Abstract

Psychological studies have demonstrated sex differences in performance and tactics for route learning. Route information can be encoded in different ways, such as the survey perspective (as in maps) and the route perspective (as we experience the world). Here we show, using functional magnetic resonance imaging, that men and women use the same brain areas to learn routes from both perspectives, and that the observed sex differences in route learning are not due to differences in the parts of the brain being used. We also show that many of the same brain areas are used in route learning from both perspectives, such as the parahippocampus, precuneus, posterior cingulate gyrus and middle frontal gyrus. However, paired comparisons of route learning from both perspectives shows that the survey perspective activates the superior and middle temporal gyri and the angular gyrus, which are not activated in the route perspective.

Introduction

Route learning is a multi-faceted activity and as such it has been shown to activate a distributed network of brain areas (Maguire et al., 1996). Route learning and navigation have generally been studied from the route perspective (Aguirre et al., 1996, 1998; Ghaem et al., 1997; Maguire et al., 1996, 1997, 1998; Maguire, 1997; Grön et al., 2000). Little attention has been given in the functional imaging literature to other perspectives (Mellet et al., 2000, 2002; Shelton and Gabrieli, 2002).

Neuropsychological work has shown that route learning from the survey perspective engenders a qualitatively different internal representation to route learning from the route perspective (Richardson et al., 1999). A recent functional imaging study by Shelton and Gabrieli (2002) showed that many of the same brain areas were used to learn topographical information from the two perspectives, but that the route perspective recruited extra brain areas.

A number of different behavioural paradigms have been applied to the study of the encoding and recall of topographical information. Investigators have used real and virtual environments from the route and survey perspectives. The paradigms used involve variously the learning of routes or of whole maps through passive viewing, navigation through virtual mazes and mental navigation through previously constructed internal representations (Aguirre et al., 1996, 1998; Ghaem et al., 1997; Maguire et al., 1996, 1997, 1998; Maguire, 1997; Grön et al., 2000; Mellet et al., 2000, 2002; Shelton and Gabrieli, 2002). These paradigms have all activated a broadly similar network of brain areas, leading to the conclusion that encoding and recall of topographical information in route form and in map form all activate similar brain areas. One study has looked at the encoding of topographical information from the survey perspective; the experimental task was learning the layout of space. The learning of routes from the survey perspective remains unstudied.

There has been a great deal of investigation in the neuropsychological literature of sex differences in route learning (Miller and Santoni, 1986; Ward et al., 1986; Holding and Holding, 1988; Galea and Kimura, 1993; Caplan and Lipman, 1995; Gibbs and Wilson, 1999). Some of these studies have shown men to perform better than women, whereas some have shown differences in tactics. Specifically, in describing routes and drawing maps men use geometric information, whereas women use landmark information.

Animal studies have shown that frontal lesions impair performance of female rats more than male rats in the Morris water maze whereas male rats were more sensitive to lesions of the entorhinal cortex (Roof et al., 1993; Kolb and Cioe, 1996).

There has, to date, been only one functional imaging study, by Grön et al. (2000), of sex differences for navigation from the route perspective. This showed left parahippocampal, hippocampal and precuneus activation in men that was not present in women and activation of the left medial and right superior frontal gyri and left superior parietal lobule that was present only in women.

We therefore used functional magnetic resonance imaging (fMRI) to identify the parts of the brain used to learn routes from the route and survey perspectives and to identify sex differences for this.

We took 25 subjects (13 men) and conducted two experiments. In the first experiment, subjects were instructed to learn routes from video footage taken from a moving car. In the baseline condition subjects viewed traffic lights from a static camera and were instructed to learn the movements of the cars; the baseline thus includes perception of movement in a visually rich environment and a spatial memory task. The comparison of the two conditions is hoped to isolate the parts of the brain used specifically in route learning.

In the second experiment subjects were instructed to learn routes on pictorial maps. This was compared with the baseline condition in which subjects learnt the orientation of random coloured lines.

Materials and Methods

Subjects

Twenty-five subjects (13 males) gave written informed consent. Subjects were all right handed, between 20 and 25 years old, without a history of neurological or psychiatric disorder.

