Humans and many non-human animals need to accurately
and efficiently navigate from one place to the next in their
environment. Over 3,000 years ago the volcanic islands of the Pacific
were settled by the people of Polynesia (Gibbons, 2001). These
navigators sailed in craft from Samoa to Hawaii covering an area
extending some 4,500 km without the benefits of modern navigational
equipment. Errors in the estimation of direction or position during
trips to and from the islands in this region could have dire
consequences. Some 2,400 years later, European sailors had started
mastering oceanic navigation and were probably surprised to discover
that people had already traveled to, and were living on, these remote
Pacific islands. Today, few humans make such long trips without the
benefits of modern navigational tools.
Many non-human animals were engaging in impressive
feats of navigation long before people were navigating across oceans.
Desert ants (Cataglyphis fortis) live in subterranean nests that
insulate them from arid conditions above ground. During the course of
the day foraging ants depart their nest in search of food, in this case
other insects that have succumbed to the desert heat. The foraging
desert ant may be required to take a rather long (several hundred
meters) and circuitous route before it finds a food item, since the
location of food items vary dramatically from day to day. Once the
foraging ant has found food, it takes a direct route back to the
entrance of the subterranean nest (Wehner, 2003). Errors in the
estimation of the nest location could easily prove fatal. Notably, like
an early human navigator on the ocean, the ant’s impressive feat of
navigation is accomplished in a relatively homogeneous environment
lacking surfaces that could be used as landmarks to help indicate the
position of the nest.
These two examples help to introduce the idea that
survival within an environment may require that an animal (including
humans) navigate to goals over short distances, such as a few meters
(e.g., the desert ant), or over distances encompassing several hundred
kilometers (e.g., the Polynesian sailors). How are such navigational
feats accomplished? When the goal location is not visible, perhaps
because it is far away or occluded, the navigator must use his/her
memory to navigate. How is this memory or representation of the
spatial environment stored in memory?
Two prominent theoretical perspectives have guided
research designed to investigate this question within the context of
place learning. On the one hand, a theoretical approach to spatial
learning has grown out of the principles of associative learning. This
approach proposes that the process of learning about one’s environment
is subject to the same rules as other forms of learning. As one moves
through an environment associations are built between stimuli, such as
objects in the environment, and responses, such as walking towards an
object; spatial learning follows conventions of classical and
instrumental conditioning. On the other hand, cognitive mapping theory
proposes that spatial place learning is different from other forms of
learning. In particular, it is proposed that a topographical
representation of one’s environment is constructed in the hippocampus
(O’Keefe & Nadel, 1978). According to this approach, initially proposed
by Tolman (1948), spatial representations allow for flexible navigation
to a goal location from familiar or novel positions with equal capacity.
These two theoretical approaches allow for
interesting predictions as to the form of the representation stored in
memory as well as the susceptibility of the representation to cue
competition. According to the associative approach, a representation of
one’s environment is incrementally built with continued experience with
the environment. Thus, the navigator forms egocentric-based, or
viewpoint dependent, representations of the environment. In contrast,
the topographical representation proposed by the cognitive mapping
theory would be stored viewpoint independent.
The associative approach to spatial learning further
predicts that the learning of spatial information is subject to cue
competition. Two classic forms of cue competition are blocking and
overshadowing. In blocking, a conditioned stimulus prevents conditioning
of a subsequently presented stimulus – the first conditioned stimulus
blocks conditioning of a second stimulus. Whereas in overshadowing,
a salient conditioned stimulus can interfere with the conditioning of a
less salient stimulus – the salient conditioned stimulus overshadows
the weaker conditioned stimulus. The associative approach to place
learning predicts that spatial learning would be susceptible to cue
competition, whereas cue competition is not likely to be evident in
spatial learning, according to the cognitive mapping theory, as
additional spatial cues would provide a more detailed topographical map.
A great deal of our understanding of place learning
is rooted in animal studies (e.g., Olton & Samuelson, 1976; Tolman,
1948; for reviews see Gallistel, 1990; O’Keefe & Nadel, 1978; Redish,
1999; Shettleworth, 1998; Wang & Spelke, 2002). The focus of this review
is to examine the two theories of place learning through the examination
of current research using three main behavioral techniques, developed
for the study of spatial navigation in animals, but modified for the
study of human spatial navigation. We will begin this review with a
general overview of the three behavioral tasks as developed to study
place learning in non-human animals, providing one example of a research
investigation of spatial place learning to demonstrate the task. This
part of the review is meant only as an overview to provide a general
understanding of the research area for comparative purposes; a complete
review would be considerable and well beyond the scope of our review
(interested researchers are encouraged to refer to volumes such as
O’Keefe & Nadel, 1978; Redish, 1999). We will briefly discuss how these
techniques have not only furthered our understanding of the associative
approach and the cognitive mapping theory at a behavioral level, but
also strengthened our understanding of the neurological mechanisms of
place learning.
Next, we will turn to the area of human navigation,
focusing on how the three behavioral techniques have recently been
adopted, and modified, for study of human spatial abilities using
virtual environments. Again, our review of these studies will focus on
whether the results support an associative approach or a cognitive
mapping approach to spatial learning. Finally, we will discuss how the
use of these behavioral techniques represents an excellent opportunity
for future comparative studies of spatial learning.
