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The Journal of Neuroscience, April 1, 2002, 22(7):2711-2717
Neural Correlates of Encoding Space from Route and Survey
Perspectives
Amy L.
Shelton and
John D. E.
Gabrieli
Department of Psychology, Stanford University, Stanford, California
94305
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ABSTRACT |
The neural mechanisms underlying ground-level spatial navigation
have been investigated, but little is known about other kinds of
spatial navigation. Functional magnetic resonance imaging was used to identify differences in brain activation for two types of
spatial information, information from the ground-level perspective (route) and information from a global perspective (survey).
Participants were scanned during the encoding of two different virtual
reality environments, one from each perspective. Comparisons of brain activation during route and survey encoding suggested that both types
of information recruited a common network of brain areas, but with
important differences. Survey encoding recruited a subset of areas
recruited by route encoding, but with greater activation in some areas,
including inferior temporal cortex and posterior superior parietal
cortex. Route encoding, in contrast, recruited regions that were not
activated by survey encoding, including medial temporal lobe
structures, anterior superior parietal cortex, and postcentral gyrus.
These differences in brain activation are associated with differences
in memory performance for the two types of spatial information and
contribute to specification of brain components of spatial knowledge.
Key words:
spatial representation; navigation; memory; medial
temporal lobe; parietal cortex; functional MRI
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INTRODUCTION |
Successful navigation requires
learning the spatial layout of the environment. Understanding how the
brain acquires spatial knowledge has been explored in humans (Aguirre
and D'Esposito, 1997 ; Ghaëm et al., 1997; Maguire et al., 1997;
Aguirre et al., 1998; Epstein and Kanwisher, 1998), nonhuman primates
(Ono et al., 1993; Rolls, 1999), and rats (McNaughton et al., 1996;
Cooper and Mizumori, 2001). The result has been the identification of a
network of brain areas for processing spatial information, including parahippocampal cortex, hippocampus, posterior cingulate, precuneus, retrosplenial cortex, and premotor cortex. In rats and primates, much
of the focus has been on brain processes underlying spatial navigation.
Human neuroimaging and patient studies have focused, more specifically,
on how spatial information (i.e., relationships among objects) differs
from other types of visual information (i.e., the appearance of
individual objects). However, little is known about how different types
of spatial information are encoded and represented in the brain.
Evidence for a fundamental distinction between two types of spatial
information, route and survey knowledge, comes from behavioral analysis
of human spatial cognition (Siegel and White, 1975; Perrig and Kintsch,
1985; Tversky, 1991). Route-based knowledge is characterized as
knowledge of spatial layout from the perspective of a ground-level observer navigating the environment. In contrast, survey knowledge is
characterized by an external perspective, such as an aerial or map-like
view, allowing direct access to the global spatial layout. Both
perspectives afford information about spatial layout, but they can have
different behavioral consequences (Thorndyke and Hayes-Roth, 1982;
Streeter et al., 1985). For example, after participants studied virtual
environments from either route or survey perspectives, recognition
memory for scenes was superior when the environment was studied and
tested in the same perspective. Facilitation was equivalent for route
and survey learning, suggesting different mental representations for
the two types of spatial knowledge (A. L. Shelton and T. P. McNamara, unpublished observations).
The present study was designed to use functional magnetic resonance
imaging (fMRI) to explore how route and survey knowledge are encoded in
the brain. Previous neuroimaging work has used navigation as the
primary source of spatial information, reflecting primarily the route
perspective. Mellet et al. (2000) studied brain activation during
mental imagery after route and survey learning. There was right
hippocampus activation for both route and survey imagery, but bilateral
activation of parahippocampal gyrus for route imagery exclusively.
However, the study relied on imagining previously learned environments
and not the encoding of novel environments. In the present study,
participants were scanned while learning two different large-scale
virtual environments, one from the route perspective and one from the
survey perspective. We hypothesized that different perspectives would
lead to differences in brain activation. Specifically, we were
interested in whether the two types of spatial information are
distinguished in the brain by recruitment of different areas or
differential activation within the same spatial processing network.
Symmetrical dissociation between route and survey encoding in the brain
would support the perspective-specific facilitation observed in memory retrieval.
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MATERIALS AND METHODS |
Participants. Twelve healthy, right-handed volunteers
(six females, six males; mean age, 23.1 years) participated in return for monetary compensation. All participants gave informed written consent.
Experimental task. Three novel environments (convention
center, city park, and market place) were constructed in desktop
virtual reality using Virtus WalkThrough Pro (Fig.