The study was approved by the South Glasgow University Hospitals NHS Trust Ethics Committee.

fMRI Data Acquisition

A 1.5 T General Electric NVi scanner with a transmit /receive head coil was used. Images were projected onto a translucent screen by an LCD projector linked to a PC laptop. The screen, at the subjects’ feet, was viewed on a mirror set into the head coil.

Changes in BOLD T2* weighted MR signal were measured using a gradient-echo echoplanar (EPI) imaging sequence (TR = 5000 ms, TE = 50 ms, 90° flip angle). Twenty contiguous axial slices, 6 mm apart, covered the whole of the cerebral hemispheres from the bottom of the medial temporal lobes to the top of the frontal lobes. Each slice was acquired six times per condition. Thus 20 × 6 × 8 slices were performed per experiment with an acquisition matrix of 64 × 64 voxels over a field of view of 24 cm.

Image processing was done using Statistical Parametric Mapping (SPM99, Wellcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac.uk) executed in MATLAB 5.3 (MathWorks, Natick, MA). All images were realigned to the first image in the set, using sinc interpolation for reslicing. All images were normalized, using trilinear interpolation, to the Montreal Neurological Institute (MNI152, voxel size 2 × 2 × 2 mm) template and spatially smoothed with a 7.5 × 7.5 × 12 mm Gaussian kernel.

The variance of every voxel was estimated for each session according to the general linear model using a box-car model convoluted with haemodynamic response function as a predictor. A high-pass filter with a cut-off of 120 s was used to remove low frequency confounds. No global signal scaling was used, to prevent spurious activations. This created a contrast file for each subject for each experiment showing the t-value for every voxel. These files were then used for the between subjects analysis.

All group analyses were calculated using a random effects model. Data were analysed across all subjects for the two experiments separately using a one-sample one-tailed t-test. A paired t-test across all subjects was used to compare the two experiments. Males and females were compared for experiments 1 and 2 separately using a two-sample t-test. The P values were set to α = 0.05 corrected for multiple comparisons. The regions-of-interest analysis for the male–female comparison used α = 0.001 uncorrected for multiple comparisons.

Navigation Tasks

All subjects were scanned on both experiments. In each experiment, there were four baseline and four active conditions interleaved, beginning with the baseline. Each condition lasted 29.5 s. Thus the total acquisition time for each experiment was 3 min 56 s. All video footage was run at double speed to increase the task difficulty without extending the acquisition time.

Experiment 1

In the active condition, subjects viewed digital video footage of four different, complex, routes, taken from a car driving around an unfamiliar residential part of Glasgow. Subjects were instructed to learn the routes to the extent that they would be able to close their eyes and think their way through them later.

In the baseline condition subjects viewed digital video footage taken from a static camera looking at a crossroads with traffic lights and moving cars and people. Subjects were instructed to learn what vehicles came from what directions and where they went.

Subjects therefore viewed four different routes, which were neither temporally nor spatially contiguous. This setup was chosen so that the experience of all four active conditions would be comparable; had the routes been contiguous, then the experience of the first route would have been qualitatively different from the others in that they would have been viewed within a setting of a priori knowledge of the environment. In between viewing each route, subjects viewed the static camera footage.

Experiment 2

In the active condition subjects viewed ancient maps of Dutch cities — chosen because they are intuitive, requiring no experience of map reading. Subjects were shown a preparatory map (see Fig. 1) and instructed to learn a route from the top arrow to the middle arrow and then from the middle to the bottom arrow if they had time. They were told to learn it to the extent that they would be able to close their eyes and think their way through it later. They were allowed to familiarize themselves with this map and had the buildings, moat and city walls pointed out to them, to ensure that they would be able to understand the (similar) maps presented in the experiment.

In the baseline condition subjects viewed random assortments of coloured lines. Subjects were instructed to learn the orientation of the lines in as much of the picture as they could, ignoring the colours, to the extent that they would be able to reproduce a section of the picture in monochrome later.