Non-Human Animal Research: Three Prominent Tasks
Over the past several decades, three laboratory based
paradigms, the Morris-type water maze task, the radial arm maze task,
and the geometric arena task, have been extensively used to investigate
spatial learning in non-human animals.
Morris-type water maze task
![](Figures/Kelly&Gibson/K&G1.jpg)
Figure 1. An image of a Morris
water maze created from a round (2 m diameter) cattle
trough. The position of the hidden platform is revealed
by a light colored disk in the right portion of the
pool. The markers on the perimeter of the maze indicate
a coordinate system for placing multiple landmarks.
Photo courtesy of Brett M. Gibson. |
|
The Morris-type water maze task (MWM; Morris, 1981)
has a long history of use in investigations of spatial cognition with
non-human animals, primarily rodents (for reviews see Brandeis, Brandys,
& Yehuda, 1989; Redish, 1999). For this aversively motivated task a
shallow pool is filled with cool water that is made opaque using dried milk or
non-toxic paint (see Figure 1). A platform, which is the goal, is positioned at
a fixed location in the pool and rests just below the surface of the water so
that it is not visible. The animal is typically released from the side of the
pool and searches for the hidden platform to allow it to escape from the cool
water. With continued experience in the pool, the animal becomes very efficient
at locating the hidden platform. (Efficiency is typically seen by the animal
making a more direct path from its starting location to the hidden platform; see
Video Clip 1 showing an example of a mouse
performing a MWM task.)
A strength of the MWM task is that it can be used to
investigate a variety of different navigational systems as well as the
types of spatial cues animals use when navigating. For instance, many
researchers have used this task to learn about how rats use individual
objects or configurations of multiple objects as landmarks to find the
hidden platform. Roberts and Pearce (1999), for instance, designed an
experiment to examine whether blocking would be evident in a spatial
task using the MWM. In the first phase of the experiment, the
investigators trained rats to locate a submerged platform that had an
object attached directly to the platform. Thus, the rats simply needed
to swim directly to the object to find the platform (a behavioral
strategy known as beaconing). The second phase of the experiment was
identical to the first, with the exception that distinctive visual cues
were placed around the maze (extramaze cues). Thus, in this second phase
the rats could continue to use the beacon to find the submerged platform
or they could also encode the extramaze cues—allowing them to find the
platform with either the beacon and/or the extramaze cues. To examine
whether the rats had encoded the extramaze cues (from the second
training phase) the investigators concluded the experiment with a test
phase in which both the platform and the beacon were removed. If the
rats had encoded the extramaze cues, they should have been able to swim
directly to the location of where the platform should have been located.
However, if the rats had not encoded the extramaze cues—the learning of
the beacon in the first phase blocked the learning of the
extramaze cues in the second phase—the rats would not know where to
search for the platform. Indeed the latter was the case; the rats swam
randomly in search of the platform. The study by Roberts and Pearce is
particularly interesting because it supports the associative account of
spatial learning.
Radial arm maze
The radial arm maze task (RAM; Olton & Samuelson,
1976), like the MWM, also has a long history of use by researchers
interested in the spatial cognitive abilities of animals (see Hodges, 1996). A
typical RAM consists of eight flat "arms" that extend out from a central
platform (see Figure 2). Radial arm mazes are often elevated and/or have side
walls that may be or may not be fully enclosed so that the animal stays on the
maze while completing the task. Food can be placed in cups or wells at the
distal end of one or more of the arms. During a typical session, an animal is
placed in the center of the maze and allowed to retrieve food from the distal
ends of four arms (see Video Clip 2 showing an example of a rat
performing in a RAM task).
![](Figures/Kelly&Gibson/K&G2.jpg)
Figure 2. An image of an enclosed
and fully automated 8-arm radial maze. Entries between
the arms and central chamber are recorded using a system
of photocells. Water can be dispensed in the distal ends
of the arm by a pump and used as a reward for correct
responses. Plexiglas guillotine doors between the arms
and central chamber can be raised or lowered by a
computer controlling the session. Photo courtesy of
Brett M. Gibson. |
|
Following a retention interval of variable length,
the animal is placed back on the maze and permitted to search any of the
eight arms. If the animal has encoded the locations of the arms that it
has previously visited into working memory, then it should limit its
searching to only those arms that it has not already visited. Like the
MWM, the RAM can be arranged to investigate the use of a variety of
different navigational cues. The RAM has been used to investigate the
use of egocentric mechanisms of navigation (Ossenkopp & Hargreaves,
1993), local cues identifying each arm (Diez-Chamizo, Sterio, &
Mackintosh, 1985), the use of distal room cues (McDonald & White, 1993),
the interaction between several types of cues (Diez-Chamizo et al.,
1985; Gibson & Shettleworth, 2005), and even episodic-like memory (Babb
& Crystal, 2005). While the RAM was originally developed for use with
rats, analogues of the RAM task have been used with mice, rabbits, hedge
hogs, guinea pigs, corvids, pigeons, and chickens (for a review see Lipp
et al., 2001).
Recently, Gibson and Shettleworth (2005) used the
radial arm maze to examine whether learning about the response
required to travel to a goal location would interfere with learning
about the physical place of the goal within the larger
environment. Theories of associative learning would suggest that these
two types of cues should compete with each other for control of
behavior, whereas cognitive mapping theory would suggest that learning
about the response required to get to a goal and learning about the
location of a goal should not compete with each other (i.e., both should
be acquired simultaneously and without competition).
