Kai-Florian Richter

Not all anxious individuals get lost: trait anxiety and mental rotation ability interact to explain performance in map-based route learning in men

Navigation through an environment is a fundamental human activity. Although group differences in navigational ability are documented (e.g., gender), little is known about traits that predict these abilities. Apart from a well-established link between mental rotational abilities and navigational learning abilities, recent studies point to an influence of trait anxiety on the formation of internal cognitive spatial representations. However, it is unknown whether trait anxiety affects the processing of information obtained through externalized representations such as maps. Here, we addressed this question by taking into account emerging evidence indicating impaired performance in executive tasks by high trait anxiety specifically in individuals with lower executive capacities. For this purpose, we tested 104 male participants, previously characterised on trait anxiety and mental rotation ability, on a newly-designed map-based route learning task, where participants matched routes presented dynamically on a city map to one presented immediately before (same/different judgments). We predicted an interaction between trait anxiety and mental rotation ability, specifically that performance in the route learning task would be negatively affected by anxiety in participants with low mental rotation ability. Importantly, and as predicted, an interaction between anxiety and mental rotation ability was observed: trait anxiety negatively affected participants with low-but not high-mental rotation ability. Our study reveals a detrimental role of trait anxiety in map-based route learning and specifies a disadvantage in the processing of map representations for high-anxious individuals with low mental rotation abilities.

A Mobile Application for a User-Generated Collection of Landmarks

Landmarks are crucial elements in how people understand and communicate about space. In wayfinding they provide references that are preferred and easier to follow than distances or street names alone. Thus, the inclusion of landmarks into navigation services is a long-held goal, but its implementation has largely failed so far. To a large part this is due to significant difficulties in obtaining a sufficient data set of landmark candidates. In this paper, we introduce a mobile application, which enables a user-generated collection of landmarks. Employing a photo-based interface, the application calculates and ranks potential landmark candidates based on the current visible area and presents them to the user, who then may choose the intended one. We use OpenStreetMap as data source; the app allows tagging OSM objects as potential landmarks. Integrating users into the landmark selection process keeps data requirements low, while a simple interface lowers the burden on the users.

Integration of simulation and optimization for evacuation planning

Abstract Evacuation is a time critical process in which the highest priority is to get those people who may be affected by a disaster out of the danger zone as fast as possible. For disaster-prone areas, authorities often distribute evacuation plans well in advance, or encourage the population to prepare themselves for eventual disasters. This paper presents an approach to such planning ahead for evacuation that tightly couples optimization and traffic simulation in order to determine optimal evacuation time and exit from the area for each evacuee. In this paper, we discuss the approach’s properties and illustrate its performance using two case studies of wildfire-prone areas in the state of Victoria, Australia. The results show that our approach can lead to significant improvements when compared to ad-hoc evacuation, but these improvements also strongly depend on population density and road network topology. More generally, our research highlights the significant benefits of tightly coupling optimization and simulation for evacuation modeling.

Wayfinding Decision Situations: A Conceptual Model and Evaluation

Humans engage in wayfinding many times a day. We try to find our way in urban environments when walking towards our work places or when visiting a city as tourists. In order to reach the targeted destination, we have to make a series of wayfinding decisions of varying complexity. Previous research has focused on classifying the complexity of these wayfinding decisions, primarily looking at the complexity of the decision point itself (e.g., the number of possible routes or branches). In this paper, we proceed one step further by incorporating the user, instructions, and environmental factors into a model that assesses the complexity of a wayfinding decision. We constructed and evaluated three models using data collected from an outdoor wayfinding study. Our results suggest that additional factors approximate the complexity of a wayfinding decision better than the simple model using only the number of branches as a criterion.

Up, Down, Turn Around: Assisted Wayfinding Involving Level Changes

Both maps and verbal descriptions have been shown to be an effective wayfinding assistance. However, most studies investigating these aids have been performed in two-dimensional spaces that ignore level changes. It seems less clear that both types of assistance work equally well in settings that involve going up some stairs or taking an elevator. In this paper, we present a study that had participants follow a route in a multi-level setting involving several level changes while being assisted by either a textual description or a sketch map. Results indicate that both types of assistance are effective and that the few differences in performance that we discovered can be attributed to differences in the employed wayfinding strategies rather than differences in the assistance types. Our findings have implications for the design of (mobile) assistance services that aim at using graphical instructions for guiding users seamlessly through indoor and outdoor environments.

Identifying Landmark Candidates Beyond Toy Examples

Incorporating references to landmarks in navigation systems requires having data on potential landmarks in the first place. While there have been many approaches in the scientific literature for identifying landmark candidates, these have hardly been picked up in actual, running systems. One major obstacle for this to happen may be that most—if not all—approaches presented so far are not scalable due to their underlying data requirements. In this paper, I will critically discuss existing approaches in light of their scalability. I will then suggest a way forward to more scalable solutions by combining in a smart way aspects of different approaches.

The effects of visual realism, spatial abilities, and competition on performance in map-based route learning in men

ABSTRACT We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users.

Defensive Wayfinding: Incongruent Information in Route Following

Extensive research has focused on what constitutes good route directions, identifying qualities such as the logical sequential ordering, the inclusion of landmarks, and ergonomic ways of referring to turns as critical to delivering cognitively adequate instructions. In many cases, however, people are not actually provided with route directions adhering to these qualities. Yet, often people are still able to successfully navigate to the planned destinations, despite poor or even erroneous direction giving. In this paper, we introduce the concept of defensive wayfinding as the particular type of problem solving people undertake when presented with route directions incongruent with their experience of the environment. We present a systematic investigation of the incompatibilities that may occur between route descriptions and the environment. We note that the content of route directions is produced by the direction giver based on observations of the environment. We develop a classification of the impacts of uncertainty in these observations based on the theory of measurement scales of Stevens [33]. We then relate uncertainty to its impact on route following and the ability of the wayfinder to detect problems during wayfinding. We conclude with a discussion of the impacts of common-sense expectations on the need to engage in defensive wayfinding.

Cognitive Aspects: How People Perceive, Memorize, Think and Talk About Landmarks

This chapter deals with the human mind and its representation of geographic space, particularly with the role of landmarks in these representations. The scientific disciplines that are called upon to illuminate this area are neuroscience, cognitive science, and linguistics. This broad range of disciplines is necessary, because the structure of spatial representations in the human brain and the behaviour of these representations in spatial tasks are not directly accessible, and thus, indirect approaches have to be pursued. Direct observations of brain cells are invasive, and thus applied typically on animals only. To what extent observations from animals can be transferred to explain human spatial cognition is a matter for investigation in its own right. However, indirect methods such as functional magnetic resonance imaging shed some light into brain activity. Cognitive scientists, being interested in intelligence and behaviour rather than actual cell structures, live with a similar challenge. They observe the human mind indirectly by devising experiments on human memory, reasoning, and behaviour. Linguists add studies of human spatial communication, which should also allow indirect conclusions about mental representations.

Computational Aspects: How Landmarks Can Be Observed, Stored, and Analysed

In this chapter, we will explore how to ‘compute’ a landmark. We will look at ways to calculate that some geographic object sticks out from its background. We will also discuss approaches for selecting the most appropriate landmark for describing specific spatial situations. Both these aspects are important steps for the integration of landmarks in computational services. Therefore, in a third part of this chapter we will discuss commonalities and differences between both aspects, where and why the presented approaches may fail, and what alternatives there are for overcoming these shortcomings.