1, bottom panel). Each environment measured 110 × 130 feet (330 × 390 m) in
virtual space and contained 10 large landmarks and 7 small landmarks in
addition to fixed features such as external walls and sidewalks.
Environments were designed to be visually distinct from each other,
with no overlap of landmarks. Two navigation movies were recorded for each environment, one from the ground-level perspective (route movie;
Fig. 1, left panel) and one from an aerial
perspective (survey movie; Fig. 1, right panel). The
route movie was recorded from the perspective of a 6-foot-tall observer
walking through the environment. The route began at the entrance to the
environment (always in the southwest corner) and consisted of four
route legs joined by turns (walk north, turn right, walk east, turn
right, walk south, turn right, walk west, turn left to face entrance again). The survey movie was taken from the perspective of an aerial
observer (70 feet above ground level in virtual space), looking
straight down with 20% of the environment visible at any moment. The
path began in the southwest corner, panned north, east, south, and west
without any changes in heading. The different natures of the two
perspectives did not allow for equating the objects visible from frame
to frame, but the number of exposures to landmarks was kept similar by
following the same paths in both perspectives. One complete run of each
movie lasted 46 sec, and the same movie was used for a given
environment throughout the encoding. Each participant was assigned to
learn two environments, one as a route and one as a survey. Pairs of
environments (e.g., convention center-market, convention center-park,
and market-park) were used four times each across participants. Each
environment appeared as a route or survey movie equally often across
participants. Before scanning, participants viewed each movie one time
to familiarize them with the virtual reality presentation. During this
initial viewing, the landmarks were identified by the experimenter in the order they were encountered. Participants were informed that they
did not need to recall the landmark names. During the scan, each movie
served as a 46 sec block. Route and survey movies were repeated six
times along with six, 24 sec blocks of fixation. During fixation
blocks, participants were instructed to fixate on a white cross in the
middle of the black screen. Block order was determined by using all
possible orders of conditions (route, survey, and fixation) across the
six repetitions, with the constraint that there be no direct
repetitions of any condition. Two different orders were used to
counterbalance which environment was seen first (route or survey).
Participants were asked to learn each environment as well as possible
for a later memory test. They were also instructed to use the fixation
periods to rest.
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Figure 1.
Still images of one of the virtual environments
(convention center) shown from the route (top left
panel) and survey (top right
panel) perspectives. Bottom panel shows
entire convention center layout.
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To assess whether environments were learned, participants were given
recognition memory tests for images of each environment. Participants
saw still images of each environment from the route and survey
perspectives and had to indicate whether the image was from the correct
environment or from a distractor environment. Distractors were created
by randomly rearranging the same landmarks within the environment.
Participants judged a total of 64 correct images and 64 distractors for
each environment. After the entire session, participants were also
asked to draw a map of each environment from memory.
Procedure. The scan session began with anatomical scans
followed by the functional scan for encoding the environment (12 min). [Additional functional scans were acquired during recognition of each
environment, but the data will not be discussed here.]
FMRI data acquisition and analysis. Whole-brain imaging data
were acquired on a 3 Tesla MRI Signa LX Horizon Echospeed (General Electric Medical Systems, 8.2.5 system revisions). Three-dimensional (3-D) high-resolution T1-weighted spoiled gradient echo
anatomical images were acquired in 124 contiguous 1.5 mm slides
[minimum full echo time (TE); 30° flip angle; 24 cm field of view].
T2-weighted spin-echo anatomical images were acquired in 29 contiguous
6 mm coronal slices [30 msec TE; 4000 msec reaction time
(TR)]. Functional images were acquired in the same slices using
T2*-sensitive gradient echo spiral pulse sequence (Glover and Lai,
1998) (30 msec TE; 2000 msec TR; 76° flip angle; 20 cm field of view;
64 × 64 acquisition matrix). Head motion was minimized with a
bitebar using the participant's dental impression.
Using SPM99 (Wellcome Department of Cognitive Neurology, London, UK),
the data for each participant were corrected for motion, normalized to
a T2-weighted template image, and smoothed with a smoothing kernel of 8 mm. Individual models were calculated for each participant using a
general linear model (Friston et al., 1995). Contrast images from each
participant's model were subjected to random effects analyses (Holmes
and Friston, 1998) for all of the effects of interest. Areas of
statistical significance were identified using a height and extent
threshold of p < 0.05 corrected for the number of comparisons.
