Knowing Where and Getting There: A Human Navigation Network

Abstract

The neural basis of navigation by humans was investigated with functional neuroimaging of brain activity during navigation
in a familiar, yet complex virtual reality town. Activation of the right hippocampus was strongly associated with knowing
accurately where places were located and navigating accurately between them. Getting to those places quickly was strongly
associated with activation of the right caudate nucleus. These two right-side brain structures function in the context of
associated activity in right inferior parietal and bilateral medial parietal regions that support egocentric movement through
the virtual town, and activity in other left-side regions (hippocampus, frontal cortex) probably involved in nonspatial aspects
of navigation. These findings outline a network of brain areas that support navigation in humans and link the functions of
these regions to physiological observations in other mammals.

Full text

electrode in the flowing bath solution, which were
connected to a GeneClamp 500 amplifier (Axon
Instruments, Foster City, CA) interfaced with a
computer. A drop of water at 0°C, which was add-
ed to the recording chamber at the end of each
experiment, produced the expected large depolar-
ization mediated by Ca
21
and Cl
2
channels [B. D.
Lewis, C. Karlin-Neumann, R. W. Davis, E. P. Spal-
ding, Plant Physiol. 114, 1327 (1997)], a test of
proper cell impalement.
15. D. J. Walker, R. A. Leigh, A. J. Miller, Proc. Natl.
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16. The shunt resistance between an inserted electrode
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143, 267 (1978)], the effect of which may be to un-
derestimate V
m
by 20 to 50 mV [W. Gassmann and
J. I. Schroeder, Plant Physiol. 105, 1399 (1994)].
Corrections of that magnitude would shift each of
our V
m
measurements, the most positive of which
was 2193 mV, to values more negative than 2230
mV.
17. Plants were grown vertically on Petri plates contain-
ing 1.5% (w/v) agar, 1 mM KCl, and 1 mM CaCl
2
in
continuous light for 4 to 5 days. Root protoplasts
were prepared by cutting the root about 150 mm
from the tip with a micromanipulator-mounted razor.
The cut seedlings were infiltrated with an enzyme
solution containing Cellulysin (12 mg/ml) (Calbio-
chem), Pectinase (2 mg/ml) (Sigma), and bovine se-
rum albumin (5 mg/ml) (Sigma) dissolved in 10 mM
KCl, 1 mM CaCl
2
, 5 mM MES, and 300 mM sorbitol
{pH 5.2 with 1,3-bis[tris(hydroxymethyl)methyl-
amino]propane (BTP)} using a vacuum produced by
a faucet aspirator. After 2 hours of incubation, the
seedlings were rinsed in the solution without en-
zymes and stored at 4°C or placed in a 500- ml
recording chamber containing 10 mM CaCl
2
,30mM
KCl, 5 mM Hepes, and 120 to180 mM sorbitol (pH
7.0 with BTP). Protoplasts of nonepidermal cells
emerged from the cut end of the otherwise undigest-
ed root. Patch pipettes were filled with 130 mM K-
glutamate, 2 mM EGTA, 5 mM Hepes, and 4 mM
Mg–adenosine triphosphate (Mg-ATP) (pH 7.0 with
BTP). Patch-clamp equipment and procedures were
as described [M. H. Cho and E. P. Spalding Proc.
Natl. Acad. Sci. U.S.A. 93, 8134 (1996)]. Our proce-
dures led to observations of inward currents in all
patch-clamped wild-type cells. Lower percentages
reported by others (5) may be explained by different
growth conditions, different voltage protocols, or use
of different tissue. We encountered more variability in
the voltage dependence of the inward currents than
has been reported for heterologously expressed in-
ward rectifiers [F. Gaymard et al., J. Biol. Chem. 271,
22863 (1996)]. This variability is responsible for the
appearance of weak rectification in the averaged
whole-cell I-V curve in Fig. 3E. Channel gating may
have been affected by cytoplasmic components that
washed out in some patch-clamped protoplasts.
