Coding of navigational affordances in the human visual system

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.

Task-modulated neural responses in scene-selective regions of the human brain

The study of scene perception is crucial to the understanding of how one interprets and interacts with their environment, and how the environment impacts various cognitive functions. The literature so far has mainly focused on the impact of low-level and categorical properties of scenes and how they are represented in the scene-selective regions in the brain, PPA, RSC, and OPA. However, higher-level scene perception and the impact of behavioral goals is a developing research area. Moreover, the selection of the stimuli has not been systematic and mainly focused on outdoor environments. In this fMRI experiment, we adopted multiple behavioral tasks, selected real-life indoor stimuli with a systematic categorization approach, and used various multivariate analysis techniques to explain the neural modulation of scene perception in the scene-selective regions of the human brain. Participants (N = 21) performed categorization and approach-avoidance tasks during fMRI scans while they were viewing scenes from built environment categories based on different affordances ((i)access and (ii)circulation elements, (iii)restrooms and (iv)eating/seating areas). ROI-based classification analysis revealed that the OPA was significantly successful in decoding scene category regardless of the task, and that the task condition affected category decoding performances of all the scene-selective regions. Model-based representational similarity analysis (RSA) revealed that the activity patterns in scene-selective regions are best explained by task. These results contribute to the literature by extending the task and stimulus content of scene perception research, and uncovering the impact of behavioral goals on the scene-selective regions of the brain.

Visuospatial computations vary by category and stream and continue to develop in adolescence

Reading, face recognition, and navigation are supported by visuospatial computations in category-selective regions across ventral, lateral, and dorsal visual streams. However, the nature of visuospatial computations across streams and their development in adolescence remain unknown. Using fMRI and population receptive field (pRF) modeling in adolescents and adults, we estimate pRFs in high-level visual cortex and determine their development. Results reveal that pRF location, size, and visual field coverage vary across category, stream, and hemisphere in both adolescents and adults. While pRF location is mature by adolescence, pRF size and visual field coverage continue to develop - increasing in face-selective and decreasing in place-selective regions - alongside similar development of category selectivity. These findings provide a timeline for differential development of visual functions and suggest that visuospatial computations in high-level visual cortex continue to be optimized to accommodate both category and stream demands through adolescence.

Topography of scene memory and perception activity in posterior cortex - a publicly available resource

Adaptive behavior in complex environments requires integrating visual perception with memory of our spatial environment. Recent work has implicated three brain areas in posterior cerebral cortex - the place memory areas (PMAs) that are anterior to the three visual scene perception areas (SPAs) - in this function. However, PMAs' relationship to the broader cortical hierarchy remains unclear due to limited group-level characterization. Here, we examined the PMA and SPA locations across three fMRI datasets (44 participants, 29 female). SPAs were identified using a standard visual localizer where participants viewed scenes versus faces. PMAs were identified by contrasting activity when participants recalled personally familiar places versus familiar faces (Datasets 1-2) or places versus multiple categories (familiar faces, bodies, and objects, and famous faces; Dataset 3). Across datasets, the PMAs were located anterior to the SPAs on the ventral and lateral cortical surfaces. The anterior displacement between PMAs and SPAs was highly reproducible. Compared to public atlases, the PMAs fell at the boundary between externally-oriented networks (dorsal attention) and internally-oriented networks (default mode). Additionally, while SPAs overlapped with retinotopic maps, the PMAs were consistently located anterior to mapped visual cortex. These results establish the anatomical position of the PMAs at inflection points along the cortical hierarchy between unimodal sensory and transmodal, apical regions, which informs broader theories of how the brain integrates perception and memory for scenes. We have released probabilistic parcels of these regions to facilitate future research into their roles in spatial cognition.

Spatial Contextual Information Modulates Affordance Processing and Early Electrophysiological Markers of Scene Perception

Scene perception allows humans to extract information from their environment and plan navigation efficiently. The automatic extraction of potential paths in a scene, also referred to as navigational affordance, is supported by scene-selective regions (SSRs) that enable efficient human navigation. Recent evidence suggests that the activity of these SSRs can be influenced by information from adjacent spatial memory areas. However, it remains unexplored how this contextual information could influence the extraction of bottom-up information, such as navigational affordances, from a scene and the underlying neural dynamics. Therefore, we analyzed ERPs in 26 young adults performing scene and spatial memory tasks in artificially generated rooms with varying numbers and locations of available doorways. We found that increasing the number of navigational affordances only impaired performance in the spatial memory task. ERP results showed a similar pattern of activity for both tasks, but with increased P2 amplitude in the spatial memory task compared with the scene memory. Finally, we reported no modulation of the P2 component by the number of affordances in either task. This modulation of early markers of visual processing suggests that the dynamics of SSR activity are influenced by a priori knowledge, with increased amplitude when participants have more contextual information about the perceived scene. Overall, our results suggest that prior spatial knowledge about the scene, such as the location of a goal, modulates early cortical activity associated with SSRs, and that this information may interact with bottom-up processing of scene content, such as navigational affordances.

Combined representation of visual features in the scene-selective cortex

Visual features of separable dimensions conjoin to represent an integrated entity. We investigated how visual features bind to form a complex visual scene. Specifically, we focused on features important for visually guided navigation: direction and distance. Previously, separate works have shown that directions and distances of navigable paths are coded in the occipital place area (OPA). Using functional magnetic resonance imaging (fMRI), we tested how separate features are concurrently represented in the OPA. Participants saw eight types of scenes, in which four of them had one path and the other four had two paths. In single-path scenes, path direction was either to the left or to the right. In double-path scenes, both directions were present. A glass wall was placed in some paths to restrict navigational distance. To test how the OPA represents path directions and distances, we took three approaches. First, the independent-features approach examined whether the OPA codes each direction and distance. Second, the integrated-features approach explored how directions and distances are integrated into path units, as compared to pooled features, using double-path scenes. Finally, the integrated-paths approach asked how separate paths are combined into a scene. Using multi-voxel pattern similarity analysis, we found that the OPA's representations of single-path scenes were similar to other single-path scenes of either the same direction or the same distance. Representations of double-path scenes were similar to the combination of two constituent single-paths, as a combined unit of direction and distance rather than as a pooled representation of all features. These results show that the OPA combines the two features to form path units, which are then used to build multiple-path scenes. Altogether, these results suggest that visually guided navigation may be supported by the OPA that automatically and efficiently combines multiple features relevant for navigation and represent a navigation file.