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RESULTS |
Behavioral data
To verify that participants learned both environments, we compared
the speed and accuracy of recognition for the route environment and the
survey environment. Response latencies did not differ for route (mean,
1777 msec; SD, 56.6) and survey (mean, 1743 msec; SD, 54.3),
t(11) = 1.66; p = 0.13. Accuracy (hits  false alarms) did not differ either (for
route, mean, 78%; SD, 7.3; for survey, mean, 82%; SD, 6.0),
t(11) = 1.36; p = 0.20. The degree of distortion in the sketch maps was measured using
bidimensional regression and a distortion index (Waterman and Gordon,
1984). Distortion indices for route (mean, 20.13%; SD, 1.55) and
survey (mean, 19.47%; SD, 1.51) encoding were not significantly
different, t(11) = 0.94; p = 0.37. Map drawing was further evaluated according
to whether the participant used a sequential drawing strategy (drawing
in the landmarks in the order they were encountered during the movie), a hierarchical drawing strategy (drawing the central then peripheral features and/or filling in spatial quadrants), or a random strategy (no
clear pattern of placement). All 12 participants drew maps of the route
environment sequentially, whereas 11 of the 12 participants drew the
survey environment in a hierarchical manner (entered the central
features then filled in spatial quadrants out of sequence). The
remaining participant drew the survey map by first drawing the central
features then proceeding sequentially around the periphery. These
drawing strategy differences occurred in the same person following
different types of encoding, suggesting differences in underlying
representations after route and survey encoding.
Imaging data
Comparison with fixation
The route and survey blocks were first compared individually
against fixation (Tables 1,
2). Many areas of activation were revealed, including large portions of the dorsal and ventral visual processing streams and frontal cortex. Activation for fixation was
greater than either route or survey encoding only in the insular cortex.
Effects of encoding type
To assess differences between route and survey encoding, these
conditions were contrasted directly. There was greater activation for
route encoding in bilateral medial temporal lobes (MTL) incorporating parahippocampal cortex and posterior hippocampus, bilateral postcentral gyrus (BA 5 and 7), right superior parietal cortex (BA 7), bilateral posterior cingulate and precuneus (BA 31), right inferior parietal cortex (BA 40), left cuneus and middle occipital gyrus (BA 18), right
superior temporal/insular cortex (BA 22 and 13), and left medial
frontal gyrus (BA 6) (Fig. 2, left
panel, Table 3). There was greater
activation for survey encoding in bilateral fusiform and inferior
temporal gyri (BA 37, 19), bilateral superior parietal cortex (BA 7, posterior to the region observed for route > survey), left
insula/clautrum (BA 13), and left superior frontal gyrus (BA 8) (Fig.
2, right panel, Table 3).
To determine whether these areas were exclusive to the particular type
of encoding or were shared areas that showed differential activation,
functional regions of interest (ROI) were analyzed. Percentage of
signal change was calculated for route and survey encoding relative to
fixation. These signal change values were compared with a signal change
of zero using one-sample t tests. (For areas that showed
bilateral activation, laterality was also analyzed using ANOVA;
however, no differences reached statistical significance.)
Figure 3 shows percentage of signal
change relative to fixation for the ROIs that were more active for
route encoding. Given that these were functionally defined ROIs, we
expected all regions to show activation for route encoding. The key
question was whether the survey encoding was also activating these
areas. Several clusters did not show activation for survey encoding,
including: postcentral gyrus (BA 5,7),
t(11) = 0.32, p = 0.75, and t(11) = 1.66, p = 0.13, for left and right clusters, respectively;
MTL, t(11) = 1.78, p = 0.10, and t(11) = 0.58, p = 0.57, for left and right clusters, respectively;
left posterior cingulate (BA 31),
t(11) = 0.37; and left medial frontal
gyrus (BA 6), t(11) = 0.74, p = 0.47. For the right posterior cingulate, there was
marginal activation for survey encoding,
t(11) = 1.99, p = 0.07. The right precuneus and the cuneus/middle occipital gyrus
cluster were both activated during survey encoding,
t(11) = 4.83, p = 0.0005, and t(11) = 10.26, p = 0.0001, respectively. Finally, the right insular cortex (BA 13) and the right inferior parietal cortex showed
significant deactivation for survey encoding,
t(11) = 3.07, p = 0.01 and t(11) = 3.22,
p = 0.008, respectively. Therefore, all of the areas present in the direct comparison map (Fig. 2), except for small
clusters in the parietal and occipital cortex, were activated exclusively by the route encoding.
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Figure 3.
Mean percentage of signal change as a function of
encoding type for clusters that had greater activation for route
encoding than for survey encoding. Error bars reflect ±1
SEM.
