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Spatial Cognition & Computation
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Taxonomy of Human Wayfinding Tasks: A Knowledge-Based Approach
Jan M. Wiener a; Simon J. Büchner a; Christoph Hölscher a
a Center for Cognitive Science, University of Freiburg, Germany
Online Publication Date: 01 April 2009
To cite this Article Wiener, Jan M., Büchner, Simon J. and Hölscher, Christoph(2009)'Taxonomy of Human Wayfinding Tasks: A
Knowledge-Based Approach',Spatial Cognition & Computation,9:2,152 — 165
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Spatial Cognition & Computation, 9:152–165, 2009
Copyright © Taylor & Francis Group, LLC
ISSN: 13 87-5868 print/1542-7633 online
DOI: 10.1080/1 3875860902906496
Taxonomy of Human Wayfinding Tasks:
A Knowledge-Based Approach
Jan M. Wiener,1Simon J. Büchner,1and Christoph Hölscher1
1Center for Cognitive Science, University of Freiburg, Germany
Abstract: Although the term “Wayfinding” has been defined by several authors, it
subsumes a whole set of tasks that involve different cognitive processes, drawing
on different cognitive components. Research on wayfinding has been conducted with
different paradigms using a variety of wayfinding tasks. This makes it difficult to
compare the results and implications of many studies. A systematic classification is
needed in order to determine and investigate the cognitive processes and structural
components of how humans solve wayfinding problems. Current classifications of
wayfinding distinguish tasks on a rather coarse level or do not take the navigator’s
knowledge, a key factor in wayfinding, into account. We present an extended taxonomy
of wayfinding that distinguishes tasks by external constraints as well as by the level
of spatial knowledge that is available to the navigator. The taxonomy will help to
decrease ambiguity of wayfinding tasks and it will facilitate understanding of the
differentiated demands a navigator faces when solving wayfinding problems.
Keywords: taxonomy, wayfinding, navigation
1. INTRODUCTION
Purposeful navigation between places is perhaps the most prominent real-
world application of spatial cognition. Finding one’s way is a ubiquitous
requirement of daily life and it has received considerable attention in the re-
search literature over the past 50 years. The term “wayfinding” has originally
been introduced by Kevin Lynch in 1960 and Golledge (1999, p. 6) defines
wayfinding as “the process of determining and following a path or route
between an origin and destination.” Humans solve manifold wayfinding tasks
such as search, exploration, route following, or route planning in contexts
including outdoor and urban environments, indoor spaces and virtual reality
Correspondence concerning this article should be addressed to Jan M. Wiener,
Center for Cognitive Science, Freiburg University, Friedrichstr. 50, D-79098, Freiburg,
Germany. E-mail: mail@jan-wiener.net
152
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Wayfinding Taxonomy 153
simulations. The cognitive resources required for these tasks differ consid-
erably, both with respect to the format and content of spatial knowledge in-
volved and with respect to strategies of problem solving, choice of perceptual
cues from the environment and ultimately choice of movement sequences.
The investigation of spatial representation format, cognitive processes and
strategies for solving different tasks poses a key issue in spatial cognition
research.
In order to systematically approach these issues, a taxonomy of wayfind-
ing, describing the different wayfinding tasks in detail, is of particular im-
portance for the following reasons:
First, wayfinding research is characterized by a variety of paradigms and
case studies. Without a taxonomy of wayfinding tasks, these studies are
difficult to compare and to integrate. Two studies by Michael O’Neill
(1991a, 1991b) illustrate this problem, as two rather different tasks are
both simply labeled “wayfinding”: While O’Neill (1991a) had participants
search for an unknown room in a (familiar) university building, O’Neill
(1991b) asked participants to identify the shortest route to specifically
trained target locations.
Second, wayfinding belongs to the most complex cognitive operations. In
order to successfully solve wayfinding tasks, navigators have to monitor
external and internal cues, representations of space have to be formed and
manipulated, etc. In order to uncover the dynamic and complex interplay of
these different cognitive components, one must develop an understanding
of how different wayfinding tasks relate to each other.