Retinotopy drives the variation in scene responses across visual field map divisions of the occipital place area

The occipital place area (OPA) is a scene-selective region on the lateral surface of human occipitotemporal cortex that spatially overlaps multiple visual field maps, as well as portions of cortex that are not currently defined as retinotopic. Here we combined population receptive field modeling and responses to scenes in a representational similarity analysis (RSA) framework to test the prediction that the OPA's visual field map divisions contribute uniquely to the overall pattern of scene selectivity within the OPA. Consistent with this prediction, the patterns of response to a set of complex scenes were heterogeneous between maps. To explain this heterogeneity, we tested the explanatory power of seven candidate models using RSA. These models spanned different scene dimensions (Content, Expanse, Distance), low- and high-level visual features, and navigational affordances. None of the tested models could account for the variation in scene response observed between the OPA's visual field maps. However, the heterogeneity in scene response was correlated with the differences in retinotopic profiles across maps. These data highlight the need to carefully examine the relationship between regions defined as category-selective and the underlying retinotopy, and they suggest that, in the case of the OPA, it may not be appropriate to conceptualize it as a single scene-selective region.

Human brain dynamics dissociate early perceptual and late motor-related stages of affordance processing

Affordances, the opportunities for action offered by the environment to an agent, are vital for meaningful behaviour and exist in every interaction with the environment. There is an ongoing debate in the field about whether the perception of affordances is an automated process. Some studies suggest that affordance perception is an automated process that is independent from the visual context and bodily interaction with the environment, whereas others argue that it is modulated by the visual and motor context in which affordances are perceived. The present paper aims to resolve this debate by examining affordance automaticity from the perspective of sensorimotor time windows. To investigate the impact of different forms of bodily interactions with an environment, that is, the movement context (physical vs. joystick movement), we replicated a previous study on affordance perception in which participants actively moved through differently wide doors in an immersive 3D virtual environment. In the present study, we displayed the same environment on a 2D screen with participants moving through doors of different widths using the keys on a standard keyboard. We compared components of the event-related potential (ERP) from the continuously recorded electroencephalogram (EEG) that were previously reported to be related to affordance perception of architectural transitions (passable and impassable doors). Comparing early sensory and later motor-related ERPs, our study replicated ERPs reflecting early affordance perception but found differences in later motor-related components. These results indicate a shift from automated perception of affordances during early sensorimotor time windows to movement context dependence of affordance perception at later stages, suggesting that affordance perception is a dynamic and flexible process that changes over sensorimotor stages.

A scene with an invisible wall - navigational experience shapes visual scene representation

Human navigation heavily relies on visual information. Although many previous studies have investigated how navigational information is inferred from visual features of scenes, little is understood about the impact of navigational experience on visual scene representation. In this study, we examined how navigational experience influences both the behavioral and neural responses to a visual scene. During training, participants navigated in the virtual reality (VR) environments which we manipulated navigational experience while holding the visual properties of scenes constant. Half of the environments allowed free navigation (navigable), while the other half featured an 'invisible wall' preventing the participants to continue forward even though the scene was visually navigable (non-navigable). During testing, participants viewed scene images from the VR environment while completing either a behavioral perceptual identification task (Experimentl) or an fMRI scan (Experiment2). Behaviorally, we found that participants judged a scene pair to be significantly more visually different if their prior navigational experience varied, even after accounting for visual similarities between the scene pairs. Neurally, multi-voxel pattern of the parahippocampal place area (PPA) distinguished visual scenes based on prior navigational experience alone. These results suggest that the human visual scene cortex represents information about navigability obtained through prior experience, beyond those computable from the visual properties of the scene. Taken together, these results suggest that scene representation is modulated by prior navigational experience to help us construct a functionally meaningful visual environment.

Memorability shapes perceived time (and vice versa)

Visual stimuli are known to vary in their perceived duration. Some visual stimuli are also known to linger for longer in memory. Yet, whether these two features of visual processing are linked is unknown. Despite early assumptions that time is an extracted or higher-order feature of perception, more recent work over the past two decades has demonstrated that timing may be instantiated within sensory modality circuits. A primary location for many of these studies is the visual system, where duration-sensitive responses have been demonstrated. Furthermore, visual stimulus features have been observed to shift perceived duration. These findings suggest that visual circuits mediate or construct perceived time. Here we present evidence across a series of experiments that perceived time is affected by the image properties of scene size, clutter and memorability. More specifically, we observe that scene size and memorability dilate time, whereas clutter contracts it. Furthermore, the durations of more memorable images are also perceived more precisely. Conversely, the longer the perceived duration of an image, the more memorable it is. To explain these findings, we applied a recurrent convolutional neural network model of the ventral visual system, in which images are progressively processed over time. We find that more memorable images are processed faster, and that this increase in processing speed predicts both the lengthening and the increased precision of perceived durations. These findings provide evidence for a link between image features, time perception and memory that can be further explored with models of visual processing.

Immersive scene representation in human visual cortex with ultra-wide-angle neuroimaging

While human vision spans 220°, traditional functional MRI setups display images only up to central 10-15°. Thus, it remains unknown how the brain represents a scene perceived across the full visual field. Here, we introduce a method for ultra-wide angle display and probe signatures of immersive scene representation. An unobstructed view of 175° is achieved by bouncing the projected image off angled-mirrors onto a custom-built curved screen. To avoid perceptual distortion, scenes are created with wide field-of-view from custom virtual environments. We find that immersive scene representation drives medial cortex with far-peripheral preferences, but shows minimal modulation in classic scene regions. Further, scene and face-selective regions maintain their content preferences even with extreme far-periphery stimulation, highlighting that not all far-peripheral information is automatically integrated into scene regions computations. This work provides clarifying evidence on content vs. peripheral preferences in scene representation and opens new avenues to research immersive vision.