In this study, the researchers used an associative
blocking paradigm (Experiments 3-5) to examine if prior response
learning would interfere with subsequent place learning. During the
first training phase, one group of rats (referred to as "group Same" for
reasons which will become clear) was released from the distal end of a
start arm; they were trained to travel through the center of the maze
and, once the center was crossed, to make a consistent turn (e.g., cross
the center and always turn right). This response would allow the rats in
this group to arrive at the arm that contained food. A second group of
rats (referred to as "group Different" for reasons which will become
clear) was also released from the distal end of a start arm; like the
rats in group Same, they were trained to travel through the center of
the maze and, once the center was crossed, to make a consistent turn
(e.g., cross the center and always turn left). During this initial phase
of training the maze was surrounded with an opaque curtain to eliminate
place learning.
The curtain was removed for the second phase of
testing, allowing the rats to learn about the physical place of the goal
within the larger experimental room. The location of the reinforced arm
in this second phase of testing required that the rats in group Same use
the same response as learned during Phase 1 training (e.g., turn right),
whereas for the rats in group Different, the response that was now
required to arrive at the reinforced arm was different than the response
learned in Phase 1 training (e.g., turn right).
The third phase of this experiment consisted of
preference tests. Both groups were given an opportunity to choose either
an arm that was consistent with the response learned from training phase
1, or an arm that was consistent with the place learned from training
phase 2. Associative learning theory would predict that the rats in
group Same should learn less about the place of the goal, compared to
the rats in group Different, since the response used to get to the goal
during the experiment was always the same. Thus, for the rats in group
Same, learning about a response should block or compete with subsequent
learning about the place of food in the room. The subsequent place cues
were redundant with the response information. Indeed the rats in group
Same showed a strong preference to select the arm that was consistent
with the response used to get to the food, rather than the arm that was
consistent with the place of food during training. The rats in group
Different did not display such a preference for either the response or
the place response. Thus, for group Different, learning about the
response did not block learning about the place, as was seen with the
rats in group Same, because the learning in the second phase of training
provided new information. Again, the results of these experiments
support an associative learning approach to spatial learning.
Geometric arena task
![](Figures/Kelly&Gibson/K&G3.jpg)
Figure 3. An image of a fully
enclosed geometric arena. Distinctive featural cues are
provided by the four uniquely colored and shaped panels
at each corner of the environment. The geometric
information is obtained from the rectangular shape of
the enclosure itself. Only one of the four identical
containers at each corner contains a reward. Photo
courtesy of Debbie M. Kelly. |
|
More recently, a third behavioral task has been
developed to examine whether animals can use the shape of the
environment itself when navigating, or more specifically during the
initial orienting step of navigation (Cheng, 1986; Margules & Gallistel,
1998). In this task an animal is disoriented (usually by slowly rotating
the subject in a small container) before being placed in a fully
enclosed environment. In the original investigation, and many subsequent
studies, the environment is rectangular in shape and several distinctive
objects or panels are placed at each corners of the environment (see
Figure 3). A reward is hidden in one corner of the environment and the
animal’s task is to search for this food reward. Transformation tests,
which manipulate one or more properties of the nongeometric (the
distinctive objects or features) or geometric (the shape and size of the
environment) information, are subsequently conducted to examine what
cues the animal had encoded during initial training in the environment.
Cheng (1986) initially developed the geometric arena
task to examine whether rats could encode featural and geometric
properties of the environment. As described above, disoriented rats
searched in a fully enclosed rectangular environment for food that was
consistently located at one of the corners (a reference memory task).
Each corner also contained unique featural cues; each corner had unique
color, shape, texture, and scent information. Interestingly, in the
reference memory version of the task the rats could learn to search
primarily in the corner that contained the hidden food, but they also
chose the corner diagonally opposite to the rewarded corner more than
expected by chance. This error was termed a geometric error, or a
systematic rotational error, because according to geometric cues alone
this incorrect corner was indistinguishable from the rewarded corner;
the two corners were geometrically equivalent. Not only was this
the first demonstration that rats were encoding the overall shape of
their environment (i.e., geometric information) in a place learning
task, but the results also showed that the geometric information had the
potential to overshadowing the encoding of the featural cues. (In a
working version of the task the geometric cues did indeed overshadow the
learning of the features.) This study led to the hypothesis that rats
have a geometric module – an encapsulated representation that stores
information about an environment’s geometric properties, separate from
the featural or non-geometric properties. A plethora of studies
followed, examining the encoding of featural and geometric information
by other species (e.g., chicks: Vallortigara, Zanforlin, & Pasti, 1990;
fish: Sovrano, Bisazza, & Vallortigara, 2002; pigeons: Kelly, Spetch, &
Heth, 1998; rhesus monkeys: Gouteux, Thinus-Blanc, & Vauclair, 2001; and
humans: Hermer & Spelke, 1994, 1996). Interestingly, many of these
studies show that the geometric information does not overshadow learning
about the featural properties of the environment. Thus, the results of
this research paradigm do not yet clearly support either an associative
approach or a cognitive mapping theory of place learning.