18. B. D. Lewis and E. P. Spalding, data not shown.
19. Patch pipettes were filled with 390 mM K-gluta-
mate, 2 mM EGTA, 5 mM Hepes, and 4 mM Mg-
ATP (pH 7.0 with BTP). The bath contained 130
mM KCl, 10 mM CaCl
2
, and 5 mM Hepes (pH 7.0
with BTP). Tail currents were evoked by clamping
V
m
at 2200 mV before stepping to a series of more
positive potentials.
20. Plants were grown in continuous light on media con-
taining KCl at the concentrations indicated plus
0.8% (w/v) agarose (Bio-Rad, Hercules, CA), 0.5%
(w/v) sucrose, 2.5 mM NaNO
3
, 2.5 mM CaNO
3
,2
mM NH
4
H
2
PO
4
, 2 mM MgSO
4
, 0.1 mM FeNaEDTA,
25 mM CaCl
2
,25mMH
3
BO
3
,2mM ZnSO
4
,2mM
MnSO
4
, 0.5 mM CuSO
4
, 0.2 mMNa
2
MoO
4
, and 0.01
mM CoCl
2
, adjusted to pH 5.7 with NaOH.
21. Of 104 seedlings tested, 23 were phenotypically
scored as mutant and 88 were scored as wild type,
giving x
2
5 0.346 (based on the expected ratio of
three wild type to one mutant); P . 0.05.
22. Wild-type and akt1-1 plants were grown on agar
containing MS salts and 5% (w/v) sucrose in con-
stant light for 14 days. Roots were excised, weighed,
and washed for 16 to 17 hours in a K
1
-free desorp-
tion solution (DS) containing 1.5 mM CaCl
2
adjusted
to pH 5.7 with CaOH. Roots were incubated for 10
min in DS containing 4 mM NH
4
Cl and 10 mM, 100
mM, or 1 mM Rb(
86
Rb)Cl and then washed two times
for 10 min each in ice-cold DS. Radioactivity was
measured by detection of Cerenkov radiation.
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Rev. Biophys. Biomol. Struct. 23, 441 (1994); W.
Gassmann, J. M. Ward, J. I. Schroeder, Plant Cell 5,
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W. A. Jackson, Planta 173, 424 (1988).
25. Supported by the Department of Energy (DOE)/NSF/
USDA Collaborative Research in Plant Biology Pro-
gram (BIR-9220331), funds to R.E.H. from the NIH
University of Wisconsin Cellular and Molecular Biology
Training Grant (GM07215), to M.R.S. from DOE (DE-
FG02-88ER13938) and NSF (DCB-90-04068), and to
E.P.S. from the NASA/NSF Network for Research on
Plant Sensory Systems (IBN-9416016). We thank T.
Janczewski and R. Meister for technical assistance
and P. Krysan, W. Robertson, J. Satterlee, and J.
Young for helpful comments on the manuscript.
26 November 1997; accepted 27 March 1998
Knowing Where and Getting There: A Human
Navigation Network
Eleanor A. Maguire,* Neil Burgess, James G. Donnett,
Richard S. J. Frackowiak, Christopher D. Frith, John O’Keefe
The neural basis of navigation by humans was investigated with functional neuroimaging
of brain activity during navigation in a familiar, yet complex virtual reality town. Activation
of the right hippocampus was strongly associated with knowing accurately where places
were located and navigating accurately between them. Getting to those places quickly
was strongly associated with activation of the right caudate nucleus. These two right-side
brain structures function in the context of associated activity in right inferior parietal and
bilateral medial parietal regions that support egocentric movement through the virtual
town, and activity in other left-side regions (hippocampus, frontal cortex) probably
involved in nonspatial aspects of navigation. These findings outline a network of brain
areas that support navigation in humans and link the functions of these regions to
physiological observations in other mammals.