Response to commentaries on What Babies Know

Twenty-five commentaries raise questions concerning the origins of knowledge, the interplay of iconic and propositional representations in mental life, the architecture of numerical and social cognition, the sources of uniquely human cognitive capacities, and the borders among core knowledge, perception, and thought. They also propose new methods, drawn from the vibrant, interdisciplinary cognitive sciences, for addressing these questions and deepening understanding of infant minds.

Common and specific activations supporting optic flow processing and navigation as revealed by a meta-analysis of neuroimaging studies

Optic flow provides useful information in service of spatial navigation. However, whether brain networks supporting these two functions overlap is still unclear. Here we used Activation Likelihood Estimation (ALE) to assess the correspondence between brain correlates of optic flow processing and spatial navigation and their specific neural activations. Since computational and connectivity evidence suggests that visual input from optic flow provides information mainly during egocentric navigation, we further tested the correspondence between brain correlates of optic flow processing and that of both egocentric and allocentric navigation. Optic flow processing shared activation with egocentric (but not allocentric) navigation in the anterior precuneus, suggesting its role in providing information about self-motion, as derived from the analysis of optic flow, in service of egocentric navigation. We further documented that optic flow perception and navigation are partially segregated into two functional and anatomical networks, i.e., the dorsal and the ventromedial networks. Present results point to a dynamic interplay between the dorsal and ventral visual pathways aimed at coordinating visually guided navigation in the environment.

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.

Representation of navigational affordances and ego-motion in the occipital place area

Humans effortlessly use vision to plan and guide navigation through the local environment, or "scene". A network of three cortical regions responds selectively to visual scene information, including the occipital (OPA), parahippocampal (PPA), and medial place areas (MPA) - but how this network supports visually-guided navigation is unclear. Recent evidence suggests that one region in particular, the OPA, supports visual representations for navigation, while PPA and MPA support other aspects of scene processing. However, most previous studies tested only static scene images, which lack the dynamic experience of navigating through scenes. We used dynamic movie stimuli to test whether OPA, PPA, and MPA represent two critical kinds of navigationally-relevant information: navigational affordances (e.g., can I walk to the left, right, or both?) and ego-motion (e.g., am I walking forward or backward? turning left or right?). We found that OPA is sensitive to both affordances and ego-motion, as well as the conflict between these cues - e.g., turning toward versus away from an open doorway. These effects were significantly weaker or absent in PPA and MPA. Responses in OPA were also dissociable from those in early visual cortex, consistent with the idea that OPA responses are not merely explained by lower-level visual features. OPA responses to affordances and ego-motion were stronger in the contralateral than ipsilateral visual field, suggesting that OPA encodes navigationally relevant information within an egocentric reference frame. Taken together, these results support the hypothesis that OPA contains visual representations that are useful for planning and guiding navigation through scenes.

Visual features are processed before navigational affordances in the human brain

To navigate through their immediate environment humans process scene information rapidly. How does the cascade of neural processing elicited by scene viewing to facilitate navigational planning unfold over time? To investigate, we recorded human brain responses to visual scenes with electroencephalography and related those to computational models that operationalize three aspects of scene processing (2D, 3D, and semantic information), as well as to a behavioral model capturing navigational affordances. We found a temporal processing hierarchy: navigational affordance is processed later than the other scene features (2D, 3D, and semantic) investigated. This reveals the temporal order with which the human brain computes complex scene information and suggests that the brain leverages these pieces of information to plan navigation.

"Walking selectivity" in the occipital place area in 8-year-olds, not 5-year-olds

A recent neuroimaging study in adults found that the occipital place area (OPA)-a cortical region involved in "visually guided navigation" (i.e. moving about the immediately visible environment, avoiding boundaries, and obstacles)-represents visual information about walking, not crawling, suggesting that OPA is late developing, emerging only when children are walking, not beforehand. But when precisely does this "walking selectivity" in OPA emerge-when children first begin to walk in early childhood, or perhaps counterintuitively, much later in childhood, around 8 years of age, when children are adult-like walking? To directly test these two hypotheses, using functional magnetic resonance imaging (fMRI) in two groups of children, 5- and 8-year-olds, we measured the responses in OPA to first-person perspective videos through scenes from a "walking" perspective, as well as three control perspectives ("crawling," "flying," and "scrambled"). We found that the OPA in 8-year-olds-like adults-exhibited walking selectivity (i.e. responding significantly more to the walking videos than to any of the others, and no significant differences across the crawling, flying, and scrambled videos), while the OPA in 5-year-olds exhibited no walking selectively. These findings reveal that OPA undergoes protracted development, with walking selectivity only emerging around 8 years of age.

Immersive scene representation in human visual cortex with ultra-wide angle neuroimaging

While humans experience the visual environment in a panoramic 220° view, traditional functional MRI setups are limited to display images like postcards in the central 10-15° of the visual field. Thus, it remains unknown how a scene is represented in the brain when perceived across the full visual field. Here, we developed a novel method for ultra-wide angle visual presentation and probed for signatures of immersive scene representation. To accomplish this, we bounced the projected image off angled-mirrors directly onto a custom-built curved screen, creating an unobstructed view of 175°. Scene images were created from custom-built virtual environments with a compatible wide field-of-view to avoid perceptual distortion. We found that immersive scene representation drives medial cortex with far-peripheral preferences, but surprisingly had little effect on classic scene regions. That is, scene regions showed relatively minimal modulation over dramatic changes of visual size. Further, we found that scene and face-selective regions maintain their content preferences even under conditions of central scotoma, when only the extreme far-peripheral visual field is stimulated. These results highlight that not all far-peripheral information is automatically integrated into the computations of scene regions, and that there are routes to high-level visual areas that do not require direct stimulation of the central visual field. Broadly, this work provides new clarifying evidence on content vs. peripheral preferences in scene representation, and opens new neuroimaging research avenues to understand immersive visual representation.