Neurological Mechanisms: Animal-based Research
The three navigation tasks we reviewed outlined above
typically require an animal to navigate to a hidden goal location. To
successfully locate this goal area the animal must maintain an accurate
representation of the environment (Gallistel, 1990). As we have seen, in
the MWM and the RAM tasks, this representation can be established using
cues in the room that maintain a fixed relationship to goal. In the
geometric arena task, the representation may be established using the
overall shape of the enclosure (or in some studies additional
distinctive featural cues). Single-cell recording studies have found
cells that selectively fire when an animal is in a particular location
(for a review see O’Keefe & Nadel, 1978). A different population of
these cells fire as the animal moves through an environment. The
selective activity of these place cells can be used to reconstruct the
path of an animal through an environment. This mapping of the
environment is argued to support the idea that the hippocampus forms a
type of cognitive map.
O’Keefe and Dostrovsky (1971) were the first to
characterize the presence of cells in the hippocampus of the rat that
fire when the animal enters a particular place in the environment. These
place cells, the pyramidal cell of the hippocampus and granule cells of
the dentate gyrus, comprise 90% of the cells that make up the
hippocampus. Studies have shown that place cells acquire their receptive
field quite quickly, typically within a few minutes after an animal has
been placed in a novel environment, and these cells can retain their
field even when landmarks are removed (Muller & Kubie, 1987; O’Keefe &
Conway, 1978). Interestingly, however, rats navigating in the dark and
blind rats (either blinded late in life or one week postnatal) also show
place cell responding when they locomote through an environment (Hill &
Best, 1981; Markus, Barnes, McNaughton, Gladden, & Skaggs, 1994; Save,
Cressant, Thinus-Blanc, & Poucet, 1998). This illustrates that, for
rats, other forms of sensory information in addition to vision must
influence place cell firing.
One type of sensory input that appears to be
important is self-motion or idiothetic information (e.g., Save, Nerad, &
Poucet, 2000). Idiothetic information might be essential for encoding
geometry (for a review, see Redish, 1999). However, this seems to
contrast with studies that show hippocampal cells maintain their spatial
tuning even when the subject is passively transported through a darkened
room (Gavrilov, Wiener, & Berthoz, 1998; Lackner & DiZio, 2005). Yet,
lesions of the hippocampus and related structures have resulted in
dramatic deficits in place learning tasks and the salience of
environmental geometry (Bingman, Erichsen, Anderson, Good, & Pearce,
2006).
A second set of cells that would appear important to
navigation and returning to goals in these two tasks are head direction
cells (for a review see Taube, 1998). Head direction cells fire when an
animal’s head is pointing in a single direction with respect to the
environment. Thus, the cells fire when they maintain a particular angle
with respect to the broader environment (allocentric relationship)
rather than with respect to the animal’s body (egocentric relationship).
These cells, many of which are located in the postsubiculum of the rat,
would appear to provide important information for formation of a spatial
representation. Head direction cells can be sensitive to the movement of
distal directional cues such that as the distal cues are rotated, so too
are the tuning of the directional cells.
Much of what we know about the neurological
mechanisms that underlie spatial navigation has been determined through
the use of MWM, RAM, and more recently geometric arena tasks. These
standardized behavioral procedures have allowed researchers studying
animal spatial cognition to develop a complex understanding of the
involvement of the hippocampus for successful navigation within
environments. This research has also put into question the original idea
proposed by O’Keefe and Nadel (1978) that the hippocampus stores a
cognitive map of the environment. Indeed, research over the past two
decades has shown that lesions or damage to particular areas within the
hippocampal structure result in differential performance in many tasks
of place learning. However, particular to our review, recent studies
(e.g., Sutherland, Chew, Baker, & Linggard, 1987) have shown that
previous investigations which have reported that rats readily show novel
shortcutting when placed at a novel starting point within the MWM (e.g.,
Morris, 1981) may have gathered information about the arena when
initially learning the task – thus, putting into question the actual
novelty of the novel starting points. Such careful behavioral research
programs, in combination with physiological studies, continue to present
new challenges to current theories of place learning.
Human Research Adopting the Three Prominent Tasks
Traditionally, many studies of human spatial
cognition have adopted either paper-and-pencil tasks (e.g., the Mental
Rotations test: Vandenberg & Kuse, 1978; and the Object Location Memory
test: Eals & Silverman, 1994; Silverman & Eals, 1992) or real-world
tasks to examine how people use environmental cues to learn a spatial
layout. Two-dimensional tasks, such as traditional paper-and-pencil
versions of spatial wayfinding tasks, have played a major role in our
understanding of spatial cognition. However, the cognitive processing
required during these tasks may be, and indeed has been argued to be,
quite different than during navigation through a real-world environment
(see Hegarty, Montello, Richardson, Ishikawa, & Lovelace, 2006). This
concern has led many researchers to design investigations to directly
compare the use of spatial cues and performance accuracy on
two-dimensional wayfinding tasks with those conducted in real-world
environments.
Typical studies of real-world navigation require
participants to learn a route though a room, a building, or an outdoor
setting such as a university campus or a shopping center (e.g., Bell &
Saucier, 2004; Foreman, Stanton-Fraser, Wilson, Duffy, & Parnell, 2005).
Although real-world navigation tasks allow for an ecologically valid
examination of spatial ability in comparison to standard
paper-and-pencil tasks, such paradigms are not also without their
drawbacks. Due to the large spatial scale of these tasks, they lack the
precise control and manipulability necessary for examining many
questions of spatial ability (e.g., it is difficult, if not impossible,
to modify or remove large global cues). The physical nature of a
wayfinding task, although not necessarily challenging for young adults,
the focal group for many of these studies, may be too demanding for some
older adults for which spatial abilities is an important field of study.