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Figure 4 shows percentage of signal
change relative to fixation for the ROIs that were more active for
survey encoding. As shown, all of these clusters show significant
activation for both route and survey encoding, all
t(11) > 5, p < 0.001, except for the left insula (BA 13) for which neither encoding
condition showed significant activation, both
t(11) < 1, and left superior frontal gyrus (BA 8), which showed significant deactivation for route encoding,
t(11) = 3.53, p = 0.005, and was not different from fixation for survey,
t(11) = 1.39, p = 0.19. These results suggest that there were no areas of activation
exclusive to the survey encoding.
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Figure 4.
Mean percentage of signal change as a function of
encoding type for clusters that had greater activation for survey
encoding. Error bars reflect ±1 SEM.
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DISCUSSION |
The aim of this study was to determine how encoding route and
survey information differed in the brain. Route versus survey encoding
led to different mental representations of the spatial environments.
Route encoding resulted in sequentially drawn maps, whereas survey
encoding resulted in hierarchically drawn maps. Both types of encoding,
however, yielded nearly equivalent accuracy by recognition and
map-drawing measures. As such, differences in brain activation cannot
be attributed to encoding success or failure; rather they reflect
differences in the underlying processing. Route and survey encoding led
to different patterns of brain activation. These differences, however,
did not reflect separate neural systems, but rather differential
activation within the same spatial learning system. Specifically,
survey encoding appeared to recruit a subset of brain areas recruited
by route encoding.
Many brain regions broadly associated with spatial navigation
participated exclusively in route encoding. Route, but not survey, encoding activated bilateral MTL, postcentral gyrus, right posterior cingulate, and left medial frontal gyrus. The spatial processes mediated by these structures appear to be unnecessary for effective survey encoding. Rather, survey encoding resulted in greater activation than route encoding in a number of areas also active for route encoding, including bilateral fusiform and inferior temporal gyri and
posterior superior parietal cortex. Heightened activation of a
specialized subset of regions by survey encoding suggests that the
survey-based learning system may develop as a secondary system within a
larger route-learning system. Developmental studies support this
hierarchical relationship between route and survey knowledge,
demonstrating that children develop route learning before survey
learning (Siegel and White, 1975).
Previous imaging studies, which have not distinguished between route
and survey knowledge, have typically probed route learning or its
consequences. In some cases, participants were explicitly instructed to
explore an environment at the route level. Such exploration and its
subsequent memory activated a number of the same areas found in the
route, but not survey, condition of the present study, including
parahippocampal cortex, precuneus, and posterior cingulate (Aguirre et
al., 1998). When London taxi drivers recalled familiar routes, there
was greater activation of the right hippocampus, bilateral
parahippocampal cortex, and bilateral precuneus (Maguire et al., 1997).
Parahippocampal cortex has also been associated with memory for scenes
(Epstein and Kanwisher, 1998). Although they do not explicitly require
navigation, these scenes are presented as route-perspective images.
Two important differences between route and survey encoding may be
critical to the observed brain differences. First, route perspectives
facilitate a sense of immersion relative to survey perspectives.
Participants in the present study reported feeling as if they had
actually been in the environment for route encoding, suggesting that
they perceived the route as their local environment during encoding. No
such experience was reported for survey encoding; instead participants
described the condition as a map, suggesting that the survey
perspective did not invoke a sense of immersion. Differences in the
parietal cortex for route and survey encoding may be related to this
immersion difference. In studies of near (peripersonal) and far
(extrapersonal) space, dorsal visuomotor regions of parietal and
premotor cortices have been associated with experiences and deficits in
peripersonal space (Halligan and Marshall, 1991; Weiss et al., 2000).
In the present study, route encoding likely invoked learning in both
peripersonal and extrapersonal space, whereas survey encoding may have
been limited to extrapersonal space.
A second property distinguishing route and survey perspectives is the
form of updating involved. To learn the spatial layout from a route
perspective, the observer must continuously update changes in the local
environment based on movements through and turns within the space. As
one turns a corner, for example, the association between two very
different views must be established. Thus, global spatial structure
must be constructed from a series of views with only limited visual
information available at any given moment. MTL and parietal areas
identified for spatial navigation may be responsible for binding
together the different aspects of local environment as it changes.
Conversely, survey encoding allows more direct access to the global
structures of the environment and requires updating that is continuous
with this global structure rather than relative to bodily orientation
in space. For example, the extent of a wall and its relationship to
adjacent walls is readily available from the visual information. The
presence of continuous global structure in a survey perspective may
induce participants to treat the environment more like a map rather
than a local, navigable environment.
