Challenges for identifying the neural mechanisms that support spatial navigation: the impact of spatial scale.

Spatial navigation is a fascinating behavior that is essential for our everyday lives. It involves nearly all sensory systems, it requires numerous parallel computations, and it engages multiple memory systems. One of the key problems in this field pertains to the question of reference frames: spatial information such as direction or distance can be coded egocentrically-relative to an observer-or allocentrically-in a reference frame independent of the observer. While many studies have associated striatal and parietal circuits with egocentric coding and entorhinal/hippocampal circuits with allocentric coding, this strict dissociation is not in line with a growing body of experimental data. In this review, we discuss some of the problems that can arise when studying the neural mechanisms that are presumed to support different spatial reference frames. We argue that the scale of space in which a navigation task takes place plays a crucial role in determining the processes that are being recruited. This has important implications, particularly for the inferences that can be made from animal studies in small scale space about the neural mechanisms supporting human spatial navigation in large (environmental) spaces. Furthermore, we argue that many of the commonly used tasks to study spatial navigation and the underlying neuronal mechanisms involve different types of reference frames, which can complicate the interpretation of neurophysiological data

The Floor Strategy: Wayfinding Cognition in a Multi-Level Building

This short paper is concerned with strategies and cognitive processes of wayfinding in public buildings. We conducted an empirical study in a complex multi-level building, comparing performance measures of experienced and inexperienced participants in different wayfinding tasks. Thinking aloud protocols provided insights into navigation strategies, planning phases, use of landmarks and signage. Three specific strategies for navigation in multi-level buildings were compared. The cognitively efficient floor strategy was preferred by experts over a central-point strategy or a direction strategy, and overall was associated to better wayfinding performance

From Isovists via Mental Representations to Behaviour: First Steps Toward Closing the Causal Chain

This study addresses the interrelations between human wayfinding performance, the mental representation of routes, and the geometrical layout of path intersections. The virtual reality based empirical experiment consisted of a route learning and reproduction task and two choice reaction tasks measuring the acquired knowledge of route decision points. In order to relate the recorded behavioural data to the geometry of the environment, a specific adaptation of isovist-based spatial analysis was developed that accounts for directional bias in human spatial perception and representation. Taken together, the applied analyses provided conclusive evidence for correspondences between geometrical properties of environments as captured by isovists and their mental representation

Acquisition and consolidation of hierarchical representations of space

Navigation – the ability to reach targets which are no visible from the current position - depends on the correct recall of the desired target and the environment between one's current position and this target. The content of these representations are subject to influences from different modalities, e.g. vision, and language. A place can be recognized through different cues, e.g. due to a salient object, but also because of the angles of the routes at an intersection, or a name. The location of places as well as the routes connecting them can be integrated and memorized in an allocentric, survey-like representation. Depending on the amount of detail, the granularity level of a representation can be coarser or finer; the different levels are organized hierarchically. Characteristics of a superordinate category, like a region, can affect the perception of its constituting elements, the places; an inheritance of qualities from region to place levels is possible. The formation of superordinate categories depends both on environmental factors as well as individual ones: what is recognized, what is remembered, and which predictions are drawn from this representation? In this dissertation I examine the acquisition of representations of space, in order to identify features that are well suited for being remembered and auxiliary for navigation. I have two research foci: First, I examine the impact of language by using different hierarchically structured naming schemes as place names. Wiener & Mallot (2003) found that characterizing places only with landmarks belonging to different semantic categories influenced route choice as well as the representations of space. I compare these findings to the impact of different naming schemes. I show that there are naming schemes that may influence behavior in a similar way as a landmark does, but that seeing something and reading its name is by far the same thing. The second part focuses on the content of representations established during navigation. With three different navigation experiments, I examine the content of the concepts of space that are acquired during navigation. What is remembered - the location of places, the routes, or the hierarchical structure of the experimental environment? Are there features that are more likely to be consolidated during sleep, e.g., the transfer of concrete knowledge about places and routes into an abstract, survey-like representation? I show that there are improvements in one wayfinding task correlated to sleep. In the other experiments, learning effects were found for both groups. I also address the question of suitable parameters for measuring survey knowledge

Entropy and a Sub-Group of Geometric Measures of Paths Predict the Navigability of an Environment

Despite extensive research on navigation, it remains unclear which features of an environment predict how difficult it will be to navigate. We analysed 478,170 trajectories from 10,626 participants who navigated 45 virtual environments in the research app-based game Sea Hero Quest. Virtual environments were designed to vary in a range of properties such as their layout, number of goals, visibility (varying fog) and map condition. We calculated 58 spatial measures grouped into four families: task-specific metrics, space syntax configurational metrics, space syntax geometric metrics, and general geometric metrics. We used Lasso, a variable selection method, to select the most predictive measures of navigation difficulty. Geometric features such as entropy, area of navigable space, number of rings and closeness centrality of path networks were among the most significant factors determining the navigational difficulty. By contrast a range of other measures did not predict difficulty, including measures of intelligibility. Unsurprisingly, other task-specific features (e.g. number of destinations) and fog also predicted navigation difficulty. These findings have implications for the study of spatial behaviour in ecological settings, as well as predicting human movements in different settings, such as complex buildings and transport networks and may aid the design of more navigable environments

Predefining regionalised environments for assisted navigation : does incorporating regions into navigation instructions assist a user’s spatial understanding of the environment they aretravelling through?