Allocation of Space-Based Attention is Guided by Efficient Comprehension of Spatial Direction

Spatial navigation is supported by visual cues (e.g., scenes, schemas like arrows, and words) that must be comprehended quickly to facilitate effective transit. People comprehend spatial directions faster from schemas and words than scenes. We hypothesize that this occurs because schemas and words efficiently engage space-based attention, allowing for less costly computations. Here, participants completed a spatial cueing paradigm, and we calculated cue validity effects - how much faster participants responded to validly than invalidly cued locations - for each cue format. We pre-registered Experiment 1 and found significant cue validity effects with schemas and words, but not scenes, suggesting space-based attention was allocated more efficiently with schemas and words than scenes. In Experiment 2, we explicitly instructed participants to interpret the scenes from an egocentric perspective and found that this instruction manipulation still did not result in a significant cue validity effect with scenes. In Experiment 3, we investigated whether the differential effects between conditions were due to costly computations to extract spatial direction and found that increasing cue duration had no influence. In Experiment 4, significant cue validity effects were observed for orthogonal but not non-orthogonal spatial directions, suggesting space-based attention was allocated more efficiently when the spatial direction precisely matched the target location. These findings confirm our hypothesis that efficient allocation of space-based attention is guided by faster spatial direction comprehension. Altogether, this work suggests that schemas and words may be more effective supports than scenes for navigation performance in the real-world.

Systematic transition from boundary extension to contraction along an object-to-scene continuum

After viewing a picture of an environment, our memory of it typically extends beyond what was presented, a phenomenon referred to as boundary extension. But, sometimes memory errors show the opposite pattern-boundary contraction-and the relationship between these phenomena is controversial. We constructed virtual three-dimensional environments and created a series of views at different distances, from object close-ups to wide-angle indoor views, and tested for memory errors along this object-to-scene continuum. Boundary extension was evident for close-scale views and transitioned parametrically to boundary contraction for far-scale views. However, this transition point was not tied to a specific position in the environment (e.g., the point of reachability). Instead, it tracked with judgments of the best-looking view of the environment, in both rich-object and low-object environments. We offer a dynamic-tension account, where competition between object-based and scene-based affordances determines whether a view will extend or contract in memory. This study demonstrates that boundary extension and boundary contraction are not two separate phenomena but rather two parts of a continuum, suggesting a common underlying mechanism. The transition point between the two is not fixed but depends on the observer's judgment of the best-looking view of the environment. These findings provide new insights into how we perceive and remember a view of environment.

On natural attunement: Shared rhythms between the brain and the environment

Rhythms exist both in the embodied brain and the built environment. Becoming attuned to the rhythms of the environment, such as repetitive columns, can greatly affect perception. Here, we explore how the built environment affects human cognition and behavior through the concept of natural attunement, often resulting from the coordination of a person's sensory and motor systems with the rhythmic elements of the environment. We argue that the built environment should not be reduced to mere states, representations, and single variables but instead be considered a bundle of highly related continuous signals with which we can resonate. Resonance and entrainment are dynamic processes observed when intrinsic frequencies of the oscillatory brain are influenced by the oscillations of an external signal. This allows visual rhythmic stimulations of the environment to affect the brain and body through neural entrainment, cross-frequency coupling, and phase resetting. We review how real-world architectural settings can affect neural dynamics, cognitive processes, and behavior in people, suggesting the crucial role of everyday rhythms in the brain-body-environment relationship.

Scene context is predictive of unconstrained object similarity judgments

What makes objects alike in the human mind? Computational approaches for characterizing object similarity have largely focused on the visual forms of objects or their linguistic associations. However, intuitive notions of object similarity may depend heavily on contextual reasoning-that is, objects may be grouped together in the mind if they occur in the context of similar scenes or events. Using large-scale analyses of natural scene statistics and human behavior, we found that a computational model of the associations between objects and their scene contexts is strongly predictive of how humans spontaneously group objects by similarity. Specifically, we learned contextual prototypes for a diverse set of object categories by taking the average response of a convolutional neural network (CNN) to the scene contexts in which the objects typically occurred. In behavioral experiments, we found that contextual prototypes were strongly predictive of human similarity judgments for a large set of objects and rivaled the performance of models based on CNN representations of the objects themselves or word embeddings for their names. Together, our findings reveal the remarkable degree to which the natural statistics of context predict commonsense notions of object similarity.

Scene Perception and Visuospatial Memory Converge at the Anterior Edge of Visually Responsive Cortex

To fluidly engage with the world, our brains must simultaneously represent both the scene in front of us and our memory of the immediate surrounding environment (i.e., local visuospatial context). How does the brain's functional architecture enable sensory and mnemonic representations to closely interface while also avoiding sensory-mnemonic interference? Here, we asked this question using first-person, head-mounted virtual reality and fMRI. Using virtual reality, human participants of both sexes learned a set of immersive, real-world visuospatial environments in which we systematically manipulated the extent of visuospatial context associated with a scene image in memory across three learning conditions, spanning from a single FOV to a city street. We used individualized, within-subject fMRI to determine which brain areas support memory of the visuospatial context associated with a scene during recall (Experiment 1) and recognition (Experiment 2). Across the whole brain, activity in three patches of cortex was modulated by the amount of known visuospatial context, each located immediately anterior to one of the three scene perception areas of high-level visual cortex. Individual subject analyses revealed that these anterior patches corresponded to three functionally defined place memory areas, which selectively respond when visually recalling personally familiar places. In addition to showing activity levels that were modulated by the amount of visuospatial context, multivariate analyses showed that these anterior areas represented the identity of the specific environment being recalled. Together, these results suggest a convergence zone for scene perception and memory of the local visuospatial context at the anterior edge of high-level visual cortex.SIGNIFICANCE STATEMENT As we move through the world, the visual scene around us is integrated with our memory of the wider visuospatial context. Here, we sought to understand how the functional architecture of the brain enables coexisting representations of the current visual scene and memory of the surrounding environment. Using a combination of immersive virtual reality and fMRI, we show that memory of visuospatial context outside the current FOV is represented in a distinct set of brain areas immediately anterior and adjacent to the perceptually oriented scene-selective areas of high-level visual cortex. This functional architecture would allow efficient interaction between immediately adjacent mnemonic and perceptual areas while also minimizing interference between mnemonic and perceptual representations.

