry to psychiatry, proposing that diverse symptoms - from delusions to conceptual disorganization - stem from abnormalities in how the brain forms structured representations. These abnormalities may arise from a confluence of neurophysiological perturbations (excitation-inhibition imbalance, resulting in attractor instability and impaired representational capacity) and/or environmental factors such as early life psychosocial stressors (which impinge on representation learning). This proposal thus links knowledge of neural circuit abnormalities, environmental risk factors, and symptoms.

Eye movements follow the dynamic shifts of attention through serial order in verbal working memory

How are arbitrary sequences of verbal information retained and manipulated in working memory? Increasing evidence suggests that serial order in verbal WM is spatially coded and that spatial attention is involved in access and retrieval. Based on the idea that brain areas controlling spatial attention are also involved in oculomotor control, we used eye tracking to reveal how the spatial structure of serial order information is accessed in verbal working memory. In two experiments, participants memorized a sequence of auditory words in the correct order. While their eye movements were being measured, they named the memorized items in a self-determined order in Experiment 1 and in a cued order in Experiment 2. We tested the hypothesis that serial order in verbal working memory interacts with the spatial attention system whereby gaze patterns in visual space closely follow attentional shifts in the internal space of working memory. In both experiments, we found that the gaze shifts in visual space correlated with the spatial shifts of attention along the left-to-right one-dimensional mapping of serial order positions in verbal WM. These findings suggest that spatial attention is employed for dynamically searching through verbal WM and that eye movements reflect the spontaneous association of order and space even in the absence of visuospatial input.

The Geometry and Dynamics of Meaning

An enigma for human languages is that children learn to understand words in their mother tongue extremely fast. The cognitive sciences have not been able to fully understand the mechanisms behind this highly efficient learning process. In order to provide at least a partial answer to this problem, I have developed a cognitive model of the semantics of natural language in terms of conceptual spaces. I present a background to conceptual spaces and provide a brief summary of their main features, in particular how it handles learning of concepts. I then apply the model to give a geometric account of the semantics of different word classes. In particular, I propose a "single-domain hypotheses" for the semantics of all word classes except nouns. These hypotheses provide a partial answer to the enigma of how words are learned. Next, a dynamic cognitive model of events is introduced that replaces and extends the function of thematic roles. I apply it to analyze the meanings of different kinds of verbs. I argue that the model also explains some aspects of syntactic structure. In particular, I propose that a sentence typically refers to an event. Some further applications of conceptual spaces are briefly presented.

Curiosity shapes spatial exploration and cognitive map formation in humans

Cognitive maps are thought to arise, at least in part, from our intrinsic curiosity to explore unknown places. However, it remains untested how curiosity shapes aspects of spatial exploration in humans. Combining a virtual reality task with indices of exploration complexity, we found that pre-exploration curiosity states predicted how much individuals spatially explored environments, whereas markers of visual exploration determined post-exploration feelings of interest. Moreover, individual differences in curiosity traits, particularly Stress Tolerance, modulated the relationship between curiosity and spatial exploration, suggesting the capacity to cope with uncertainty enhances the curiosity-exploration link. Furthermore, both curiosity and spatial exploration predicted how precisely participants could recall spatial-relational details of the environment, as measured by a sketch map task. These results provide new evidence for a link between curiosity and exploratory behaviour, and how curiosity might shape cognitive map formation.

Improved Navigation Performance Through Memory Triggering Maps: A Neurocartographic Approach