This paper aims to provide a taxonomy1of wayfinding tasks and their
demands regarding spatial knowledge. The goal is to extend rather than
replace existing classifications of wayfinding. Although several authors have
already identified different high-level wayfinding tasks, our contribution pro-
vides a more fine-grained (microlevel) differentiation based on the types
of spatial knowledge that are involved. Knowledge about the location of
a specific goal, as well as knowledge about a route or the environment as
a whole crucially determines which wayfinding behaviors and strategies can
be applied. Consequently, we suggest that a taxonomy of wayfinding must
reflect these factors as well.
A number of classifications of navigation behavior have been proposed in
the literature (e.g., Allen, 1999, Kuipers, 1978, 2000; Mallot, 1999; Montello,
2001, 2005) of which we introduce the ones most relevant for this paper.
1Taxonomies provide a hierarchical structure of entities that allow the classifi-
cation of instances of these entities. In the history of taxonomies in the sciences, the
most prominent one may be the Systema Naturae by Carolus Linnaeus from the 18th
century, defining the relationship among species.
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154 J. M. Wiener, S. J. Büchner, and C. Hölscher
Montello (2001, 2005) defines navigation as consisting of two components,
locomotion and wayfinding. Locomotion refers to navigation behavior in
response to current sensory-motor input of the immediate surrounding and
includes tasks such as steering, obstacle avoidance, and the approach of a
visible object in vista space. The term wayfinding subsumes a number of
navigation tasks that share certain common features: they require decision
making and/or planning processes, involve some representation of the
environment and aim at reaching destinations beyond the current sensory
horizon. Typical wayfinding tasks are, for example, search, exploration,
and route planning.
Mallot (1999) classifies navigation behavior according to their complexity
and according to the kind of memory required to perform the behav-
ior. A surprisingly rich repertoire of spatial behavior can be performed
without spatial memory, such as course stabilization within a corridor,
obstacle avoidance or visual approach. This class of navigation behavior
is very similar if not identical to what has been referred to as locomotion
by Montello (2005). Integration of spatial information over time allows
forming a working memory. Path integration—the integration of perceived
ego-motion over time—is one example for a navigation behavior that can
be explained by the integration of spatiotemporal information in working
memory. Spatial information stored in long-term memory allows for various
navigation abilities ranging from stereotyped behavior such as following a
memorized route to cognitive—i.e., goal dependent and flexible—behavior,
such as planning a novel route through a well-known environment.
The most elaborate taxonomy of wayfinding comes from Allen (1999).
He defines three wayfinding tasks: exploratory navigation, travel to famil-
iar destination, and travel to novel destinations and provides prototypical
examples. Relocating to a new city and exploring the surroundings is
a typical example of exploratory navigation; commuting between home
and work place is a typical example of travel to familiar destinations,
and wayfinding guided by maps is a typical example of travel to novel
destinations (cf. Allen, 1999). Allen furthermore describes six wayfinding
means by which the tasks can be solved (oriented search, following a
marked trail, piloting between landmarks, path integration, habitual loco-
motion, referring to cognitive map). Essentially, these means range from
fundamental navigation mechanisms such as following marked trail or path
integration to knowledge retrieval processes such as referring to a cognitive
map.