Where am I? Where are other places in the
environment? How do I get there? Ques-
tions such as these reflect the essential
functions of a navigation system. The neu-
ral basis of way-finding activity has been
extensively studied. Spatially tuned neurons
found in the hippocampal formation of free-
ly moving rats [place cells coding for the
rat’s location (1) and head direction cells
coding for its orientation (2)] support the
idea that this part of the brain provides an
allocentric (world-centered) representation
of locations, or cognitive map (3). The
posterior parietal lobe has been implicated
in providing complementary egocentric
representations of locations (centered on
parts of the body) (4). Other brain regions,
such as the dorsal striatum (5), have also
been identified as possible elements of a
navigation system. In humans, there has
been much evidence for the involvement of
the hippocampus in episodic memory, the
memory for events set in their spatio-tem-
poral context (3, 6). By contrast, the role of
the hippocampus in human navigation has
remained controversial, and the wider neu-
ral network supporting human navigation is
even less well understood. We attack this
issue by combining functional neuroimag-
ing with a quantitative characterization of
human navigation within a complex virtual
reality environment.
We used positron emission tomography
(PET) (7) to scan subjects while they nav-
igated to locations in a familiar virtual re-
ality town using their internal representa-
tion of the town built up during a contin-
uous period of exploration immediately be-
fore scanning (Fig. 1A). In one navigation
condition, the subjects could head directly
toward the goal (nav1), while in the other
(nav2), direct routes were precluded by
closing some of the doors and placing a
barrier to block one of the roads, forcing the
subjects to take detours. Navigation was
compared to a task in which subjects moved
through the town following a trail of arrows,
thus not needing to refer to an internal
representation of the town. An additional
task requiring the identification of features
in static scenes from the town was included
for contrast with the three dynamic tasks
(8).
We first investigated which brain re-
gions were involved in successful naviga-
E. A. Maguire, R. S. J. Frackowiak, C. D. Frith, Wellcome
Department of Cognitive Neurology, Institute of Neurolo-
gy, University College London, 12 Queen Square, Lon-
don WC1N 3BG, UK.
N. Burgess, J. G. Donnett, J. O’Keefe, Department of
Anatomy and Developmental Biology and Institute of
Cognitive Neuroscience, University College London,
Gower Street, London WC1E 6BT, UK.
*To whom correspondence should be addressed. E-mail:
e.maguire@fil.ion.ucl.ac.uk
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tion in both direct and detour way-finding.
The trials in these conditions were divided
into successful ones in which the correct
destination was reached (22/30 trials in
nav1 and 21/30 in nav2 across the 10 sub-
jects) and those in which it was not. The
successful trials compared to the arrows task
showed significant activation of the right
hippocampus (Fig. 1B), as did the compar-
ison of the successful trials with the unsuc-
cessful trials. This latter comparison also
revealed activation in the left hippocampus,
left lateral temporal cortex, left frontal cor-
tex, and in the thalamus (Fig. 1B).
In order to explore in greater depth the
relationship between regional cerebral
blood flow (rCBF) and behavior during di-
rect way-finding (nav1), we derived a quan-
titative measure of the accuracy of heading
toward the goal (9). The accuracy of head-
ing measured across all trials in nav1 covar-
ied significantly with rCBF in the right
hippocampus and the right inferior parietal
cortex (Fig. 2). Not only is the right hip-
pocampus more active during navigation
than trail-following, but the more accurate
the navigation, the more active it is.
These results are consistent with our
interpretation that the right hippocampus
and inferior parietal cortex cooperate to
enable navigation to an unseen goal: The
hippocampus provides an allocentric (envi-
ronment-based) representation of space
that allows the computation of the direc-
tion from any start location to any goal
location, and the right inferior parietal cor-
tex uses this information to compute the
correct body turns to enable movement to-
ward the goal given the relative (egocen-
tric, body-centered) location of obstacles in
the way (doorways to go through, barriers
across roads, and so forth) and the current
heading direction. Because the parietal cor-
tex takes account of information in addi-
tion to the allocentric direction to the goal,
it would not have as high a correlation with
the accuracy of heading toward the goal as
the hippocampal formation (consistent
with our findings; Fig. 2). Similarly, rCBF
in the right inferior parietal cortex would
not be significantly different in the trail-
following and way-finding conditions, be-
cause both tasks have similar egocentric
requirements, and no such difference was
found (Fig. 1B). However, differences in
right inferior parietal activity would be ex-
pected when subtracting the static condi-
tion from either trail-following or way-find-
ing. This comparison did indeed show right
inferior parietal activation, along with bi-
lateral activation of medial parietal areas,
which we assume are also involved in ego-
centric aspects of movement, for example,
processing the optic flow generated by the
movement (Fig. 3B) (10).