Combined representation of visual features in the scene-selective cortex

Visual features of separable dimensions like color and shape conjoin to represent an integrated entity. We investigated how visual features bind to form a complex visual scene. Specifically, we focused on features important for visually guided navigation: direction and distance. Previously, separate works have shown that directions and distances of navigable paths are coded in the occipital place area (OPA). Using functional magnetic resonance imaging (fMRI), we tested how separate features are concurrently represented in the OPA. Participants saw eight different types of scenes, in which four of them had one path and the other four had two paths. In single-path scenes, path direction was either to the left or to the right. In double-path scenes, both directions were present. Each path contained a glass wall located either near or far, changing the navigational distance. To test how the OPA represents paths in terms of direction and distance features, we took three approaches. First, the independent-features approach examined whether the OPA codes directions and distances independently in single-path scenes. Second, the integrated-features approach explored how directions and distances are integrated into path units, as compared to pooled features, using double-path scenes. Finally, the integrated-paths approach asked how separate paths are combined into a scene. Using multi-voxel pattern similarity analysis, we found that the OPA's representations of single-path scenes were similar to other single-path scenes of either the same direction or the same distance. Representations of double-path scenes were similar to the combination of two constituent single-paths, as a combined unit of direction and distance rather than pooled representation of all features. These results show that the OPA combines the two features to form path units, which are then used to build multiple-path scenes. Altogether, these results suggest that visually guided navigation may be supported by the OPA that automatically and efficiently combines multiple features relevant for navigation and represent a navigation file.

The occipital place area represents visual information about walking, not crawling

Recent work has shown that the occipital place area (OPA)-a scene-selective region in adult humans-supports "visually guided navigation" (i.e. moving about the local visual environment and avoiding boundaries/obstacles). But what is the precise role of OPA in visually guided navigation? Considering humans move about their local environments beginning with crawling followed by walking, 1 possibility is that OPA is involved in both modes of locomotion. Another possibility is that OPA is specialized for walking only, since walking and crawling are different kinds of locomotion. To test these possibilities, we measured the responses in OPA to first-person perspective videos from both "walking" and "crawling" perspectives as well as for 2 conditions by which humans do not navigate ("flying" and "scrambled"). We found that OPA responded more to walking videos than to any of the others, including crawling, and did not respond more to crawling videos than to flying or scrambled ones. These results (i) reveal that OPA represents visual information only from a walking (not crawling) perspective, (ii) suggest crawling is processed by a different neural system, and (iii) raise questions for how OPA develops; namely, OPA may have never supported crawling, which is consistent with the hypothesis that OPA undergoes protracted development.

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is still unclear to which extent artificial neural networks approximate human object vision at the behavioral and neural levels. Here, we investigated whether machine object vision mimics the representational hierarchy of human object vision with an experimental design that allows testing within-domain representations for animals and scenes, as well as across-domain representations reflecting their real-world contextual regularities such as animal-scene pairs that often co-occur in the visual environment. We found that DCNNs trained in object recognition acquire representations, in their late processing stage, that closely capture human conceptual judgements about the co-occurrence of animals and their typical scenes. Likewise, the DCNNs representational hierarchy shows surprising similarities with the representational transformations emerging in domain-specific ventrotemporal areas up to domain-general frontoparietal areas. Despite these remarkable similarities, the underlying information processing differs. The ability of neural networks to learn a human-like high-level conceptual representation of object-scene co-occurrence depends upon the amount of object-scene co-occurrence present in the image set thus highlighting the fundamental role of training history. Further, although mid/high-level DCNN layers represent the category division for animals and scenes as observed in VTC, its information content shows reduced domain-specific representational richness. To conclude, by testing within- and between-domain selectivity while manipulating contextual regularities we reveal unknown similarities and differences in the information processing strategies employed by human and artificial visual systems.

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.

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the "Go To Goal" instruction, those who receive the "Take Shortcut" instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the "Go To Goal" instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

Ramp-shaped neural tuning supports graded population-level representation of the object-to-scene continuum

We can easily perceive the spatial scale depicted in a picture, regardless of whether it is a small space (e.g., a close-up view of a chair) or a much larger space (e.g., an entire class room). How does the human visual system encode this continuous dimension? Here, we investigated the underlying neural coding of depicted spatial scale, by examining the voxel tuning and topographic organization of brain responses. We created naturalistic yet carefully-controlled stimuli by constructing virtual indoor environments, and rendered a series of snapshots to smoothly sample between a close-up view of the central object and far-scale view of the full environment (object-to-scene continuum). Human brain responses were measured to each position using functional magnetic resonance imaging. We did not find evidence for a smooth topographic mapping for the object-to-scene continuum on the cortex. Instead, we observed large swaths of cortex with opposing ramp-shaped profiles, with highest responses to one end of the object-to-scene continuum or the other, and a small region showing a weak tuning to intermediate scale views. However, when we considered the population code of the entire ventral occipito-temporal cortex, we found smooth and linear representation of the object-to-scene continuum. Our results together suggest that depicted spatial scale information is encoded parametrically in large-scale population codes across the entire ventral occipito-temporal cortex.