These psychological differences between route and survey information
may provide clues to interpreting brain regions that were more active
for survey than for route encoding. Although there were no
survey-specific activations, greater survey than route activation was
observed in inferior temporal cortex and posterior superior parietal
cortex. The temporal activation may reflect greater object processing
(Tanaka et al., 1991) because of the map-like nature of the survey
encoding. In addition to providing spatial information, maps can be
treated as individual physical objects themselves. Perhaps participants
were recruiting more effort from object areas to maintain a
representation of the environment as an object itself. Greater
activation in posterior superior parietal cortex for survey encoding
may also reflect the tendency to treat the survey environment as an
object. Parietal areas have been associated with spatial attention and
mental rotation of objects (Cohen et al., 1996; Alvisatos and Petrides,
1997). Perhaps increased activation for survey encoding may have
resulted from greater attention to global properties and the use of
those properties to build a complete representation of the environment as a map. Although both route and survey encoding required attention to
the spatial configuration, the way in which these spatial relations were gleaned likely involved different reference systems (Shelton and
McNamara, 2001).
Robust MTL activation for route encoding may reflect the need to update
one's local environment as one moves through space. This MTL
activation may be related to ideas about hippocampal participation in
spatial memory. The hippocampus has been described as supporting
"cognitive maps" (O'Keefe and Nadel, 1978); greater activation for
route encoding may reflect greater demand on the "map-building"
properties of this region. Subsequent research, however, has
demonstrated that rat hippocampal neurons encode both spatial and
nonspatial aspects of an experience (Wood et al., 1999), suggesting
that hippocampus is more generally involved in binding different
features of an episode together in a hippocampal "memory space"
(Eichenbaum et al., 1999). In the present study, route encoding
required participants to link steps in a sequence that had a beginning,
middle, and end. The entire layout of the space could only be extracted
if successive steps were bound together appropriately. As seen in the
map-drawing strategies, the sequence was preserved after route
encoding, but had little importance after survey encoding. This
difference may have lead to different mnemonic demands, thus producing
differential activation of MTL structures.
Differences between the perspectives provide plausible explanations for
route encoding yielding greater MTL activation than survey encoding. It
is unclear, however, why survey encoding failed to activate MTL, given
the equivalent memory performance in the two conditions. MTL activation
occurs during the encoding of novel stimuli (Gabrieli et al., 1997;
Brewer et al., 1998), and bilateral damage to MTL structures yields a
global deficit for remembering new material that likely extends to
survey spatial knowledge (Scoville and Milner, 1957; Squire, 1992).
Failure to measure MTL activation for survey encoding may be related to
the fixation baseline. Comparisons of baseline tasks suggest that MTL
activation is greater during fixation than during other baseline tasks
(Stark and Squire, 2001). Moreover, there was a trend for left MTL
activation in survey encoding (p = 0.1). Thus,
it is likely that the MTL structures participate at some level during
both types of spatial encoding. The present results, however, indicate
greater MTL participation in route learning.
Spatial information has often been treated as a unitary type of
information. Numerous behavioral studies, however, have established a
fundamental distinction between route and survey spatial information (Siegel and White, 1975; Perrig and Kintsch, 1985; Tversky, 1991). Behavioral performance has suggested that route and survey information rely on different neural systems (Shelton and McNamara, unpublished observations) (Thorndyke and Hayes-Roth, 1982). Our results provide initial evidence to the contrary. Route and survey encoding recruited a
common spatial learning system in the brain, with survey encoding recruiting a specialized subset of route-encoding areas. Notably, these
are not the same brain areas previously associated with spatial
navigation. These results provide new insights into the role of these
regions, suggesting that the degree to which they are responsible for
"spatial processing" may depend on the particular type of spatial
processing involved. To our knowledge, the present study was the first
to explore route and survey distinctions in the brain during encoding
of novel environments. The brain bases of other types of spatial
knowledge, including tactile or text-based, remain to be investigated.
Understanding how spatial information, in its many forms, is
represented in the brain provides new approaches for linking encoding
processes to their behavioral consequences.
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FOOTNOTES |
Received Oct. 9, 2001; revised Dec. 11, 2001; accepted Dec. 27, 2001.
This work was supported by National Institutes of Health Postdoctoral
Training Grant MH12638 and Raytheon Systems Corporation. We thank
Jennifer Burrows, Barbara Tversky, Timothy P. McNamara, John Desmond,
Gary Glover, Silvia Bunge, Joanna Salidis, and two anonymous reviewers
for their contributions.
Correspondence should be addressed to Amy Lynne Shelton at
her present address: Department of Psychological and Brain Sciences, Johns Hopkins University, Ames Hall, 3400 North Charles Street, Baltimore, MD 21218. E-mail: ashelton{at}jhu.edu.
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ABSTRACT
INTRODUCTION