Activity in the left hippocampus, al-
though associated with successful naviga-
tion, does not covary significantly with our
measure of the accuracy of navigation. We
interpret this as a role in actively maintain-
ing the memory trace of the appropriate
destination during navigation or recollect-
ing specific paths taken during learning that
lead to the goal but are not necessarily
direct. Either role would be consistent with
the known involvement of the left hip-
pocampus in “episodic” memory for person-
ally experienced events (6).
These results are consistent with previ-
ous reports of the involvement of the hip-
pocampal or parietal areas in topographical
memory (11) and provide a more precise
interpretation of their roles in the actual
performance of navigation.
A
B
Fig. 1. (A) Example of view from inside the virtual
town. (B) The comparison between successful
navigation (nav1 and nav2) compared to following
a trail of arrows. PET data are superimposed onto
the averaged MRI of the 10 subjects at the voxel of
peak activation in the right hippocampus dis-
played in the coronal plane. The color scale of the
activation pertains to the significance level of the z
scores with the peak of the activation in white.
Coordinates in stereotactic space (x,y,z, respec-
tively) and z scores of the activations are: right
hippocampus (30, 216, 222; z 5 3.74) and left
tail of caudate (228, 216, 28; z 5 3.05). Other
areas activated in this comparison but not dis-
played on this plane were: left occipital area 18
(224, 2102, 22; z 5 3.50) and left superior fron-
tal gyrus (222, 52, 22; z 5 3.65). The comparison
between successful versus lost trials showed the
following activations: right hippocampus (30,
220, 216; z 5 3.61); left hippocampus (216,
226, 26; z 5 4.10); left superior temporal gyrus
(254, 230, 14; z 5 3.86); left inferior temporal
gyrus (–52, 250, 212; z 5 5.23); left inferior fron-
tal gyrus (–46, 22, 6; z 5 4.59); and right thalamus
(6, 26, 12; z 5 4.25).
AB
C
Fig. 2. (A) The virtual en-
vironment is shown from
an aerial perspective,
demonstrating the com-
plexity of the town and
the many possible paths
between the various
places. Subjects’ navi-
gation during scanning
on the navigation task
was analyzed in terms of
accuracy in degrees (9).
Three trajectories be-
tween the screens at A and B (18 m
apart) are shown from the range of
subjects’ behavior: an accurate tra-
jectory (yellow, accuracy 5 155.3°),
an inaccurate but successful trajec-
tory (green, accuracy 5 127.9°),
and an inaccurate “lost” trajectory
(red, accuracy 5 70.6°). (B) The
PET data from the correlational
analysis of rCBF (22) and accuracy
are superimposed onto the aver-
aged MRI of the 10 subjects at the
voxel of peak activation in the right
hippocampus displayed in the
coronal plane. Areas activated in this comparison were right hippocampus (36, –12, –20; z 5 3.66;
displayed in figure) and right inferior parietal cortex (60, –30, 50; z 5 3.36). (C) Scatter plot of the
correlation of rCBF values at the voxel of peak activation in the right hippocampus plotted against the
accuracy of navigation (r 5 0.56, P , 0.002). The behavioral data for one trial of one subject (subject 6)
was not available. The data points for each subject are plotted in different colors. The correlation of
accuracy of navigation and perfusion in the right inferior parietal cortex was r 5 0.43, P , 0.02.
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Next, we looked at successful navigation
requiring detours (nav2) compared to suc-
cessful navigation in the nav1 condition.
This comparison revealed left frontal acti-
vation (Fig. 3A). The increased require-
ment for strategy switching in the presence
of obstacles (nav2) compared to direct way-
finding (nav1) is consistent with findings of
frontal involvement in other studies with
tasks making similar demands (12). Left
frontal activation was also apparent when
successful navigation was compared to fol-
lowing arrows or compared to unsuccessful
navigation. These frontal activations are
consistent with a role for this region in
planning and decision making (13). It is
likely that following the trail of arrows de-
mands less planning than way-finding.