When using navigation devices the "cognitive map" created in the user's mind is much more fragmented, incomplete and inaccurate, compared to the mental model of space created when reading a conventional printed map. As users become more dependent on digital devices that reduce orientation skills, there is an urgent need to develop more efficient navigation systems that promote orientation skills. This paper proposes to consider brain processes for creating more efficient maps that use a network of optimally located cardinal lines and landmarks organized to support and stabilize the neurocognitive structures in the brain that promote spatial orientation. This new approach combines neurocognitive insights with classical research on the efficiency of cartographic visualizations. Recent neuroscientific findings show that spatially tuned neurons could be linked to navigation processes. In particular, the activity of grid cells, which appear to be used to process metric information about space, can be influenced by environmental stimuli such as walls or boundaries. Grid cell activity could be used to create a new framework for map-based interfaces that primarily considers the brain structures associated with the encoding and retrieval of spatial information. The new framework proposed in this paper suggests to arrange map symbols in a specific way that the map design helps to stabilize grid cell firing in the brain and by this improve spatial orientation and navigational performance. Spatially oriented cells are active in humans not only when moving in space, but also when imagining moving through an area-such as when reading a map. It seems likely that the activity of grid cells can be stabilized simply by map symbols that are perceived when reading a map.

Evidence for convergence of distributed cortical processing in band-like functional zones in human entorhinal cortex

The wide array of cognitive functions associated with the hippocampus is supported through interactions with the cerebral cortex. However, most of the direct cortical input to the hippocampus originates in the entorhinal cortex, forming the hippocampal-entorhinal system. In humans, the role of the entorhinal cortex in mediating hippocampal-cortical interactions remains unknown. In this study, we used precision neuroimaging to examine the distributed cortical anatomy associated with the human hippocampal-entorhinal system. Consistent with animal anatomy, our results associate different subregions of the entorhinal cortex with different parts of the hippocampus long axis. Furthermore, we find that the entorhinal cortex comprises three band-like zones that are associated with functionally distinct cortical networks. Importantly, the entorhinal cortex bands traverse the proposed human homologs of rodent lateral and medial entorhinal cortices. Finally, we show that the entorhinal cortex is a major convergence area of distributed cortical processing and that the topography of cortical networks associated with the anterior medial temporal lobe mirrors the macroscale structure of high-order cortical processing.

Invariant representations in abstract concept grounding - the physical world in grounded cognition

Grounded cognition states that mental representations of concepts consist of experiential aspects. For example, the concept "cup" consists of the sensorimotor experiences from interactions with cups. Typical modalities in which concepts are grounded are: The sensorimotor system (including interoception), emotion, action, language, and social aspects. Here, we argue that this list should be expanded to include physical invariants (unchanging features of physical motion; e.g., gravity, momentum, friction). Research on physical reasoning consistently demonstrates that physical invariants are represented as fundamentally as other grounding substrates, and therefore should qualify. We assess several theories of concept representation (simulation, conceptual metaphor, conceptual spaces, predictive processing) and their positions on physical invariants. We find that the classic grounded cognition theories, simulation and conceptual metaphor theory, have not considered physical invariants, while conceptual spaces and predictive processing have. We conclude that physical invariants should be included into grounded cognition theories, and that the core mechanisms of simulation and conceptual metaphor theory are well suited to do this. Furthermore, conceptual spaces and predictive processing are very promising and should also be integrated with grounded cognition in the future.

Integrating Neural Radiance Fields End-to-End for Cognitive Visuomotor Navigation

We propose an end-to-end visuomotor navigation framework that leverages Neural Radiance Fields (NeRF) for spatial cognition. To the best of our knowledge, this is the first effort to integrate such implicit spatial representation with embodied policy end-to-end for cognitive decision-making. Consequently, our system does not necessitate modularized designs nor transformations into explicit scene representations for downstream control. The NeRF-based memory is constructed online during navigation, without relying on any environmental priors. To enhance the extraction of decision-critical historical insights from the rigid and implicit structure of NeRF, we introduce a spatial information extraction mechanism named Structural Radiance Attention (SRA). SRA empowers the agent to grasp complex scene structures and task objectives, thus paving the way for the development of intelligent behavioral patterns. Our comprehensive testing in image-goal navigation tasks demonstrates that our approach significantly outperforms existing navigation models. We demonstrate that SRA markedly improves the agent's understanding of both the scene and the task by retrieving historical information stored in NeRF memory. The agent also learns exploratory awareness from our pipeline to better adapt to low signal-to-noise memory signals in unknown scenes. We deploy our navigation system on a mobile robot in real-world scenarios, where it exhibits evident cognitive capabilities while ensuring real-time performance.