For the investigation of the cognitive architecture underlying wayfind-
ing—the question how different cognitive components and processes involved
in wayfinding interact—the existing taxonomies have shortcomings. The most
important one is that none of the taxonomies aims at a detailed analysis
of different wayfinding tasks that would allow distinguishing, for example,
between a search in a familiar and a novel environment. Montello (2001,
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Wayfinding Taxonomy 155
2005) does not distinguish between different wayfinding tasks and different
kinds of cognitive components required. Mallot (1999) distinguishes between
different memory systems and learning processes involved, but again does not
explicitly differentiate between different tasks. Allen (1999) distinguishes be-
tween both, wayfinding tasks and wayfinding means. However, the wayfinding
means remain underspecified. Path integration is an example for a rather well-
defined mechanism (cf. Loomis, Klatzky, Golledge, Cicinelli, Pellegrino, &
Fry, 1993). Referring to a cognitive map (cf. Tolman, 1948; Kitchin, 1994),
by contrast, is rather ill-defined possibly comprising a number of different
operations that can elicit different kinds of knowledge.
In addition, the distinction of three wayfinding tasks is fairly coarse. For
example, “travel to a familiar destination” subsumes a number of different
tasks, such as following a memorized path and planning a novel path to a
known destination. These two tasks, however, are fundamentally different
requiring different forms of memory and different information processing:
For path following route-level knowledge is considered sufficient, while path
planning builds on survey-level knowledge and involves spatial inference
beyond simple recall from memory. In Allen’s taxonomy, four out of the
six wayfinding means can be applied in all three wayfinding tasks. For
the systematic evaluation of wayfinding behavior it is essential to classify
wayfinding tasks and the cognitive processes that are involved on a more
fine-grained level.
2. TOWARDS A NOVEL TAXONOMY OF
WAYFINDING TASKS
A more fine-grained taxonomy of wayfinding should take task constraints and
different kinds of knowledge into account and thus provide a more detailed (if
not comprehensive) classification of wayfinding tasks. For example, consider
search tasks: It is a basic property of a search task that the location of the
target (e.g., a specific object or room) is unknown. How does the search
for a specific target differ in a familiar and in a novel environment? Search
often takes place in unfamiliar environments, for example, when searching
for a specific office in a large, complex university campus that one has never
visited before. A search can also take place in familiar environments: Imagine
searching for a newly-opened bar in the downtown area of your hometown.
What is the influence of spatial knowledge on the selection of a wayfinding
strategy? In addition, which cognitive processes are shared by both tasks and
which are specific for one or the other task?
We reason that a navigator’s search behavior and search strategy will
be heavily influenced by their degree of familiarity with the environment. In
fact, it has been shown that familiarity with the environment does influence
strategy choice in directed wayfinding tasks (Hölscher, Meilinger, Vrachliotis,
Brösamle, & Knauff, 2006). The impact of the navigator’s knowledge on cog-
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156 J. M. Wiener, S. J. Büchner, and C. Hölscher
Figure 1. Proposed taxonomy of wayfinding tasks, classified by the existence of an
external aid, a specific destination and the availability of different levels of knowledge.
nitive task characteristics becomes even more apparent, when both extensive
familiarity with the environment and information about the specific location
of the target are available. Here the wayfinding agent can engage in a mental
planning process to determine the shortest route to the target. These examples
demonstrate that spatial knowledge is a key factor along which wayfinding
tasks may be classified. Spatial knowledge has been distinguished between
(at least) three levels of knowledge: knowledge about a point in space (e.g., a
landmark, a destination), knowledge about a sequence of points (i.e., a path
to a destination, often referred to as route knowledge), knowledge about an
area (i.e., knowledge about the spatial relation of at least two points, often
referred to as survey knowledge; Siegel and White, 19752: landmark, route,
and survey knowledge; Golledge, 1999: points, lines, areas).
In the following, we introduce a tentative taxonomy of wayfinding (see
Figure 1) that extends earlier taxonomies. The starting point is the defini-
tion of navigation by Montello (2001), in which he describes navigation as
consisting of two components: locomotion and wayfinding. We concentrate
on wayfinding, i.e., navigation in environmental space (cf. Montello, 1993)
that is directed to distant destinations or distant space, respectively. A crucial
2Ishikawa & Montello (2006) have shown that learning of information on these
three levels of knowledge need not follow a strict ascending order but can be obtained
in parallel.