As well as comparing the dynamic and
static tasks detailed above, we further char-
acterized movement in the town in terms of
the speed of motion, that is, the ratio be-
tween distance traveled and time taken,
producing an average speed measure in vir-
tual meters per second. In contrast to the
areas whose activity correlated with naviga-
tional accuracy, the only area of regional
activation that covaried significantly with
speed of navigation in the nav1 condition
was in the right caudate nucleus; rCBF in
this region increased as speed increased (Fig
3, C and D). This correlation with speed of
virtual navigation was much more signifi-
cant than that with simple motor response
variables such as the rate or average dura-
tion of keypad presses (Fig. 3D). It suggests
a higher function than the simple control of
the physical movement of parts of the body,
although its precise interpretation remains
open. The dorsomedial caudate region re-
ceives projections from the cortices adja-
cent to the hippocampus in rats (14). We
suggest that location within the environ-
ment or spatial context might provide an
important source of information for the stri-
atal control of higher-level aspects of cur-
rent or planned movements and that this
control is reflected in the amplitude or
speed of movement, rather than the direc-
tion of movement.
In conclusion, our results outline the
network of brain regions supporting human
navigation and suggest specific roles for
each of these regions. They agree with, and
further illuminate, previous findings show-
ing that lesions of the right human hip-
pocampus result in deficits of spatial mem-
ory (15) while those of the right inferior
parietal cortex result in deficits of the abil-
ity to represent or act on objects located
with respect to the egocentric left-right
body axis (16). Our interpretation of the
parietal role in navigation agrees with neu-
ronal responses from inferior parietal cortex
in monkeys (in particular, area 7a and the
lateral intraparietal area) implicating it in
the translation of the location of stimuli
from retinal to head- or body-centered co-
ordinates (4), and with the connections of
area 7a to the hippocampal formation [in-
cluding the presubiculum which, at least in
rats, codes for the current head direction
(2)]. Our interpretation of the hippocampal
role in navigation is concordant with neuro-
nal responses in rats (3) and with models of
how the hippocampus guides rats’ navigation
(17), from which our measure of navigation-
al accuracy was explicitly derived. Our find-
ing that rCBF in the right caudate nucleus
correlates with the speed of navigation is
compatible with its proposed role in motor
learning (18) and the process by which
movements are reinforced [and hence, the
occurrence of abulia after lesions of this re-
gion (19)], and also with the more general
hypothesis of a role in context recognition
(20). It also has relevance to the suggestions
that rats may use signals derived from cells
that encode their speed of movement to
determine distances and that such speed cells
might be located in one of the sub-cortical
nuclei (21), perhaps in the basal ganglia as
we identified here. Although many details of
the inputs and outputs of a human naviga-
tion network remain to be specified, we have
demonstrated the closest link yet between
humans and other mammals in the neural
implementation of navigation.
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AB
D
C
Fig. 3. (A) Activation of the left mid-
dle and superior frontal gyri when
successful detour navigation (nav2)
is compared to successful direct
(nav1) navigation, superimposed
onto the averaged MRI of the 10
subjects displayed in the transverse
plane. Areas activated in this com-
parison are: left superior frontal gy-
rus (–10, 66, 8; z 5 3.57; displayed
in figure); left middle frontal gyrus
(–16, 26, 40; z 5 3.57); and right
cerebellum (24, 274, 246; z 5
4.10). (B) The SPM in the coronal
plane associated with comparison
of movement tasks (nav1, nav2, arrows) with the static scenes task. Displayed on a transparent brain to
facilitate viewing of all significant activations that are on different planes. Areas activated: left medial
parietal cortex (216, 252, 54; z 5 4.48); right medial parietal cortex (12, 268, 48; z 5 4.02); right inferior
parietal lobe (56, 238, 32; z 5 6.30); left cerebellum (234, 240, 240; z 5 3.89); and right cerebellum
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movement, displayed here in sagittal section on the averaged MRI of the 10 subjects at the voxel of peak
activation in the right caudate (10, 14, 24; z 5 3.97). (D) Scatter plot of the correlation of rCBF (22)
values at the voxel of peak activation in the right caudate plotted against the speed of navigation (r 5
0.75, P , 0.0001). The behavioral data for one trial of one subject (subject 6) was not available. The data
points for each subject are plotted in different colors. The correlations between right caudate rCBF and
peripheral motor behaviors were much less significant (average duration of button press r 5 0.08, P ,
0.67; rate of button pressing r 5 0.51, P , 0.004).