Pulmonary Flora-Derived Lipopolysaccharide Mediates Lung-Brain Axis through Activating Microglia Involved in Polystyrene Microplastic-Induced Cognitive Dysfunction

Microplastics (MPs) have been detected in the atmospheric and the human respiratory system, indicating that the respiratory tract is a significant exposure route for MPs. However, the effect of inhaled MPs on cognitive function has not been adequately studied. Here, a C57BL/6 J mouse model of inhalation exposure to polystyrene MPs (PS-MPs, 5 µm, 60 d) is established by intratracheal instillation. Interestingly, in vivo fluorescence imaging and transmission electron microscopy reveal that PS-MPs do not accumulate in the brain. However, behavioral experiments shows that cognitive function of mice is impaired, accompanied by histopathological damage of lung and brain tissue. Transcriptomic studies in hippocampal and lung tissue have demonstrated key neuroplasticity factors as well as cognitive deficits linked to lung injury, respectively. Mechanistically, the lung-brain axis plays a central role in PS-MPs-induced neurological damage, as demonstrated by pulmonary flora transplantation, lipopolysaccharide (LPS) intervention, and cell co-culture experiments. Together, inhalation of PS-MPs reduces cognitive function by altering the composition of pulmonary flora to produce more LPS and promoting M1 polarization of microglia, which provides new insights into the mechanism of nerve damage caused by inhaled MPs and also sheds new light on the prevention of neurotoxicity of environmental pollutants.

A hierarchical active inference model of spatial alternation tasks and the hippocampal-prefrontal circuit

Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Physical space navigation involves hippocampal (HC) allocentric codes, while abstract task space engages medial prefrontal cortex (mPFC) task-specific codes. Previous studies show that challenging tasks, like spatial alternation, require integrating these two types of maps. The disruption of the HC-mPFC circuit impairs performance. We propose a hierarchical active inference model clarifying how this circuit solves spatial interaction tasks by bridging physical and task-space maps. Simulations demonstrate that the model's dual layers develop effective cognitive maps for physical and task space. The model solves spatial alternation tasks through reciprocal interactions between the two layers. Disrupting its communication impairs decision-making, which is consistent with empirical evidence. Additionally, the model adapts to switching between multiple alternation rules, providing a mechanistic explanation of how the HC-mPFC circuit supports spatial alternation tasks and the effects of disruption.

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.

Revisiting serotonin's role in spatial memory: A call for sensitive analytical approaches

The serotonergic system is involved in various psychiatric and neurological conditions, with serotonergic drugs often used in treatment. These conditions frequently affect spatial memory, which can serve as a model of declarative memory due to well-known cellular components and advanced methods that track neural activity and behavior with high temporal resolution. However, most findings on serotonin's effects on spatial learning and memory come from studies lacking refined analytical techniques and modern approaches needed to uncover the underlying neuronal mechanisms. This In Focus review critically investigates available studies to identify areas for further exploration. It finds that well-established behavioral models could yield more insights with modern tracking and data analysis approaches, while the cellular aspects of spatial memory remain underexplored. The review highlights the complex role of serotonin in spatial memory, which holds the potential for better understanding and treating memory-related disorders.

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.

Replay shapes abstract cognitive maps for efficient social navigation

To make adaptive social decisions, people must anticipate how information flows through their social network. While this requires knowledge of how people are connected, networks are too large to have first-hand experience with every possible route between individuals. How, then, are people able to accurately track information flow through social networks? Here we find that people immediately cache abstract knowledge about social network structure as they learn who is friends with whom, which enables the identification of efficient routes between remotely connected individuals. These cognitive maps of social networks, which are built while learning, are then reshaped through overnight rest. During these extended periods of rest, a replay-like mechanism helps to make these maps increasingly abstract, which privileges improvements in social navigation accuracy for the longest communication paths that span distinct communities within the network. Together, these findings provide mechanistic insight into the sophisticated mental representations humans use for social navigation.