Furthermore, real-world navigation tasks by their very nature require a
moving participant and thus cannot be used for brain imaging studies
important for the understanding of neurological foundations of
navigation and subsequent linkage with the vast amount of non-human
studies in this area.
Recent technological advances have allowed for new
approaches to examine human spatial learning. In particular, the use of
computer-generated immersive virtual realities and virtual environments
appears to be quite fruitful for furthering our knowledge of spatial
cognition. Immersive virtual reality (VR) and desktop virtual
environments (VE) have proven useful as evaluative tools for behavioral
and cognitive assessments as well as experimental investigations of
spatial learning (Skelton, Ross, Nerad, & Livingstone, 2006). Typically,
VR is characterized as the participant obtaining a sense of
participating within the environment being shown. Technology such as the
CAVE (CAVE Automatic Virtual Environment: Cruz-Neira, Sandin, & DeFanti,
1993) or head-mounted displays in conjunction with a head tracker have
been used to achieve this sense of immersion. Other interaction devices
(e.g., VR gloves) may also be used. Studies which have examined spatial
abilities using VR have shown that this approach may be very useful not
only for understanding how space is being represented, but also as a
possible assessment tool for examining spatial memory loss due to aging
and/or cognitive disorders (e.g., Moffat, Zonderman, & Resnick, 2001;
Rose et al., 1999). However, the technology required to investigate
spatial abilities using a VR approach is still very expensive,
cumbersome, and not easily portable. Furthermore, due to the immersive
nature of the VR approach many participants experience "cybersickness,"
a type of motion sickness that may affect men and women, as well as
young and older individuals, differently (e.g., Liu, Watson, & Miyazaki,
1999).
An alternative, more economical and portable approach
to investigating spatial abilities using a more ecologically valid
approach is to use a VE task. VE tasks (as we are defining them) differ
from VR in that the participant views a 3D environment with a lessened
sense of immersion (this also reduces the experience of cybersickness).
Desktop monitors or wide-screen projection units are the typical means
of display. As in VR, this approach has also been used not only to
investigate spatial abilities from a research point of view, but also
has been evaluated as a tool for applied approaches to assessment and
rehabilitation for individuals with spatial ability loss (Rizzo et al.,
2001). Because the VE approach lends itself more readily to studies of a
comparative nature, we will focus our review on research which has
adopted this technology to investigate spatial learning in humans.
Do VE approaches to the study of spatial learning
have external validity? Is the spatial information presented using VE
sufficient to generate similar spatial representations as that when one
is navigating through a real-world environment? Studies using VE to
simulate a task originally designed to study spatial learning in rodents
may shed light on these questions. In the following sections we will
review the three prominent tasks of spatial learning adopted from animal
procedures to study human spatial navigation.
Virtual Morris-type water maze task
The virtual Morris-type water maze task (VMWM) is a
computer-generated environment developed after the MWM, a very popular
task to study non-human spatial memory (Astur, Ortiz, & Sutherland,
1998; Hamilton, Driscoll, & Sutherland, 2002; Hamilton & Sutherland,
1999). As discussed earlier in this review, in the original MWM task an
animal is trained to locate a platform submerged in a pool of opaque
water. Typically, distant featural cues are presented on the walls of
the pool in a fixed spatial relationship to the submerged platform such
that the animal can use these cues to accurately locate the platform.
This task has been used extensively to examine the relationship between
spatial memory and the hippocampus (e.g., O’Keefe & Nadel, 1978; Redish,
1999). The virtual version of this task presents a human participant
with a similar visual experience. The participant views an arena that
typically has featural cues on the walls and using a mouse or joystick
can navigate to a hidden platform. The platform rises out of the water
when the participant has successfully reached its location. As in the
MWM, participants can begin each trial from several different locations.
Hamilton et al. (2002) investigated whether humans
navigating through a VMWM would form a topographical-like representation
of the environment (supporting a cognitive mapping theory) or would form
a viewpoint dependent representation of the environment (supporting an
associative learning approach). Hamilton and colleagues trained
participants to navigate directly from a starting position to a hidden
platform within a VMWM. The participants were divided into independent
groups based on the opportunity to view extra-maze cues (e.g., pictures
on the walls) and/or to navigate through the entire arena or just half
of the arena. By systematically limiting some of the groups’ experience
with cues within the environment, or ability to navigate through only
one half, Hamilton and colleagues were able to directly test whether
participants could take novel routes to locate the goal when their
original training routes were not available. The researchers found that
the participants’ performance did not support a cognitive mapping theory
of place learning; the participants that had their navigation restricted
to one side of the arena showed an increase path length and latency to
locate the platform when navigating on the novel side of the arena. The
results of this study seem to contradict an earlier study by Jacobs,
Laurance, and Thomas (1997) who found that humans navigating in a
virtual environment showed good transfer from a limited set of training
views to novel testing views. However, in the Jacob et al. study it is
not clear whether participants locomoted through the entire environment
when initially learning the task. If this was the case, the participants
may have built up a representation of the entire environment even though
their initial start locations were limited. Thus, the Hamilton et al.
investigation provides a clearer examination of the viewpoint dependency
in a virtual environment.