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Wayfinding Taxonomy 157
aspect of wayfinding is that paths to the destination(s) are not available from
direct perception at the origin of travel. They have to be retrieved (or inferred)
from long term memory, or if unavailable, strategies and heuristics have to
be applied to traverse the unfamiliar parts of the environment.
2.1. Aided and Unaided Wayfinding
We further distinguish between wayfinding with and without external aids,
i.e., aided and unaided wayfinding. As Allen (1999) pointed out, much
everyday wayfinding behavior in man-made/urban environments is aided by
some form of externalized representations, such as maps, signage, route
instructions, or by modern hand-held computers and route planners. In some
cases aided wayfinding is rather simple, for example, following a trail that
is marked with signs to a distant terminal at an airport (cf. trail following:
Allen, 1999). Sign-following does not require considerable cognitive effort:
After having detected the sign, the agent needs to identify the relevant
information on the sign, match it with the target location and then execute
the action that is declared on the sign (Raubal, 2001). In sign-following the
path planning has already been done by the designer and as long as signs
are put up reliably at every decision point the agent faces very little efforts
of spatial reasoning. In the extreme case, sign following can be reduced to a
locomotion task.
In other cases, like wayfinding supported by a map, other cognitive
processes play a crucial role, for example symbol identification, object ro-
tation, self-localization, and establishing a match between the allo-centric
view provided by the map and the ego-centric view that is experienced
while moving through the environment (cf. Lobben, 2004). Taken together,
decision-making processes, memory processes, learning processes, and plan-
ning processes that are necessary to successfully solve unaided wayfinding
tasks have been externalized in aided wayfinding. We reason that the cognitive
demands of aided wayfinding are therefore fundamentally different from
those of unaided wayfinding. In the following we therefore focus on unaided
wayfinding. Clearly distinguishing aided and unaided wayfinding is also
helpful for comparing human and animal behavior, as animals are generally
restricted to unaided wayfinding. This is especially relevant since animal
models of spatial cognition on the behavioral as well as neural levels have
fruitfully inspired psychological research on human spatial cognition (e.g.,
Wang & Spelke, 2002).
Unaided wayfinding is first classified with respect to the agent’s goal.
The reason for travel through space can either have a specific spatial goal
(e.g., reaching a particular location) or a nonspatial goal (e.g., pleasure
when going for a walk along the beach). The difference between these two
kinds of wayfinding is the existence of one—or multiple—specific destina-
tion(s). Wayfinding without specific destinations is referred to as undirected
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158 J. M. Wiener, S. J. Büchner, and C. Hölscher
wayfinding. It includes both exploring a new environment to learn about its
structure, as well as recreational walks through familiar territory. Wayfinding
with specific destinations is referred to as directed wayfinding. Prototypical
examples are the walk or drive from home to work or the search for a specific
restaurant in a part of town one rarely visits. Directed wayfinding has a well-
defined stop criterion (i.e., reaching the destination) while the stop criterion
in undirected wayfinding is determined by the navigator (e.g., having received
enough joy from a walk) or by other, external constraints.
In a second step we classify directed and undirected wayfinding with
respect to the navigator’s spatial knowledge about three levels of geometric
space: (a) knowledge about the destination, (b) knowledge about the path
towards the destination, and (c) knowledge about the environment. Here we
refer to integrated knowledge about the environment, which is often called
survey or cognitive-map-like knowledge (e.g., Thorndyke & Hayes-Roth,
1982).3Obviously knowledge (a) about the destination and (b) about the
path to a destination applies only to directed wayfinding tasks, in which such
a destination is specified.
2.2. Undirected Wayfinding
Let us first consider undirected wayfinding, i.e., wayfinding without a specific
destination. The most important behavior in such situations is exploration.