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36.5 years) participated in the study, approved by
the local hospital ethics committee and the UK Ad-
ministration of Radioactive Substances Advisory
Committee. Each subject had 12 PET scans. There
were four tasks each performed three times in a
counterbalanced order. PET scans were obtained
using a Siemens/CPS ECAT EXACT HR1 (model
962) PET scanner. Scanning was performed with
septa retracted, in three-dimensional (3D) mode. The
field of view of 15.5 cm in the axial extent allowed the
whole brain to be studied. Volunteers received an
H
2
15
O intravenous bolus (330 MBq) infused over
20 s followed by a 20-s saline flush through a forearm
cannula. Data were acquired in a 90-s scan frame.
There were 12 successive administrations of H
2
15
O,
each 8 min apart. The integrated radioactivity counts
that accumulated over the 90-s acquisition period,
corrected for background, were used as an index of
regional cerebral blood flow. Attenuation correction
was computed with a transmission scan before
emission scan acquisition. Images were reconstruct-
ed into 128 pixels by 128 pixels in 63 planes with an
in-plane resolution of 6.5 mm. In addition, high-res-
olution magnetic resonance imaging (MRI) scans
were obtained with a 2.0-T Vision system (Siemens
GmbH, Erlangen, Germany) using a T1-weighted 3D
gradient echo sequence. The image dimensions
were 256 voxels by 256 voxels by 256 voxels. The
voxel size was 1 mm by 1 mm by 2 mm. Images were
analyzed with Statistical Parametric Mapping
(SPM’96, Wellcome Department of Cognitive Neu-
rology, London, UK; www.fil.ion.ucl.ac.uk) executed
in MATLAB (Mathworks, Sherborn, MA). All scans
were automatically realigned to the first scan and
then normalized using a nonlinear deformation [K. J.
Friston et al., Hum. Brain Mapp. 2, 189 (1995)] into
standard stereotactic space [J. Talairach and P.
Tournoux, Co-Planar Stereotactic Atlas of the Hu-
man Brain ( Thieme, Stuttgart, Germany, 1988)] us-
ing a template from the Montreal Neurological Insti-
tute [A. C. Evans et al.,inProceedings of the IEEE-
Nuclear Science Symposium and Medical Imaging
Conference, San Francisco, CA, 31 October to 6
November 1993, L. A. Klaisner, Ed. (IEEE Service
Center, Piscataway, NJ, 1993), pp. 1813–1817]. The
structural MRI scans were normalized into the same
space to allow for the superimposition of PET acti-
vations onto an averaged structural image. Images
were smoothed using an isotropic Gaussian kernel of
16 mm (full width at half maximum) to optimize the
signal-to-noise ratio and to adjust for intersubject
differences in gyral anatomy. Global variance be-
tween conditions was removed, using analysis of
covariance (ANCOVA). For each pixel in stereotactic
space, condition-specific adjusted rCBF values with
an associated adjusted error variance were generat-
ed. Areas of significant change in brain activity were
then determined, using appropriately weighted con-
trasts between the task-specific scans and the t sta-
tistic. The resulting sets of t values constituted the
statistical parametric map (SPM). Significance levels
were set at P , 0.001 (uncorrected).