Visual continuous recognition reveals behavioral and neural differences for short- and long-term scene memory

Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory at timescales relevant to working and longer-term memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term (< 20 s) or longer-term intervals of between 30 s and 3 min or 4 and 10 min. Overall recognition was far above chance, with better performance at shorter- than longer-term intervals. Sensor-level ANOVA and post hoc pairwise comparisons of event related potentials (ERPs) revealed three main findings: (1) occipital and parietal amplitudes distinguishing new and old from scrambled scenes; (2) frontal amplitudes distinguishing old from new scenes with a central positivity highest for hits compared to misses, false alarms and correct rejections; and (3) frontal and parietal changes from ∼300 to ∼600 ms distinguishing among old scenes previously encountered at short- and longer-term retention intervals. These findings reveal how distributed spatiotemporal neural changes evolve to support short- and longer-term recognition of complex scenes.

Inference of affordances and active motor control in simulated agents

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations.

Navigable Space and Traversable Edges Differentially Influence Reorientation in Sighted and Blind Mice

Reorientation enables navigators to regain their bearings after becoming lost. Disoriented individuals primarily reorient themselves using the geometry of a layout, even when other informative cues, such as landmarks, are present. Yet the specific strategies that animals use to determine geometry are unclear. Moreover, because vision allows subjects to rapidly form precise representations of objects and background, it is unknown whether it has a deterministic role in the use of geometry. In this study, we tested sighted and congenitally blind mice (Ns = 8-11) in various settings in which global shape parameters were manipulated. Results indicated that the navigational affordances of the context-the traversable space-promote sampling of boundaries, which determines the effective use of geometric strategies in both sighted and blind mice. However, blind animals can also effectively reorient themselves using 3D edges by extensively patrolling the borders, even when the traversable space is not limited by these boundaries.

Individual differences in mental imagery modulate effective connectivity of scene-selective regions during resting state

Successful navigation relies on the ability to identify, perceive, and correctly process the spatial structure of a scene. It is well known that visual mental imagery plays a crucial role in navigation. Indeed, cortical regions encoding navigationally relevant information are also active during mental imagery of navigational scenes. However, it remains unknown whether their intrinsic activity and connectivity reflect the individuals' ability to imagine a scene. Here, we primarily investigated the intrinsic causal interactions among scene-selective brain regions such as Parahipoccampal Place Area (PPA), Retrosplenial Complex, and Occipital Place Area (OPA) using Dynamic Causal Modelling for resting-state functional magnetic resonance data. Second, we tested whether resting-state effective connectivity parameters among scene-selective regions could reflect individual differences in mental imagery in our sample, as assessed by the self-reported Vividness of Visual Imagery Questionnaire. We found an inhibitory influence of occipito-medial on temporal regions, and an excitatory influence of more anterior on more medial and posterior brain regions. Moreover, we found that a key role in imagery is played by the connection strength from OPA to PPA, especially in the left hemisphere, since the influence of the signal between these scene-selective regions positively correlated with good mental imagery ability. Our investigation contributes to the understanding of the complexity of the causal interaction among brain regions involved in navigation and provides new insight in understanding how an essential ability, such as mental imagery, can be explained by the intrinsic fluctuation of brain signal.

Task-related connectivity of decision points during spatial navigation in a schematic map

Successful navigation is largely dependent on the ability to make correct decisions at navigational decision points. However, the interaction between the brain regions associated with the navigational decision point in a schematic map is unclear. In this study, we adopted a 2D subway paradigm to study the neural basis underlying decision points. Twenty-eight subjects performed a spatial navigation task using a subway map during fMRI scanning. We adopted a voxel-wise general linear model (GLM) approach and found four brain regions, the left hippocampus (HIP), left parahippocampal gyrus (PHG), left ventromedial prefrontal cortex (vmPFC), and right retrosplenial cortex (RSC), activated at a navigational decision point in a schematic map. Using a psychophysiological interactions (PPI) method, we found that (1) both the left vmPFC and right HIP interacted cooperatively with the right RSC, and (2) the left HIP and the left vmPFC interacted cooperatively at the decision point. These findings may be helpful for revealing the neural mechanisms underlying decision points in a schematic map during spatial navigation.

Three cortical scene systems and their development

Since the discovery of three scene-selective regions in the human brain, a central assumption has been that all three regions directly support navigation. We propose instead that cortical scene processing regions support three distinct computational goals (and one not for navigation at all): (i) The parahippocampal place area supports scene categorization, which involves recognizing the kind of place we are in; (ii) the occipital place area supports visually guided navigation, which involves finding our way through the immediately visible environment, avoiding boundaries and obstacles; and (iii) the retrosplenial complex supports map-based navigation, which involves finding our way from a specific place to some distant, out-of-sight place. We further hypothesize that these systems develop along different timelines, with both navigation systems developing slower than the scene categorization system.

Early Electrophysiological Markers of Navigational Affordances in Scenes

Scene perception and spatial navigation are interdependent cognitive functions, and there is increasing evidence that cortical areas that process perceptual scene properties also carry information about the potential for navigation in the environment (navigational affordances). However, the temporal stages by which visual information is transformed into navigationally relevant information are not yet known. We hypothesized that navigational affordances are encoded during perceptual processing and therefore should modulate early visually evoked ERPs, especially the scene-selective P2 component. To test this idea, we recorded ERPs from participants while they passively viewed computer-generated room scenes matched in visual complexity. By simply changing the number of doors (no doors, 1 door, 2 doors, 3 doors), we were able to systematically vary the number of pathways that afford movement in the local environment, while keeping the overall size and shape of the environment constant. We found that rooms with no doors evoked a higher P2 response than rooms with three doors, consistent with prior research reporting higher P2 amplitude to closed relative to open scenes. Moreover, we found P2 amplitude scaled linearly with the number of doors in the scenes. Navigability effects on the ERP waveform were also observed in a multivariate analysis, which showed significant decoding of the number of doors and their location at earlier time windows. Together, our results suggest that navigational affordances are represented in the early stages of scene perception. This complements research showing that the occipital place area automatically encodes the structure of navigable space and strengthens the link between scene perception and navigation.