In exploration, the environment is unknown and the goal is to develop a
representation of the environment. Exploratory behavior is often carried out
after relocating to a novel city, or during holidays when exploring the neigh-
borhood of the hotel (cf. Exploratory travel, Allen, 1999) or when you
reconnoiter a shopping mall (Zacharias, 2006). Undirected wayfinding is also
a frequent behavior in well-known environments. Imagine, for example, going
window-shopping in your hometown. While you know the environment—the
downtown area of your hometown—you are not planning a path to reach
a specific destination, rather you are strolling along and direct your travel
towards local sights of interest. Another example for undirected wayfinding
behavior is taking a pleasure walk through a familiar forest. During such
a pleasure walk, one is usually not striving for a specific destination (other
than returning home at the end of his walk). Yet, it is not performed without
intention, but might be aimed at receiving joy from walking through a pleasant
landscape. At some point, of course, you will want to return to your home or
car. Now you do have a specific destination and are no longer carrying out
an undirected wayfinding task, but a directed one. We will come back to the
question of how different wayfinding tasks can be nested or concatenated in
the discussion section.
3For our purposes, the exact format of the cognitive map or survey knowledge
remains underspecified and can be based on topological or metric relations.
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Wayfinding Taxonomy 159
2.3. Directed Wayfinding
As defined previously, directed wayfinding refers to wayfinding behavior in
which a navigator is striving to approach a single or multiple destinations.
The first distinction to be made is whether or not the navigator has knowledge
about where the destination is located with respect to his/her current location
or at least can infer this information through other familiar reference points
(e.g., knowing that a specific shop is next to city hall). If that is not the case,
the navigator is faced with what we refer to as a search. Search tasks can be
further divided into informed search and uninformed search.
2.3.1. Search Tasks. In informed search the navigator has survey knowledge
about the environment—i.e., he/she has knowledge about the relation of
different locations in the environment among each other. Imagine you are
searching for a friend who is in one of the restaurants in the downtown area
of your hometown. You certainly know the restaurants and how they are
located in relation to each other, but you still have to search for your friend.
In this case, you have knowledge about the environment, but you cannot tell
where in that environment the actual target is located.
In uninformed search, by contrast, the environment is unknown. A typical
example for an uninformed search is a firefighter, who has been told that there
is still a person in the burning house. He or she is now searching for the person
without any knowledge about the exact location of the person to be rescued.
The terms informed and uninformed search have also been used by
Ruddle, Payne, & Jones (1999), and they denote the same tasks that Darken
and Sibert (1996) called naïve search and primed search. We prefer informed
and uninformed search as these terms emphasize the information aspect of
the knowledge rather than the state of the agent. It is well conceivable, that
navigation behavior in informed and uninformed search and the cognitive
strategies applied will systematically differ. This is for a number of reasons:
First, the navigator remains oriented in informed search and the risk of getting
lost is minimized. The fact that the environment (i.e., the problem space) is
known, allows the navigator to systematically search through the environ-
ment, to avoid redundant walking and thus optimize search performance.
By contrast, in uninformed search a navigator cannot plan his/her search in
advance, and if the search task is to be solved efficiently, attentional resources
have to be attributed to monitoring, path integration, and other processes that
assure that the same part of the environment is not searched multiple times
and other parts of the environment are not ignored.
2.3.2. Target Approximation. If the navigator has knowledge about the des-
tination, we refer to the corresponding behavior as target approximation.
Target approximation can be further subdivided, depending on whether or
not the navigator possesses path/route knowledge, i.e., knowledge about one
particular path to the destination.
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160 J. M. Wiener, S. J. Büchner, and C. Hölscher
If the path is known, i.e., if it can be retrieved from long-term memory,
the navigator faces a path following task. He has to match sensory information
from the environment with the route knowledge he has memorized and he
needs to execute and monitor the appropriate sequence of actions (e.g., Cohen
& Schuepfer, 1980). A typical example is your everyday walk or drive to and
back from work. This task requires little attentional resources, almost no
reasoning and runs automatically; in fact it may get habituated (cf. Allen,
1999).