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThis thesis proposes introducing pre-defined regionalised areas into navigation instructions to allow drivers to learn more about the environment they’re travelling through. Following detailed navigation instructions, drivers are no longer required to learn about and understand their environment, which leaves drivers reliant on these navigation devices. An experiment using a virtual environment was conducted to evaluate if a group with additional regional instructions would complete tasks more effectively than a group with traditional instructions. While the regional group performed better on all accounts, statistically significant results were only found in three of ten variables. There were however, large differences in task completion rates, suggesting that incorporating pre-defined regions to navigation instructions does make a difference in drivers’ understanding of their environment

Theoretical Computational Models for the Cognitive Map

In den letzten Jahrzehnten hat die Forschung nach der Frage, wie Raum im Gehirn repräsentiert wird, ein weit verzweigtes Netzwerk von spezialisierten Zellen aufgedeckt. Es ist nun klar, dass Räumlichkeit auf irgendeine Art repräsentiert sein muss, aber die genaue Umsetzung wird nach wie vor debattiert. Folgerichtig liegt das übergeordnete Ziel meiner Dissertation darin, das Verständnis von der neuronalen Repräsentation, der Kognitiven Karte, mithilfe von theoretischer Computermodellierung (im Gegensatz zu datengetriebener Modellierung) zu erweitern. Die Arbeit setzt sich aus vier Publikationen zusammen, die das Problem aus verschiedenen, aber miteinander kompatiblen Richtungen angehen: In den ersten beiden Publikationen geht es um zielgerichtete Navigation durch topologische Graphen, in denen die erkundete Umgebung als Netzwerk aus loka len Positionen und sie verbindenden Handlungen dargestellt wird. Im Gegensatz zu Koordinaten-basierten metrischen Karten sind Graphenmodelle weniger gebunden und haben verschiedene Vorteile wie z.B. Algorithmen, die garantiert optimale Pfade finden. Im ersten Modell sind Orte durch Populationen von einfachen Bildfeatures im Graphen gespeichert. Für die Navigation werden dann mehrere Pfade gleichzeitig zwischen Start- und Zielpopulationen berechnet und die schlussendliche Route folgt dem Durchschnitt der Pfade. Diese Methode macht die Wegsuche robuster und umgeht das Problem, Orte entlang der Route wiedererkennen zu müssen. In der zweiten Publikation wird ein hierarchisches Graphenmodell vorgeschlagen, bei dem die Umgebung in mehrere Regionen unterteilt ist. Das Regionenwissen ist ebenfalls als übergeordnete Knoten im Graphen gespeichert. Diese Struktur führt bei der Wegsuche dazu, dass die berechneten Routen verzerrt sind, was mit dem Verhalten von menschlichen Probanden in Navigationsstudien übereinstimmt. In der dritten Publikation geht es auch um Regionen, der Fokus liegt aber auf der konkreten biologischen Umsetzung in Form von Place Cell und Grid Cell-Aktivität. Im Gegensatz zu einzigartigen Ortsknoten im Graphen zeigen Place Cells multiple Feuerfelder in verschiedenen Regionen oder Kontexten. Dieses Phänomen wird als Remapping bezeichnet und könnte der Mechanismus hinter Regionenwissen sein. Wir modellieren das Phänomen mithilfe eines Attraktor-Netzwerks aus Place- und Grid Cells: Immer, wenn sich der virtuelle Agent des Modells von einer Region in eine andere bewegt, verändert sich der Kontext und die Zellaktivität springt zu einem anderen Attraktor, was zu einem Remapping führt. Das Modell kann die Zellaktivität von Tieren in mehreren Experimentalumgebungen replizieren und ist daher eine plausible Erklärung für die Vorgänge im biologischen Gehirn. In der vierten Publikation geht es um den Vergleich von Graphen- und Kartenmodellen als fundamentale Struktur der kognitiven Karte. Im Speziellen geht es bei dieser Debatte um die Unterscheidung zwischen nicht-metrischen Graphen und metrischen euklidischen Karten; euklidische Karten sind zwar mächtiger als die Alternative, aber menschliche Probanden neigen dazu, Fehler zu machen, die stark von einer metrischen Vorhersage abweichen. Deshalb wird häufig argumentiert, dass nicht-metrische Modelle das Verhalten besser erklären können. Wir schlagen eine alternative metrische Erklärung für die nichtmetrischen Graphen vor, indem wir die Graphen im metrischen Raum einbetten. Die Methode wird in einer bestimmten nicht-euklidischen Beispielumgebung gezeigt, in der sie Versuchspersonenverhalten genauso gut vorhersagen kann, wie ein nichtmetrischer Graph. Wir argumentieren daher, dass unser Modell ein besseres Modell für Raumrepräsentation sein könnte. Zusätzlich zu den Einzelergebnissen diskutiere ich außerdem die Gemeinsamkeiten der Modelle und wie sie in den derzeitigen Stand der Forschung zur kognitiven Karte passen. Darüber hinaus erörtere ich, wie die Ergebnisse zu komplexeren Modellen vereint werden könnten, um unser Bild der Raumkognition zu erweitern.Decades of research into the neural representation of physical space have uncovered a complex and distributed network of specialized cells in the mammalian brain. It is now clear that space is represented in some form, but the realization remains debated. Accordingly, the overall aim of my thesis is to further the understanding of the neural representation of space, the cognitive map, with the aid of theoretical computational modeling (as opposed to data-driven modeling). It consists of four separate publications which approach the problem from different but complementing perspectives: The first two publications consider goal-directed navigation with topological graph models, which encode the environment as a state-action graph of local positions connected by simple movement instructions. Graph models are often less constrained than coordinate-based metric maps and offer a variety of computational advantages; for example, graph search algorithms may be used to derive optimal routes between arbitrary positions. In the first model, places are encoded by population codes of low-level image features. For goal-directed navigation, a set of simultaneous paths is obtained between the start and goal populations and the final trajectory follows the population average. This makes route following more robust and circumvents problems related to place recognition. The second model proposes a hierarchical place graph which subdivides the known environment into well-defined regions. The region knowledge is included in the graph as superordinate nodes. During wayfinding, these nodes distort the resulting paths in a way that matches region-related biases observed in human navigation experiments. The third publication also considers region coding but focuses on more concrete biological implementation in the form of place cell and grid cell activity. As opposed to unique nodes in a graph, place cells may express multiple firing fields in different contexts or regions. This phenomenon is known as “remapping” and may be fundamental to the encoding region knowledge. The dynamics are modeled in a joint attractor neural network of place and grid cells: Whenever a virtual agent moves into another region, the context changes and the model remaps the cell activity to an associated pattern from memory. The model is able to replicate experimental findings in a series of mazes and may therefore be an explanation for the observed activity in the biological brain. The fourth publication again returns to graph models, joining the debate on the fundamental structure of the cognitive map: The internal representation of space has often been argued to either take the form of a non-metric topological graph or a Euclidean metric map in which places are assigned specific coordinates. While the Euclidean map is more powerful, human navigation in experiments often strongly deviates from a (correct) metric prediction, which has been taken as an argument for the non-metric alternative. However, it may also be possible to find an alternative metric explanation to the non-metric graphs by embedding the latter into metric space. The method is shown with a specific non-Euclidean example environment where it can explain subject behavior equally well to the purely non-metric graph, and it is argued that it is therefore a better model for spatial knowledge. Beyond the individual results, the thesis discusses the commonalities of the models and how they compare to current research on the cognitive map. I also consider how the findings may be combined into more complex models to further the understanding of the cognitive neuroscience of space