Visuospatial coding as ubiquitous scaffolding for human cognition

For more than 100 years we have known that the visual field is mapped onto the surface of visual cortex, imposing an inherently spatial reference frame on visual information processing. Recent studies highlight visuospatial coding not only throughout visual cortex, but also brain areas not typically considered visual. Such widespread access to visuospatial coding raises important questions about its role in wider cognitive functioning. Here, we synthesise these recent developments and propose that visuospatial coding scaffolds human cognition by providing a reference frame through which neural computations interface with environmental statistics and task demands via perception-action loops.

The human brain uses spatial schemas to represent segmented environments

Humans and animals use cognitive maps to represent the spatial structure of the environment. Although these maps are typically conceptualized as extending in an equipotential manner across known space, psychological evidence suggests that people mentally segment complex environments into subspaces. To understand the neurocognitive mechanisms behind this operation, we familiarized participants with a virtual courtyard that was divided into two halves by a river; we then used behavioral testing and fMRI to understand how spatial locations were encoded within this environment. Participants' spatial judgments and multivoxel activation patterns were affected by the division of the courtyard, indicating that the presence of a boundary can induce mental segmentation even when all parts of the environment are co-visible. In the hippocampus and occipital place area (OPA), the segmented organization of the environment manifested in schematic spatial codes that represented geometrically equivalent locations in the two subspaces as similar. In the retrosplenial complex (RSC), responses were more consistent with an integrated spatial map. These results demonstrate that people use both local spatial schemas and integrated spatial maps to represent segmented environment. We hypothesize that schematization may serve as a general mechanism for organizing complex knowledge structures in terms of their component elements.

Parts and Wholes in Scene Processing

During natural vision, our brains are constantly exposed to complex, but regularly structured environments. Real-world scenes are defined by typical part-whole relationships, where the meaning of the whole scene emerges from configurations of localized information present in individual parts of the scene. Such typical part-whole relationships suggest that information from individual scene parts is not processed independently, but that there are mutual influences between the parts and the whole during scene analysis. Here, we review recent research that used a straightforward, but effective approach to study such mutual influences: By dissecting scenes into multiple arbitrary pieces, these studies provide new insights into how the processing of whole scenes is shaped by their constituent parts and, conversely, how the processing of individual parts is determined by their role within the whole scene. We highlight three facets of this research: First, we discuss studies demonstrating that the spatial configuration of multiple scene parts has a profound impact on the neural processing of the whole scene. Second, we review work showing that cortical responses to individual scene parts are shaped by the context in which these parts typically appear within the environment. Third, we discuss studies demonstrating that missing scene parts are interpolated from the surrounding scene context. Bridging these findings, we argue that efficient scene processing relies on an active use of the scene's part-whole structure, where the visual brain matches scene inputs with internal models of what the world should look like.

"Scene" from inside: The representation of Observer's space in high-level visual cortex

Human observers are remarkably adept at perceiving and interacting with visual stimuli around them. Compared to visual stimuli like objects or faces, scenes are unique in that they provide enclosures for observers. An observer looks at a scene by being physically inside the scene. The current research explored this unique observer-scene relationship by studying the neural representation of scenes' spatial boundaries. Previous studies hypothesized that scenes' boundaries were processed in sets of high-level visual cortices. Notably, the parahippocampal place area (PPA), exhibited neural sensitivity to scenes that had closed vs. open spatial boundaries (Kravitz et al., 2011; Park et al., 2011). We asked whether this sensitivity reflected the openness of landscape (e.g., forest vs. beach), or the openness of the environment immediately surrounding the observer (i.e., whether a scene was viewed from inside vs. outside a room). Across two human fMRI experiments, we found that the PPA, as well as another well-known navigation-processing area, the occipital place area (OPA), processed scenes' boundaries according to the observer's space rather than the landscape. Moreover, we found that the PPA's activation pattern was susceptible to manipulations involving mid-level perceptual properties of scenes (e.g., rectilinear pattern of window frames), while the OPA's response was not. Our results have important implications for research in visual scene processing and suggest an important role of an observer's location in representing the spatial boundary, beyond the low-level visual input of a landscape.

Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.

The visual information that we sample from our environment undergoes a series of neural modifications, with each modification state (or visual code) consisting of a unique distribution of responses across neurons along the visual pathway. However, current noninvasive neuroimaging techniques provide an account of that code that is coarse with respect to time or space. Here, we present dynamic electrode-to-image (DETI) mapping, an analysis technique that capitalizes on the high temporal resolution of EEG to map neural signals to each pixel within a given image to reveal location-specific modifications of the visual code. The DETI technique reveals maps of features that are associated with the neural signal at each pixel and at each time point. DETI mapping shows that real-world scenes undergo a series of nonuniform modifications over both space and time. Specifically, we find that the visual code varies in a location-specific manner, likely reflecting that neural processing prioritizes different features at different image locations over time. DETI mapping therefore offers a potential avenue for future studies to explore how each modification state informs and refines the conceptual meaning of our visual world.

Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks

Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step, we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is, scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.

Development of navigation network revealed by resting-state and task-state functional connectivity

Humans possess the essential capacity to navigate in environment, supported by multiple brain regions constituting the navigation network. Recent studies on development of the navigation network mainly examined activation changes in the medial temporal regions. It is unclear how the large-scale organization of the whole navigation network develops and whether the network organizations under resting-state and task-state develop differently. We addressed these questions by examining functional connectivity (FC) of the navigation network in 122 children (10-13 years) and 260 adults. First, we identified a modular structure in the navigation network during resting-state that included a ventral and a dorsal module. Then, we found that the intrinsic modular structure was strengthened from children to adults, that is, adults showed stronger FC within the ventral module and weaker FC between ventral and dorsal modules than children. Further, the intrinsic modular structure was loosened when performing scene-viewing task, that is, both adults and children showed decreased within-ventral FC and increased between-module FC during task- than resting-state. Finally, the task-modulated FC changes were greater in adults than in children. In sum, our study reveals age-related changes in the navigation network organization as increasing modularity under resting-state and increasing flexibility under task-state.