If no adequate route knowledge exists, i.e., no specific path sequence
from the start point to the destination is memorized, the correct path to the
destination has to be extracted or found (path finding). Here, a further dis-
tinction needs to be made between path planning and path search, depending
on the navigator’s survey knowledge about the environment.
In a well-known environment, in which the target location is known, but
a direct path towards it is unknown, because this particular path has never
been traveled before, navigators have to plan a path to reach the destination
(path planning; Gärling & Gärling, 1988; O’Neill, 1991b; Wiener, Schnee, &
Mallot, 2004). For this they have to refer to the survey knowledge they already
have available, combine it in new ways and possibly make inferences about
missing pieces. Compared to the other wayfinding tasks in our taxonomy,
path planning is probably based on the most elaborate reasoning processes.
The effort comes with a clear gain: Path planning can be employed to
flexibly identify efficient movement sequences for new combinations of start
and destination of a travel episode. McNamara & Shelton (2003) review
findings in the neuroscience community indicating that clearly separable
brain activation patterns also point to fundamental differences in the cognitive
processes underlying path following and the planning novel routes.
In unknown environments in which the navigator is informed about the
location of the target, but is lacking information about the space between the
current location and the target he/she has to search for a path (path search).
This situation arises, for example, when a distant target location is visible in
an otherwise unfamiliar surroundings. Imagine you visit Paris and, of course,
you are interested in visiting the Eiffel Tower. In some parts of Paris the Eiffel
Tower is visible but you cannot approach it directly. You have to search for
a path taking you to the bottom of the tower.
While path planning can rely on spatial inference to generate efficient
paths to a destination, path search requires that the wayfinding agent employs
heuristics to approach the destination in an iterative manner. This type of task
has been used by Hochmair & Karlsson (2005) to investigate wayfinding
strategies, namely the initial-segment and least-angle strategies. Both strate-
gies rely on local heuristics of choosing long sightlines or immediate path
options in the direction of the target location. But since no knowledge about
subsequent movement options beyond the current vista space is available,
navigators cannot plan ahead and are susceptible to detours and possibly the
need for backtracking from dead-end paths. If visual access to the target
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Wayfinding Taxonomy 161
is blocked during travel, one has to update the target location according to
ego-motion information to guide the path search processes (see also Conroy
Dalton, 2003).
3. DISCUSSION AND OPEN QUESTIONS
In this paper we have introduced a taxonomy of wayfinding tasks that extends
earlier accounts (Allen, 1999; Mallot, 1999; Montello, 2001). We argue
that knowledge about the location of a specific goal, knowledge about a
specific path toward a goal, and knowledge of the environment as a whole
crucially determine which behaviors and cognitive strategies can be applied
in order to solve a wayfinding task. Consequently, we build upon these three
levels of spatial knowledge (cf. Golledge, 1999; Siegel & White, 1975)
to provide a more fine-grained differentiation of wayfinding tasks. For the
systematic investigation of the cognitive components and processes involved
in different wayfinding tasks such a detailed specification of the task demands
appears essential. We believe that the taxonomy presented here constitutes
an important (initial) step towards the development of a more comprehensive
understanding of the cognitive architecture of human (and possibly animal)
wayfinding behavior.
This taxonomy is of tentative nature for a number of reasons:
1. While we have provided a more detailed differentiation of wayfinding
tasks, a vital step is left to future research—the assignment of necessary
and sufficient cognitive processes, components, and mechanisms to solve
the wayfinding tasks identified. That is, answering the question, what
information processing stages are required to solve a task A and what
processes are required to solve a task B? For several of the wayfinding
tasks in our taxonomy the real-world examples in the text already indicate
principal differences, e.g., between search and path planning. We have
identified the role of different levels of spatial knowledge. Further research
and theoretical elaboration should be based on elaborate task analyses to
identify and validate the underlying cognitive (sub-) processes in detail.