Testing Recall

At the end of the study subjects were given blank paper and told to write down as much as they remembered of the routes and baselines. They were not given any guidance as to whether or not to draw maps or write descriptions; they varied in how they chose to record the routes. They were then scored for recall of routes.

The routes were marked with 1 point given for each correctly remembered turn or landmark up to a maximum of 5 points for each route. So, for example, the map shown at the top of Figure 2 scored 4 points as it had three correctly described turns and one landmark. Similarly, a subject describing the same route as ‘Left by church, then turned 1st right and then 1st left again’ also scored 4 points.

The maps were again marked up to a maximum of 5 points per route, with points being given for the start, middle and end points of the route being in the correct (relative) positions, correct directions from the top to the middle arrow and from the middle to the bottom arrow, and for landmarks recalled. So, for example, the map shown at the bottom of Figure 2 scored 3 points, as did the description, ‘Walk along embankment. 1st R, opposite bridge. Down straight line → church (L)’.

Results

We first analysed the male and female results as one group using a voxel-wise one-sample t-test (P = 0.05, corrected for multiple comparisons). To validate our protocol we were able to compare these results with previous functional imaging studies of route learning and navigation. The areas activated bilaterally in experiment 1 were the parahippocampus, lingual gyrus, precuneus, posterior cingulate gyrus, cuneus, middle occipital gyrus and the middle frontal gyrus. In addition, in experiment 1, the left superior parietal lobule was also activated (Table 1). In experiment 2 the areas activated bilaterally were the parahippocampus, precuneus, posterior cingulate gyrus, superior parietal lobule, middle temporal gyrus, angular gyrus and the middle frontal gyrus. In addition, the left middle and superior occipital gyri and the inferior frontal gyrus were unilateral activations in experiment 2 (Table 2).

To find areas that demonstrated gender-specific activations, we used a voxel-wise two-sample t-test (P = 0.05, corrected for multiple comparisons) to compare the results for men and women. We found no areas that were differentially activated. We then took the results of Grön et al. (2000) as a priori data, allowing a regions-of-interest analysis with uncorrected P values (P = 0.001). Even then, no areas were shown as differentially active.

To find areas that were differentially active in the route and survey perspectives, we used a voxel-wise paired t-test. The subtraction of survey from route perspective revealed two small foci (right cerebrum, 10 voxels; left cerebrum, 3 voxels) of cuneus activation (Table 3). The subtraction of the route from the survey perspective revealed bilateral activation in the middle temporal gyrus, angular gyrus and the precuneus. There was unilateral activation revealed on the right in the cuneus and middle frontal gyrus and on the left in the superior temporal gyrus and the inferior and superior parietal lobules.

In considering the power of the study, Desmond and Glover (2002) looked at the relationship between power and sample size of fMRI studies with different inter- and intra-subject variability, different P thresholds and different percentage signal change. At P = 0.001 (uncorrected) — at which level we saw no activations in the regions of interest in the male-female comparison — the single-voxel power for a subtle difference is very low — in the region of 10%. However, a single-voxel activation is of little significance. The power increases with the expected number of voxels. When the expected cluster size reaches 30 voxels the probability of a type 2 error can then be approximated as 0.930, which gives a probability of failing to find even one voxel of a single 30 voxel cluster as 0.042. Thirty voxels has a cluster volume of 0.24cm3, which is 13% the size of reported activations of the parahippocampal place area (Tong et al., 1998). This is an area of the parahippocampus activated bilaterally by the passive viewing of scenes. This calculation makes the (violated) assumption that the single voxel probabilities are independent; however, it is intended as an approximation, as a rigorous power calculation is beyond the scope of this study.

Testing Recall

Performance data did not appear to be normally distributed and so was analysed using the Mann–Whitney U-test (two-tailed, P = 0.05). In experiment 1 the median scores were 9/20 and 6/20 for men and women respectively (P = 0.0323). In experiment 2 the median scores were 6/20 for men and 5.5/20 women (P = 0.2284).