8. A commercially available computer game (Duke
Nukem 3D, 3D Realms Entertainment, Apogee Soft-
ware Ltd., Garland, TX ) was used to present the
virtual reality town on a 120-MHz Pentium-based
personal computer, showing a color, 3D, fully tex-
tured first-person view. The town was created using
the editor provided (BUILD, Ken Silverman, 3D
Realms Entertainment). The game’s record and play-
back functions were used to store subjects’ actions
and replay them for subsequent analysis. The town
had four streets and contained shops, bars, a cine-
ma, church, bank, train station, and video games
arcade (Fig. 1A). Subjects could enter into and nav-
igate through the buildings, as each room had at
least two entrances. The town contained small
screens on the walls at various locations. Approach-
ing a screen and switching it on caused it to display a
view of another part of the town. Subjects controlled
their movement within the environment by using a
keypad with backward, forward, left turn, and right
turn buttons. A fifth button served to activate
screens. Before scanning acquisition, subjects spent
up to 60 min exploring the environment until they felt
that they had learned the spatial layout of streets and
building interiors. A trail of arrows on the floor was
present during exploration and in all conditions, but
was only relevant in the arrows condition. Subjects
were scanned under four conditions. (i) nav1: sub-
jects switch on a screen and navigate through the
town to the destination displayed. When the destina-
tion is reached, the subject activates the screen
found there, which displays the next destination, and
so on; (ii) nav2: identical to nav1, except that some
doors have been closed, and a barrier has been
moved to block a different street; (iii) arrows: sub-
jects move through the town following a trail of ar-
rows on the floor. Subjects activated the screens
encountered during the task, but the views of the
town displayed had no relevance to their task; (iv)
scenes: static scenes from the town are presented
every 2 s, subjects respond according to whether
there is a screen in the scene or not.
9. For the nav1 condition, the subject’s heading direc-
tion u
h
(in degrees) and the direction of the current
destination u
d
were calculated at each meter along
the subject’s trajectory. The absolute difference u
h
–
u
d
 was found at each meter and was averaged over
a trial to give A 5 180 2^u
h
2u
d
& as a measure of
accuracy of heading. Similar analysis was not ap-
plied to the nav2 detour condition, because subjects
did not know beforehand which doors would be
closed or where the barriers would be, and so could
not be expected to plan an optimal route. Theoreti-
cally, accuracy scores may vary from 0 (always mov-
ing directly away from the current destination),
through 90° (moving randomly) to 180° (always mov-
ing directly toward the current destination). In prac-
tice, an accuracy of 160° is hard to exceed because
of the cluttered nature of the environment (the accu-
racy of one very well-practiced author, N.B., in the
three trials varied between 144.3° and 157.4°). This
measure agrees with our subjective assessment of
trials, was independent of the speed of navigation,
and is consistent with models of how the hippocam-
pus directs navigation in rodents (17).
10. B. M. DeJong, S. Shipp, B. Skidmore, R. S. J. Frac-
kowiak, S. Zeki, Brain 117, 1039 (1994).
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Neuropsychologia 34, 993 (1996); M. L. Smith and
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Philos. Trans. R. Soc. London Ser. B 352, 1397
(1997).
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Univ. Press, Oxford, 1991), pp. 273–295; N. Bur-
gess and J. O’Keefe, Hippocampus 6, 749 (1996).
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proaches to Higher Brain Functions, S. Wise, Ed.
( Wiley, New York, 1987); R. E. Passingham, The
Frontal Lobes and Voluntary Action (Oxford Univ.
Press, Oxford, 1995).
19. K. P. Bhatia and C. D. Marsden, Brain 117, 859
(1994).
20. J. C. Houk and S. P. Wise, Cereb. Cortex 5,95
(1995).
21. J. O’Keefe, N. Burgess, J. Donnett, E. A. Maguire,
Philos. Trans. R. Soc. London Ser. B, in press.
22. C. Buchel, R. S. J. Wise, C. J. Mummery, J. B.
Poline, K. J. Friston, Neuroimage 4, 60 (1996).
23. E.A.M., R.S.J.F., and C.D.F. are supported by the
Wellcome Trust, J.O’K. and J.G.D. by the Medical
Research Council, and N.B. by the Royal Society.
We thank K. Friston, C. Price, and C. Buchel for
helpful comments.
11 December 1997; accepted 20 March 1998
SCIENCE
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