2. The taxonomy is currently restricted to prototypical examples. For the sake
of clarity we assume, for example, the clear-cut existence or nonexistence
of survey knowledge (cognitive maps). In everyday navigation, however,
we rarely face situations in which we either have perfect knowledge about
an environment or no knowledge at all. Hence, we are often engaged
in wayfinding tasks in which part of the environment is known, while
we have limited, fragmented or uncertain knowledge about other parts
of the environment. One possibility to account for such situation is by
assuming nested or concatenated wayfinding tasks. Consider the following
situation: You are about to navigate towards a specific restaurant in a part
of your hometown that you hardly ever visit. Such a wayfinding task can
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162 J. M. Wiener, S. J. Büchner, and C. Hölscher
be divided into subtasks that can be expressed as wayfinding tasks defined
in the current taxonomy. The first part of your navigation can be described
as a path planning task, from your home towards the unfamiliar part of
your hometown. As soon as you enter that area, you are missing detailed
environmental knowledge and the task changes from path planning to
uninformed search.4We reason that combining the limited number of
wayfinding tasks of our taxonomy in such ways will capture the majority
of actual real-world unaided wayfinding problems.
3. The taxonomy currently ignores the existence of background knowledge.
Even if a navigator is unfamiliar with a specific environment he can
use schemata that he has learned during earlier experiences with similar
situations. For example, railway stations are not only located at default
places (often near the center of town), they also function similarly; rest
rooms in large public buildings are often located in proximity of staircases
or elevators, etc. While until now very little research in spatial cognition
has approached this important topic (but see Murakoshi & Kawai, 2000;
Kalff & Strube, under review), it is obvious that such knowledge affects
how we solve wayfinding tasks.
4. This taxonomy of wayfinding tasks concentrates on the usage, rather
than acquisition of spatial knowledge during wayfinding. The process
of learning about an environment is not included in this tentative tax-
onomy. Background knowledge as well as survey knowledge about the
environment that is to be navigated are generally acquired and memorized
before the wayfinding task arises, often over many episodes or years. A
navigator may also learn new information about the environment during a
wayfinding episode. For example, if the navigator is performing a search
task and returns to a previously visited location (after having moved in
circles or backtracking from a dead-end), he may realize that he need
not enter the same fruitless path option again, informing at least the local
movement decision. Whether or not such experience or inference is stored
beyond the current wayfinding episode is clearly beyond the scope of this
paper.
5. The taxonomy is currently limited to unaided wayfinding. We argue that
cognitive processes and task characteristics in aided wayfinding may differ
dramatically from the aided wayfinding tasks focused on in our approach.
It will clearly be valuable for basic research as well as for applications
in Geography, Information Design or Human-Computer Interaction to
develop such a fine-grained analysis of aided wayfinding tasks as well.
Aided and unaided wayfinding might also interact. Imagine, for example,
receiving a destination description—a description of where the destination
is located rather than how to get there (cf. Tomko & Winter, 2009). In such
4Tenbrink & Winter (2009) have recently pointed out that in such combined tasks
the granularity of spatial information potentially required from external sources (cf.
unaided wayfinding) will vary systematically as well.
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Wayfinding Taxonomy 163
cases the destination knowledge is provided (aided wayfinding), while the
actual travel towards the destination remains an unaided wayfinding task
(target approximation, see Figure 1).
To summarize, the main contribution of this tentative taxonomy is the
introduction of a systematic terminology to differentiate between wayfinding
tasks that pose different cognitive demands on the navigator. We hope that
the microlevel distinction of wayfinding tasks will help to further sharpen
research questions about cognitive processes and strategies in wayfinding
and to facilitate a better integration of knowledge gained across wayfinding
studies that were difficult to compare in the past.
ACKNOWLEDGMENTS
This research has been supported by the Volkswagen Foundation and the Ger-
man Research Foundation (DFG) in the Transregional Collaborative Research
Center “Spatial Cognition” (SFB/TR-8).
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