The subjects varied in how they chose to record the routes. All subjects bar one (male) gave verbal descriptions for experiment 1. For experiment 2, 13 subjects (five males) gave verbal descriptions and 12 subjects (eight males) gave pictorial descriptions. To analyse the differences in experiment 2 we used Fisher’s exact test (two-tailed, P = 0.05); the result was not statistically significant (P = 0.238).

The scores from performance data are low in both men and women. There are three reasons for this: first, whilst the tasks were not cognitively hard, there was a very large amount of information to remember. Second, recall was generally best for the first and last routes in each experiment. Third, to score 5 points required a good description of the whole route; incomplete descriptions scored badly. To score any points at all, subjects must have demonstrated significant recall of the route.

The scores in experiment 2 were lower. This is because the task was to learn a route from the top arrow to the middle arrow, and then from the middle to the bottom arrow if there was time. Most subjects were only able to learn the first part of the route and so could not score full points. The low scores thus reflect both the volume of information to be learnt and the stringent requirements of the marking scheme.

Discussion

Male–Female Comparison

Men and women used the same brain areas for route learning from both perspectives. Group comparisons for each perspective revealed no differences in levels of activation — even when the results of Grön et al. (2000) were taken as a priori data, allowing regions-of-interest analysis with P values uncorrected for multiple comparisons. As the task in experiment 1 was sensitive enough to detect a performance difference, it is surprising that no difference in brain activation was detected.

Grön et al. (2000) showed a number of sex differences in areas of activation for a navigation task. There are a number of reasons why our results could differ: Grön et al. studied navigation, whereas this study looked at route learning. Their task used a virtual reality maze and there have been demonstrated sex differences in experience of computer games, so the results may have reflected this (Funk, 1993; Harrell et al., 1997). They also used significance levels uncorrected for multiple comparisons, so their results could have occurred by chance.

There have been demonstrated sex differences for spatial tasks (Collins and Kimura, 1997; Astur et al., 1998; Gur et al., 2000) and Grön et al. studied ‘visuospatial navigation’. We used a baseline condition with a spatial memory component, eliminating activations from this. So if the reported sex differences for route learning reflect differences for the spatial aspects of the tasks, then we cannot demonstrate their neural correlates.

Whilst topographical and route information are inherently spatial, the reverse is not true. Thus there are aspects of topographical and route learning that differ from other forms of spatial learning. We have shown that these aspects of the tasks do not have gender-specific neural substrates.

Route and Survey Perspective Comparison

The brain areas activated by route learning from both perspectives are shown in Tables 1 and 2 and Figures 3 and 4. The network of brain regions activated by the learning of unfamiliar routes from the route and survey perspectives is consistent with previous findings (Aguirre et al., 1996, 1998; Ghaem et al., 1997; Maguire et al., 1996, 1997, 1998; Maguire, 1997; Grön et al., 2000; Mellet et al., 2000, 2002; Shelton and Gabrieli, 2002).

The results for the comparison of the route and survey perspectives showed that there are many areas that are common to both. This is in line with the results of Shelton and Gabrieli (2002), who compared the encoding of topographical information from the route and survey perspectives. Their experimental tasks involved learning the layout of a space; our experiments included the additional requirements of route learning. However, the comparison of the two different perspectives is expected to yield the same results, as in our study these additional requirements are not expected to differ between them.

The only area highlighted by our comparison of the two perspectives as more active in the route perspective was the cuneus, which was also activated by the survey perspective task. Thus we can say that there are no areas used by route learning from the route perspective that are not used by route learning from the survey perspective. This contradicts the results of Shelton and Gabrieli (2002); their comparison of the two perspectives was direct — they did not use the baseline condition — and so the differences they found may reflect differing visual aspects of the task stimuli.

Shelton and Gabrieli also failed to find medial temporal lobe activation in the survey perspective — again probably from an inadequate baseline condition (Binder et al., 1999) in the separate analyses — but here we demonstrate robust bilateral parahippocampal activation in both perspectives (Figs 3 and 4). This is consistent with lesion studies that suggest that the parahippocampus is essential for the acquisition (but not the recall) of any sort of topographical information (Epstein et al., 2001).