Unveiling functions of the visual cortex using task-specific deep neural networks

The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.

Neural Correlates of Subsequent Memory-Related Gaze Reinstatement

Mounting evidence linking gaze reinstatement-the recapitulation of encoding-related gaze patterns during retrieval-to behavioral measures of memory suggests that eye movements play an important role in mnemonic processing. Yet, the nature of the gaze scanpath, including its informational content and neural correlates, has remained in question. In this study, we examined eye movement and neural data from a recognition memory task to further elucidate the behavioral and neural bases of functional gaze reinstatement. Consistent with previous work, gaze reinstatement during retrieval of freely viewed scene images was greater than chance and predictive of recognition memory performance. Gaze reinstatement was also associated with viewing of informationally salient image regions at encoding, suggesting that scanpaths may encode and contain high-level scene content. At the brain level, gaze reinstatement was predicted by encoding-related activity in the occipital pole and BG, neural regions associated with visual processing and oculomotor control. Finally, cross-voxel brain pattern similarity analysis revealed overlapping subsequent memory and subsequent gaze reinstatement modulation effects in the parahippocampal place area and hippocampus, in addition to the occipital pole and BG. Together, these findings suggest that encoding-related activity in brain regions associated with scene processing, oculomotor control, and memory supports the formation, and subsequent recapitulation, of functional scanpaths. More broadly, these findings lend support to scanpath theory's assertion that eye movements both encode, and are themselves embedded in, mnemonic representations.

Shared cognitive mechanisms involved in the processing of scene texture and scene shape

Recent research has demonstrated that the parahippocampal place area represents both the shape and texture features of scenes, with the importance of each feature varying according to perceived scene category. Namely, shape features are predominately more diagnostic to the processing of artificial human–made scenes, while shape and texture are equally diagnostic in natural scene processing. However, to date little is known regarding the degree of interactivity or independence observed in the processing of these scene features. Furthermore, manipulating the scope of visual attention (i.e., globally vs. locally) when processing ensembles of multiple objects—stimuli that share a functional neuroanatomical link with scenes—has been shown to affect their cognitive visual representation. It remains unknown whether manipulating the scope of attention impacts scene processing in a similar manner. Using the well-established Garner speeded-classification behavioral paradigm, we investigated the influence of both feature diagnosticity and the scope of visual attention on potential interactivity or independence in the shape and texture processing of artificial human–made scenes. The results revealed asymmetric interference between scene shape and texture processing, with the more diagnostic feature (i.e., shape) interfering with the less diagnostic feature (i.e., texture), but not vice versa. Furthermore, this interference was attenuated and enhanced with more local and global visual processing strategies, respectively. These findings suggest that the scene shape and texture processing are mediated by shared cognitive mechanisms and that, although these representations are governed primarily via feature diagnosticity, they can nevertheless be influenced by the scope of visual attention.

Human spatial navigation: Neural representations of spatial scales and reference frames obtained from an ALE meta-analysis

Humans use different spatial reference frames (allocentric or egocentric) to navigate successfully toward their destination in different spatial scale spaces (environmental or vista). However, it remains unclear how the brain represents different spatial scales and different spatial reference frames. Thus, we conducted an activation likelihood estimation (ALE) meta-analysis of 47 fMRI articles involving human spatial navigation. We found that both the environmental and vista spaces activated the parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area in the right hemisphere. The environmental space showed stronger activation than the vista space in the occipital and frontal regions. No brain region exhibited stronger activation for the vista than the environmental space. The allocentric and egocentric reference frames activated the bilateral PPA and right RSC. The allocentric frame showed more stronger activations than the egocentric frame in the right culmen, left middle frontal gyrus, and precuneus. No brain region displayed stronger activation for the egocentric than the allocentric navigation. Our findings suggest that navigation in different spatial scale spaces can evoke specific and common brain regions, and that the brain regions representing spatial reference frames are not absolutely separated.

Commonalities and specificities between environmental navigation and autobiographical memory: A synthesis and a theoretical perspective

It has been proposed that declarative memory evolved from spatial navigation, with episodic memory having its roots in mechanisms of egocentric navigation and semantic memory in those of allocentric navigation; however, whether these brain networks actually overlap is still unclear. Using Activation Likelihood Estimation, we assessed the correspondence between brain correlates of spatial navigation (SN) and autobiographical memory (AM), further testing whether neural substrates of episodic memory (EAM) and egocentric navigation, and those of semantic memory (SAM) and map-like navigation, coincide. SN and AM commonly activated the parahippocampal gyrus and middle hippocampus, posterior cingulate cortex and right angular gyrus, but also involved distinct brain regions. Similarly, EAM and egocentric navigation, besides sharing a network involving the right angular gyrus, bilateral posterior cingulate and parahippocampal gyrus, activated distinct brain regions; no region was commonly activated by SAM and allocentric navigation. We discuss findings in the light of theories on the relation between navigation and memory, and propose a new theoretical perspective, which takes into account the dynamic nature of navigational processes.

A network linking scene perception and spatial memory systems in posterior cerebral cortex

The neural systems supporting scene-perception and spatial-memory systems of the human brain are well-described. But how do these neural systems interact? Here, using fine-grained individual-subject fMRI, we report three cortical areas of the human brain, each lying immediately anterior to a region of the scene perception network in posterior cerebral cortex, that selectively activate when recalling familiar real-world locations. Despite their close proximity to the scene-perception areas, network analyses show that these regions constitute a distinct functional network that interfaces with spatial memory systems during naturalistic scene understanding. These "place-memory areas" offer a new framework for understanding how the brain implements memory-guided visual behaviors, including navigation.