Architectural styles of curiosity in global Wikipedia mobile app readership

Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia's mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic "busybody" and the targeted "hunter." Further, we find evidence for another style-the "dancer"-which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia's global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

The medial entorhinal cortex encodes multisensory spatial information

Animals employ spatial information in multisensory modalities to navigate their natural environments. However, it is unclear whether the brain encodes such information in separate cognitive maps or integrates it all into a single, universal map. We address this question in the microcircuit of the medial entorhinal cortex (MEC), a cognitive map of space. Using cellular-resolution calcium imaging, we examine the MEC of mice navigating virtual reality tracks, where visual and auditory cues provide comparable spatial information. We uncover two cell types: "unimodality cells" and "multimodality cells." The unimodality cells specifically represent either auditory or visual spatial information. They are anatomically intermingled and maintain sensory preferences across multiple tracks and behavioral states. The multimodality cells respond to both sensory modalities, with their responses shaped differentially by auditory or visual information. Thus, the MEC enables accurate spatial encoding during multisensory navigation by computing spatial information in different sensory modalities and generating distinct maps.

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'.

Why do bats fly into cave doors? Inattentional blindness in echolocating animals

Echolocating bats can navigate complex 3D environments by integrating prior knowledge of spatial layouts and real-time sensory cues. This study demonstrates that inattentional blindness to sensory information undermines successful navigation in Egyptian fruit bats, Rousettus aegyptiacus , a species that has access to vision and echolocation to traverse natural environments. Bats flew over repeated trials to a perch at a fixed location in the light, allowing them to navigate using both vision and echolocation. The experiment was then repeated in the dark to exclude the bat's use of vision. The perch was subsequently displaced by either 15 or 30 cm in one of six different directions (up, down, left, right, front, back). Echolocation behavior was recorded using a 25-channel microphone array, while flight paths were tracked using 13 motion capture cameras. The directional aim of echolocation clicks served as a metric for the bat's spatial attention to locations in their environment. In the light, bats modified their flight paths to successfully land on a perch that was moved 15 cm but surprisingly, often failed to land on it when displaced by 30 cm. In the dark, bats often failed to land on the perch after it was moved by only 15 cm. Landing failures suggest that learned spatial priors invoked inattentional blindness to changes in the environment, which interfered with successful navigation. In both the light and dark, when bats failed to land on the perch at its new location, they directed their attention toward the original perch position. Performance differences in the light and dark suggest that the bat's attentional spotlight may be narrower when it relies on echolocation than vision. To our knowledge, these findings provide the first evidence of inattentional blindness in a flying echolocating animal, demonstrating that spatial priors can dominate sensory processing during navigation.

Multifunctional human visual pathway-replicated hardware based on 2D materials

Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.

Quality space computations for consciousness

The quality space hypothesis about conscious experience proposes that conscious sensory states are experienced in relation to other possible sensory states. For instance, the colour red is experienced as being more like orange, and less like green or blue. Recent empirical findings suggest that subjective similarity space can be explained in terms of similarities in neural activation patterns. Here, we consider how localist, workspace, and higher-order theories of consciousness can accommodate claims about the qualitative character of experience and functionally support a quality space. We review existing empirical evidence for each of these positions, and highlight novel experimental tools, such as altering local activation spaces via brain stimulation or behavioural training, that can distinguish these accounts.