Cognitive maps of the environment are spatial by definition and are dependant on the hippocampus (O’Keefe and Nadel, 1978). The hippocampus has been shown to be involved in a wide range of spatial and non-spatial memory processes (Deweer et al., 2001). Subtraction of the spatial memory component through the spatial baseline conditions in the two experiments meant that we did not expect to find hippocampal activation. That we did not find it is consistent with the role of the hippocampus in general spatial memory.

We demonstrated lateral temporal activation that was present only in the survey perspective task (Fig. 4). The middle and superior temporal gyri and angular gyrus have been shown to be involved in semantic processing (Friederici et al., 2000; Price, 2000) and the middle temporal gyrus has been shown to be involved specifically in semantic processing of pictures (Vandenberghe et al., 1996). Activation of the angular gyrus (in association with the middle occipital gyrus) was also found by Mellet et al. (2002) during the mental scanning of internal representations built from text; however, it was not activated by scanning of representations learnt from maps, which would seem more comparable to our own experiment 2. They suggested that this activation ‘may reflect the transformation of symbolic information into analog “map-like” information’. It is possible that both experiments have in common the need to convert a symbolic representation of a landmark (pictorial or textual) into a mental image. In the mental scanning of representations learnt from maps, subjects did not have to navigate between landmarks, but instead used different coloured dots, the processing of which could not be expected to have the same semantic requirements. The lateral temporal activation in this study is thus likely to reflect semantic processing of the pictorial maps.

We also demonstrated activation of the precuneus in both perspectives. In previous demonstrations of its role in route learning, its activation could be due to optic flow (the perception of movement towards and past oneself) (de Jong et al., 1994). This is clearly not the case here, as optic flow was not present in the map learning condition. However, its activation may reflect the temporal order memory requirements of route learning — Cabeza et al. (1997) demonstrated activation of the precuneus, cuneus and middle frontal gyrus in a temporal order memory task.

In conclusion, we have shown that men and women use the same parts of the brain to learn routes from the route and survey perspectives and that route learning from the route perspective does not activate any additional areas to those used for the survey perspective.

This work was supported by the Robertson Trust and the Carnegie Trust for the Universities of Scotland.

Address correspondence to Richard Blanch, c/o Dr. Condon, Clinical Physics, Institute of Neurological Sciences, Southern General Hospital, Glasgow G51 4TF, UK. Email: rich@blanch.org.

Figure 1. A map of Maestricht is shown. This and other similar maps were shown to the subjects in the active conditions in experiment 2. The task was to learn a route from the top to the middle arrow and then to the bottom arrow if there was time.

Figure 1. A map of Maestricht is shown. This and other similar maps were shown to the subjects in the active conditions in experiment 2. The task was to learn a route from the top to the middle arrow and then to the bottom arrow if there was time.

Figure 2. Two maps drawn by two different subjects after the experiments. The top map depicts the first route in experiment 1. It has three turns and a landmark, and so scored 4 points. The bottom map depicts the first route seen in experiment 2. It has all parts of the route correctly orientated, correct directions from the starting point to the destination (middle arrow) and one landmark (the church), and so it scored 3 points.

Figure 2. Two maps drawn by two different subjects after the experiments. The top map depicts the first route in experiment 1. It has three turns and a landmark, and so scored 4 points. The bottom map depicts the first route seen in experiment 2. It has all parts of the route correctly orientated, correct directions from the starting point to the destination (middle arrow) and one landmark (the church), and so it scored 3 points.

Figure 3. Group data. Activations in experiment 1 (route perspective) at a significance level of 0.05, corrected for multiple comparisons are shown in MNI space. The extensive posterior activation runs from the parahippocampus and lingual gyrus through the retrosplenial area to the cuneus and precuneus.

Figure 3. Group data. Activations in experiment 1 (route perspective) at a significance level of 0.05, corrected for multiple comparisons are shown in MNI space. The extensive posterior activation runs from the parahippocampus and lingual gyrus through the retrosplenial area to the cuneus and precuneus.