Personal Wayfinding Assistance

We are traveling many different routes every day. In familiar environments it is easy for us to find our ways. We know our way from bedroom to kitchen, from home to work, from parking place to office, and back home at the end of the working day. We have learned these routes in the past and are now able to find our destination without having to think about it. As soon as we want to find a place beyond the demarcations of our mental map, we need help. In some cases we ask our friends to explain us the way, in other cases we use a map to find out about the place. Mobile phones are increasingly equipped with wayfinding assistance. These devices are usually at hand because they are handy and small, which enables us to get wayfinding assistance everywhere where we need it. While the small size of mobile phones makes them handy, it is a disadvantage for displaying maps. Geographic information requires space to be visualized in order to be understandable. Typically, not all information displayed in maps is necessary. An example are walking ways in parks for car drivers, they are they are usually no relevant route options. By not displaying irrelevant information, it is possible to compress the map without losing important information. To reduce information purposefully, we need information about the user, the task at hand, and the environment it is embedded in. In this cumulative dissertation, I describe an approach that utilizes the prior knowledge of the user to adapt maps to the to the limited display options of mobile devices with small displays. I focus on central questions that occur during wayfinding and relate them to the knowledge of the user. This enables the generation of personal and context-specific wayfinding assistance in the form of maps which are optimized for small displays. To achieve personalized assistance, I present algorithmic methods to derive spatial user profiles from trajectory data. The individual profiles contain information about the places users regularly visit, as well as the traveled routes between them. By means of these profiles it is possible to generate personalized maps for partially familiar environments. Only the unfamiliar parts of the environment are presented in detail, the familiar parts are highly simplified. This bears great potential to minimize the maps, while at the same time preserving the understandability by including personally meaningful places as references. To ensure the understandability of personalized maps, we have to make sure that the names of the places are adapted to users. In this thesis, we study the naming of places and analyze the potential to automatically select and generate place names. However, personalized maps only work for environments the users are partially familiar with. If users need assistance for unfamiliar environments, they require complete information. In this thesis, I further present approaches to support uses in typical situations which can occur during wayfinding. I present solutions to communicate context information and survey knowledge along the route, as well as methods to support self-localization in case orientation is lost