The study by Hamilton et al. (2002) is particularly
important for our understanding of spatial learning from a comparative
point of view because it was designed to replicate a previous study
conducted by Sutherland et al. (1987) using rats in a real-world MWM.
Hamilton and colleagues found very comparable response patterns as those
reported by Sutherland et al. Such a comparative approach to examining
spatial learning lends support to the conclusions that humans navigating
through a VMWM use similar spatial learning principles as seen by rats
navigating in MWM. Furthermore, these comparative studies provide
necessary bridges between human and non-human research on spatial
learning—research necessary for furthering our understanding of the
neurological mechanisms involved in spatial learning.
Virtual radial arm maze task
The virtual radial arm maze task (VRAM) is similar to
the real-world counterpart in that a participant begins to navigate the
maze at a central platform. From this central position, the navigator
can transverse down one of (typically) eight arms (although 12 armed
VRAM tasks have also been used, i.e., Levy, Astur, & Frick, 2005). Using
the VRAM an experimenter may measure both working and reference memory
components. As in the RAM, during the typical VRAM paradigm only half of
the arms are baited (contain a reward), and the participant must use
extramaze cues to remember which arms are rewarded and which ones are
not; this is the reference memory component of the task. However, on a
given trial the participant must also remember which arms s/he has
visited to avoid making revisits; this is the working memory component
of the task.
Although the RAM has not received as much study as a
virtual task compared to that of the MWM, and to our knowledge none of
these studies have directly examined the properties of the spatial
representation, we have chosen to include it in our review because we
would like to argue that this virtual task has much untapped potential
for furthering our understanding of the properties of spatial learning.
The majority of the studies to date that have used the VMWM have shown
similar results to that found in non-human studies in real-world MWM.
This is not so with the VRAM. For instance, Astur and colleagues have
robustly shown that the participants navigating in the VRAM (either 8 or
12 arms) do not show the typical male superiority effect (Astur, Tropp,
Sava, Constable, & Markus, 2004; Levy et al., 2005). This difference has
lead Astur and colleagues to propose that the VMWM and the VRAM may
differentially allow for spatial and non-spatial strategies in solving
the task. The MWM and the RAM are considered gold standards among
non-human spatial tasks. Using virtual versions of these tasks, where
procedural differences in variables such as motivation, environmental
cues, stress, and motor demands can be held constant, will allow for a
better understanding of the underlying properties of spatial learning.
Virtual geometric arena task
Finally, the last type of virtual environment we will
discuss in our review is the use of a virtual geometric arena task
modeled after Cheng (1986). In one version of the task participants
orient in a fully enclosed rectangular environment and are required to
locate a reward consistently positioned at one of the four corners
(Kelly & Bischof, 2005). Distinctive features are sometimes available,
and at other times only the geometric properties of the environment can
be used to differentiate the corners.
![](Figures/Kelly&Gibson/K&G4A.jpg) |
![](Figures/Kelly&Gibson/K&G4B.jpg)
Figure 4. An image of one fully
enclosed geometric arena used in a virtual environment.
Responses were made by mouse-clicking on one of the four
black patches in front of the objects. Only one of the
four response patches was associated with reinforcement.
Top panel shows the environment with distinctive
featural cues, the four uniquely colored and shaped
objects, at each corner of the environment. The
geometric information is obtained from the rectangular
shape of the enclosure itself. Bottom panel shows the
environment without any distinctive featural cues. Photo
courtesy of Walter F. Bischof and Debbie M. Kelly. |
|
Kelly and Bischof (2005) examined the use of featural
and geometric information by adults navigating in a virtual environment
designed to be similar to the real-world environment experienced by
Cheng’s (1986) rats. Men and women were trained to locate a goal in one
of four corners of a fully enclosed rectangular room (see Figure 4).
Contrary to real-world studies of this nature, in which participants
were disoriented prior to the start of each trial, the participants were
always oriented. Therefore, to ensure that the participants were using a
spatial strategy when searching for the goal, rather than simply
memorizing the absolute position of the goal on the screen of the
monitor, the environment was shown from eight different viewpoints
selected quasi-randomly across trials. One group of participants
initially experienced the environment with distinctive featural
information at each corner (see Figure 4), and once they were accurately
locating the goal, they were given transformation tests which
manipulated either the distinctive featural cues, the geometric
properties of the environment, or both. Upon completion of the
transformation tests this group was retrained in the same environment
but now all of the distinctive features were removed (see Figure 4).
Once the participants were accurately locating the goal in this modified
environment, they were given transformation tests in which some aspect
of the environment’s geometric properties were manipulated. A second
group of participants received the same training and testing conditions,
but in the opposite order (i.e., geometric training, geometric testing,
feature training, and feature, geometric, or both testing).
In one of the experiments in the Kelly and Bischof
(2005) study, participants entered into the environment from a limited
set of starting points. The researchers then tested the participants’
accuracy at locating the goal from novel starting points. The
participants were just as accurate at finding the goal when staring from
familiar or novel starting locations (in contrast to a similar study
that used two-dimensional environments which showed viewpoint dependency
for novel orientations; Kelly & Spetch, 2004). Thus, the results from
this manipulation support the cognitive mapping theory. However, a
weakness of this experiment is that although the participants had never
viewed the environment from the novel start locations, the entire
environment could be viewed from each initial training perspective. This
aspect presents similar concerns as outlined previously with the Jacobs
et al. (1997) study. Thus, we suggest a future approach that would
address this concern would be to adopt a methodology similar to that of
Hamilton et al. (2002) discussed earlier in this review. Limiting the
amount of environmental information available to the participants from
each training position would allow for subsequent novel tests which
would allow for a more robust examination of viewpoint dependency.