Experience of Euclidean geometry sculpts the development and dynamics of rodent hippocampal sequential cell assemblies

Euclidean space is the fabric of the world we live in. Whether and how geometric experience shapes our spatial-temporal representations of the world remained unknown. We deprived male rats of experience with crucial features of Euclidean geometry by rearing them inside spheres, and compared activity of large hippocampal neuronal ensembles during navigation and sleep with that of cuboid cage-reared controls. Sphere-rearing from birth permitted emergence of accurate neuronal ensemble spatial codes and preconfigured and plastic time-compressed neuronal sequences. However, sphere-rearing led to diminished individual place cell tuning, more similar neuronal mapping of different track ends/corners, and impaired pattern separation and plasticity of multiple linear tracks, coupled with reduced preconfigured sleep network repertoires. Subsequent experience with multiple linear environments over four days largely reversed these effects. Thus, early-life experience with Euclidean geometry enriches the hippocampal repertoire of preconfigured neuronal patterns selected toward unique representation and discrimination of multiple linear environments.

Shared structure facilitates working memory of multiple sequences

Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.

The immersive virtual memory task: Assessing object-location memory in neurological patients using immersive virtual reality

TRIAL REGISTRATION

German Clinical Trials Register identifier: DRKS00024005.

Electrophysiological signatures of veridical head direction in humans

Information about heading direction is critical for navigation as it provides the means to orient ourselves in space. However, given that veridical head-direction signals require physical rotation of the head and most human neuroimaging experiments depend upon fixing the head in position, little is known about how the human brain is tuned to such heading signals. Here we adress this by asking 52 healthy participants undergoing simultaneous electroencephalography and motion tracking recordings (split into two experiments) and 10 patients undergoing simultaneous intracranial electroencephalography and motion tracking recordings to complete a series of orientation tasks in which they made physical head rotations to target positions. We then used a series of forward encoding models and linear mixed-effects models to isolate electrophysiological activity that was specifically tuned to heading direction. We identified a robust posterior central signature that predicts changes in veridical head orientation after regressing out confounds including sensory input and muscular activity. Both source localization and intracranial analysis implicated the medial temporal lobe as the origin of this effect. Subsequent analyses disentangled head-direction signatures from signals relating to head rotation and those reflecting location-specific effects. Lastly, when directly comparing head direction and eye-gaze-related tuning, we found that the brain maintains both codes while actively navigating, with stronger tuning to head direction in the medial temporal lobe. Together, these results reveal a taxonomy of population-level head-direction signals within the human brain that is reminiscent of those reported in the single units of rodents.

Decomposing geographical judgments into spatial, temporal and linguistic components

When mentally exploring maps representing large-scale environments (e.g., countries or continents), humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent this type of maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information (which plays the major role in explaining participants' performance) in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring maps representing large-scale environments, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles.

Distance and grid-like codes support the navigation of abstract social space in the human brain

People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a 'social cognitive map' organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

Predictability, an Orrery, and a Speciation Machine: Quest for a Standard Model of Speciation

Accurate predictions are commonly taken as a hallmark of strong scientific understanding. Yet, we do not seem capable today of making many accurate predictions about biological speciation. Why? What limits predictability in general, what exactly is the function and value of predictions, and how might we go about predicting new species? Inspired by an orrery used to explain solar eclipses, we address these questions with a thought experiment in which we conceive an evolutionary speciation machine generating new species. This experiment highlights complexity, chance, and speciation pluralism as the three fundamental challenges for predicting speciation. It also illustrates the methodological value of predictions in testing and improving conceptual models. We then outline how we might move from the hypothetical speciation machine to a predictive standard model of speciation. Operationalizing, testing, and refining this model will require a concerted shift to large-scale, integrative, and interdisciplinary efforts across the tree of life. This endeavor, paired with technological advances, may reveal apparently stochastic processes to be deterministic, and promises to expand the breadth and depth of our understanding of speciation and more generally, of evolution.

Differential relational memory impairment in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is typically associated with pathology of the hippocampus, a key structure involved in relational memory, including episodic, semantic, and spatial memory processes. While it is widely accepted that TLE-associated hippocampal alterations underlie memory deficits, it remains unclear whether impairments relate to a specific cognitive domain or multiple ones.