A second transformation test performed by Kelly and
Bischof (2005), however, does provide more support for the cognitive
mapping theory while questioning some of the assumptions of an
associative learning hypothesis to place learning. Half of the
participants in this study were trained with both featural and geometric
cues available. The participants readily learned to use the featural
cues to locate the goal; this was evident in that the participants
directed the vast majority of their searches to the single rewarded
corner. (Geometric cues alone would only allow the participants to
differentiate the two geometrically correct corners from the two
geometrically incorrect corners.) After training, the researchers
presented the participants with a transformation test in which all of
the distinctive featural cues were removed. The participants showed
accurate performance in that they were able to use the geometric cues to
search for the goal, up to rotational errors. Thus, during training the
participants were encoding the featural and geometric information; the
distinctive features were not overshadowing the geometric cues. This
finding fits with the many non-human animal studies that have been
unable to show overshadowing in this type of task (although this is in
contrast to the overshadowing of the featural cues by the geometric cues
in the original studies using this task, i.e., Cheng, 1986). Although
the lack of overshadowing does not in itself refute the associative
learning hypothesis, it raises questions as to why overshadowing has
been found in several other place learning tasks but is difficult to
find in studies using the geometric arena task. Is there something
special about geometric information that is presented from surface cues
that is different from geometric information supplied by discrete
objects located within an environment? Preliminary studies suggest this
might be so.
Neurological Mechanisms: Human-based Research
The RAM, the MWM, and more recently the geometric
arena task have all been used to examine the neurological underpinnings
of spatial navigation in animal-based experimentation, with focus being
on the role of the hippocampus (for a review see Driscoll & Sutherland,
2005; see also Cheng & Newcombe, 2005). As discussed earlier in this
review, the types of tasks used to examine human and non-human spatial
learning have traditionally been quite different. The use of virtual
environments have allowed experimenters interested in human spatial
learning to utilize tasks quite similar to that used for the study of
non-human spatial learning. This has in turn provided an excellent
opportunity for more direct comparative studies of behavior and
neurological processes involved in spatial memory. For example, using an
fMRI approach Astur et al. (2005) found that human participants, similar
to rodent-based studies, show bilateral changes in the hippocampus when
navigating.
The VMWM has also been adopted for studies addressing
neurological questions of spatial navigation. Many investigations of
navigation (and place learning) have shown that the VMWM requires the
use of the hippocampus, as has been clearly demonstrated using the
real-world version in rodents (e.g., VMWM: Hamilton et al., 2002; and
MWM: Morris & Frey, 1997; Morris, Garrud, Rawlins, & O’Keefe, 1982). The
VMWM has also been used as a tool for examining impairments in spatial
ability associated with fetal alcohol syndrome (Hamilton, Kodituwakku,
Sutherland, & Savage, 2003) and aging (Driscoll et al., 2003; Moffat &
Resnick, 2002).
At the time of this article, unfortunately, we are
not aware of any studies adopting a virtual version that replicates a
fully enclosed geometric arena adapted after Cheng (1986) to examine the
neurological underpinning of reorientation. However, recently it has
been suggested that an area in the human brain may be critically
involved in the processing of geometric information – the
parahippocampal place area (PPA). The PPA is an area that straddles the
collateral sulcus near the parahippocampal/lingual boundary in both
hemispheres. Neuroimaging studies show that the PPA responds selectively
to the background or environmental information typically contained
within scenic images and not to objects or featural cues. Activation of
the PPA has been shown for natural and manmade environments. Carefully
controlled studies support the idea that this area is particularly
important for the processing of geometric spatial information (Epstein,
2005). One important example Epstein and colleagues have shown is that
the representations of environmental information stored in the PPA are
viewpoint dependent—a finding that weakens the cognitive mapping theory
of place learning. Although the PPA cannot be considered as an
anatomical equivalent of a module for the processing of geometric
information, these neuroimaging studies provide a better understanding
as to how the properties of an environment may be processed. The use of
a virtual geometric arena will undoubtedly be an important tool for
future research in the area of human spatial learning.
Discussion and Directions for Future Research
Traditionally investigations of non-human and human
spatial abilities have used very different approaches to understanding
the underlying mechanisms of spatial learning. Although these approaches
and associated techniques have provided each respective research area
with valuable information about spatial processes, one important problem
has been the minimal communication between the two disciplines which
share such a common interest.
Studies of non-human spatial abilities have
historically used tasks which require an animal to actively navigate
through an environment in search of a reward or to escape an aversive
experience. We have outlined three important tasks which have played an
important role in the understanding of animal (primarily rodent) spatial
memory: the Morris-type water maze (Morris, 1981), the radial arm maze
(Olton & Samuelson, 1976), and the geometric area (e.g., Cheng, 1986).
Research adopting these tasks (and other important tasks not reviewed
here such as the T-maze) has led to a rich understanding of the
neurobiology, behavior, and cognitive processing of spatial information
by animals, although much is yet to be learned.
Studies of human spatial abilities have historically
used tasks which do not require the participant to actively navigate
through an environment. A couple of classic paradigms used to study
spatial abilities are the Mental Rotations test (Vandenberg & Kuse,
1978) and Object Location Memory test (Eals & Silverman, 1994; Silverman
& Eals, 1992) as well as many different versions of two-dimensional
mapping tasks (e.g., Richardson, Montello, & Hegarty, 1999). Although
research with these tasks have also led to significant strides in
understanding spatial abilities (e.g., behavioral and cognitive changes
influenced by aging, sex differences, or neurodegenerative diseases),
less is known about the neurobiology of human spatial processing in
comparison to that of non-human animals (Driscoll & Sutherland, 2005),
and it is not clear whether these two-dimensional tasks require similar
processing of spatial information as in three-dimensional tasks.
The different types of tasks used by researchers
examining non-human spatial abilities and researchers examining human
spatial abilities has made interdisciplinary or comparative approaches
to understanding spatial processing difficult. However, recent advances
in technology have begun to bridge this divide. Experimental programs
using virtual environments (and virtual realities) have allowed
researchers studying human spatial abilities to adopt modified versions
of animal tasks to study human participants: virtual water mazes,
virtual radial mazes, and virtual geometric arenas. This approach in
combination with advances in neuroimaging techniques have provided
researchers with an opportunity to develop new comparative research
programs centered on the understanding of spatial processing.
The focus of our review was to examine studies of
human spatial learning that have attempted to use virtual versions of
techniques developed for the investigation of non-human spatial
learning. We chose to focus on three techniques prominent to the study
of non-human spatial learning, techniques that have been referred to as
gold standards for investigating animal spatial learning, in particular
the MWM and the RAM. We also included the geometric arena task because
this task has considerably furthered our understanding of the processing
of geometric information as well as generating significant
interdisciplinary research. The framework of our review was to focus on
two major theories of spatial learning: the associative learning
approach and the cognitive mapping theory.
Many recent studies of human spatial learning and
memory have supported the idea that as people move about their
environment they form a spatial representation that is viewpoint
dependent. Our review of human spatial learning which has focused on the
VMWM, VRAM, and the virtual geometric arena show some preliminary
support of this view, but by no means refutes the cognitive mapping
theory. Studies using the VMWM have provided the most robust examination
of these approaches to spatial learning—in particular the research of
Hamilton et al. (2002). We think it is interesting to note that the
impetus of this investigation was a non-human animal study in the MWM.
Furthermore, preliminary studies using the virtual geometric arena show
strong future promise. Although initial investigations have been unable
to rule out the possibility that participants are able to build
viewpoint dependent representations into a cognitive map-like
representation, we would like to argue that future research is needed
with this task. In particular, it is curious that in the geometric arena
task (both real-world and virtual) researchers have predominately been
unable to show overshadowing and blocking of geometric cues by features
(though see Gray, Bloomfield, Ferrey, Spetch, & Sturdy, 2005). In a
similar fashion, we see an increased need to examine the principles of
spatial learning using the VRAM. Although this task has been
particularly important for the study of animal spatial learning, it has
not been used often as a virtual tool for examining human spatial
learning. Yet, the few studies that have been completed raise some
important questions regarding the possibility that this task may call
upon spatial abilities to a different degree than the MWM.
Finally, our review may appear to suggest that the
development of virtual environments have created a unidirectional
advancement and transfer of information from animal-based studies to
human-based studies. Many studies have shown that this is not so. We
would like to point out two interesting examples of the use of virtual
environments in an animal-based approach: the study of pigeons cognitive
processing of directional motion and the study of the looming response
of insects.
Cook, Shaw, and Blaisdell (2001) used a virtual
environment to examine whether pigeons could discriminate whether path
of movement (i.e., the perspective of the camera) towards an object with
a hollowed center was such that the camera would pass through the
object’s center or would move around the object. Interestingly, the
researchers found that the pigeons were able to discriminate, by
responding differently, to the two scenarios. Furthermore, the birds
showed strong transfer to a novel object, as well as disrupted
performance when the frames of the movie were shown out of sequence.
Obviously, such an experiment would be impossible with real objects.
In a program of study, Gray and colleagues have used
a virtual reality approach to examine the neurobiology of the looming
response of insects (Gray, Pawlowski, & Willis, 2002). These studies
have found that the locusts’ (Locusta migratoria L.) response to
looming objects is influenced by not only an object’s particular shape
but also the angle of approach (Guest & Gray, 2006). Furthermore, Gray
has shown that when a visual neuron is habituated to a particular object
with a determined trajectory, changes to either the object properties or
the trajectory result in dishabituation at the neuronal level (Gray,
2005). Using a virtual approach to understanding both the behavioral and
neurobiology of looming responses in this species of locust has allowed
researchers to address questions that would be impossible in a
real-world environment. Thus, although using virtual environments for
animal-based studies requires the consideration of many additional
issues, such as the sensory system of the species being examined, if
used wisely this technique offers many exciting possibilities.
The last decade has witnessed an important shift in
the examination of human spatial memory through the use of virtual
environments to replicate well studied animal paradigms. In our review,
we argue that the adoption of a comparative perspective has begun to
enhance our understanding of the properties underlying spatial learning.
We have outlined specific areas that we suggest to be important for
future areas of research using both human and non-human research
paradigms. We anxiously await the next decade to see how the techniques
outlined in our review and other technological developments will
continue to inform current theories of spatial learning and more
generally influence the field of comparative cognition.
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