METHODS

We administered a recently validated task paradigm to evaluate episodic, semantic, and spatial memory in 24 pharmacoresistant TLE patients and 50 age- and sex-matched healthy controls. We carried out two-way analyses of variance to identify memory deficits in individuals with TLE relative to controls across different relational memory domains, and used partial least squares correlation to identify factors contributing to variations in relational memory performance across both cohorts.

RESULTS

Compared to controls, TLE patients showed marked impairments in episodic and spatial memory, with mixed findings in semantic memory. Even when additionally controlling for age, sex, and overall cognitive function, between-group differences persisted along episodic and spatial domains. Moreover, age, diagnostic group, and hippocampal volume were all associated with relational memory behavioral phenotypes.

SIGNIFICANCE

Our behavioral findings show graded deficits across relational memory domains in people with TLE, which provides further insights into the complex pattern of cognitive impairment in the condition.

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.

Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex

How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.

Spontaneous eye movements reflect the representational geometries of conceptual spaces

Functional neuroimaging studies indicate that the human brain can represent concepts and their relational structure in memory using coding schemes typical of spatial navigation. However, whether we can read out the internal representational geometries of conceptual spaces solely from human behavior remains unclear. Here, we report that the relational structure between concepts in memory might be reflected in spontaneous eye movements during verbal fluency tasks: When we asked participants to randomly generate numbers, their eye movements correlated with distances along the left-to-right one-dimensional geometry of the number space (mental number line), while they scaled with distance along the ring-like two-dimensional geometry of the color space (color wheel) when they randomly generated color names. Moreover, when participants randomly produced animal names, eye movements correlated with low-dimensional similarity in word frequencies. These results suggest that the representational geometries used to internally organize conceptual spaces might be read out from gaze behavior.

Integration of cognitive tasks into artificial general intelligence test for large models

During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

Space as a scaffold for rotational generalisation of abstract concepts

Learning invariances allows us to generalise. In the visual modality, invariant representations allow us to recognise objects despite translations or rotations in physical space. However, how we learn the invariances that allow us to generalise abstract patterns of sensory data ('concepts') is a longstanding puzzle. Here, we study how humans generalise relational patterns in stimulation sequences that are defined by either transitions on a nonspatial two-dimensional feature manifold, or by transitions in physical space. We measure rotational generalisation, i.e., the ability to recognise concepts even when their corresponding transition vectors are rotated. We find that humans naturally generalise to rotated exemplars when stimuli are defined in physical space, but not when they are defined as positions on a nonspatial feature manifold. However, if participants are first pre-trained to map auditory or visual features to spatial locations, then rotational generalisation becomes possible even in nonspatial domains. These results imply that space acts as a scaffold for learning more abstract conceptual invariances.

Changes in spatial self-consciousness elicit grid cell-like representation in the entorhinal cortex

Grid cells in the entorhinal cortex (EC) encode an individual's location in space, integrating both environmental and multisensory bodily cues. Notably, body-derived signals are also primary signals for the sense of self. While studies have demonstrated that continuous application of visuo-tactile bodily stimuli can induce perceptual shifts in self-location, it remains unexplored whether these illusory changes suffice to trigger grid cell-like representation (GCLR) within the EC, and how this compares to GCLR during conventional virtual navigation. To address this, we systematically induced illusory drifts in self-location toward controlled directions using visuo-tactile bodily stimulation, while maintaining the subjects' visual viewpoint fixed (absent conventional virtual navigation). Subsequently, we evaluated the corresponding GCLR in the EC through functional MRI analysis. Our results reveal that illusory changes in perceived self-location (independent of changes in environmental navigation cues) can indeed evoke entorhinal GCLR, correlating in strength with the magnitude of perceived self-location, and characterized by similar grid orientation as during conventional virtual navigation in the same virtual room. These data demonstrate that the same grid-like representation is recruited when navigating based on environmental, mainly visual cues, or when experiencing illusory forward drifts in self-location, driven by perceptual multisensory bodily cues.

Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning

Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.