Figure 4. Group data. Activations in experiment 2 (survey perspective) at a significance level of 0.05, corrected for multiple comparisons are shown in MNI space. A similar distribution of activations to experiment 1 is seen. In addition there is temporal activation not present in the route perspective task and a greater degree of frontal activation.

Figure 4. Group data. Activations in experiment 2 (survey perspective) at a significance level of 0.05, corrected for multiple comparisons are shown in MNI space. A similar distribution of activations to experiment 1 is seen. In addition there is temporal activation not present in the route perspective task and a greater degree of frontal activation.

Table 1


 Group data, experiment 1: anatomical regions and steriotactic coordinates of the voxels of peak activation

Anatomical region MNI coordinates  Z-value 
 x y z   
Left      
 Posterior cingulate –18 –64 12  5.01 
 Parahippocampus –18 –44 –6  6.49 
 Lingual gyrus –22 –70  0  4.88 
 Cuneus –16 –92 18  5.77 
 Cuneus –18 –84  6  4.65 
 Middle occipital gyrus –20 –76 12  5.3 
 Precuneus –18 –80 40  4.78 
 Superior parietal lobule –18 –58 58  5.57 
 Middle frontal gyrus –26  –6 54  4.64 
Right      
 Posterior cingulate  22 –60 10  4.77 
 Parahippocampus  22 –50 –6  6.05 
 Lingual gyrus   6 –72  0  5.07 
 Cuneus  22 –82 20  6.11 
 Middle occipital gyrus  26 –80  6  5.4 
 Precuneus  20 –66 48  4.85 
 Cingulate gyrus  16 –30 34  4.84 
 Middle frontal gyrus  30  –2 54  5.55 
Anatomical region MNI coordinates  Z-value 
 x y z   
Left      
 Posterior cingulate –18 –64 12  5.01 
 Parahippocampus –18 –44 –6  6.49 
 Lingual gyrus –22 –70  0  4.88 
 Cuneus –16 –92 18  5.77 
 Cuneus –18 –84  6  4.65 
 Middle occipital gyrus –20 –76 12  5.3 
 Precuneus –18 –80 40  4.78 
 Superior parietal lobule –18 –58 58  5.57 
 Middle frontal gyrus –26  –6 54  4.64 
Right      
 Posterior cingulate  22 –60 10  4.77 
 Parahippocampus  22 –50 –6  6.05 
 Lingual gyrus   6 –72  0  5.07 
 Cuneus  22 –82 20  6.11 
 Middle occipital gyrus  26 –80  6  5.4 
 Precuneus  20 –66 48  4.85 
 Cingulate gyrus  16 –30 34  4.84 
 Middle frontal gyrus  30  –2 54  5.55 
Table 2


 Group data, experiment 2: anatomical regions and steriotactic coordinates of the voxels of peak activation

Anatomical region MNI coordinates  Z-value 
 x y z   
Left      
 Posterior cingulate  –6 –44  10  4.56 
 Parahippocampus –24 –46 –10  5.53 
 Middle temporal gyrus –38 –76  28  5.38 
 Middle occipital gyrus –30 –88  12  5.26 
 Superior occipital gyrus –38 –76  28  5.38 
 Angular gyrus –44 –66  36  4.58 
 Precuneus –18 –68  42  5.75 
 Cuneus –10 –76  34  4.97 
 Superior parietal lobule –30 –62  52  4.6 
 Inferior frontal gyrus –46  18   2  4.81 
 Middle frontal gyrus –30   6  54  6.22 
Right      
 Posterior cingulate   6 –52  10  4.62 
 Parahippocampus  28 –42 –12  4.95 
 Superior parietal lobule  25 –72  46  5.05 
 Middle temporal gyrus  40 –78  22  5.74 
 Angular gyrus  42 –74  32  5.37 
 Precuneus  14 –66  52  5.92 
 Middle frontal gyrus  32   2  48  5.79 
Anatomical region MNI coordinates  Z-value 
 x y z   
Left      
 Posterior cingulate  –6 –44  10  4.56 
 Parahippocampus –24 –46 –10  5.53 
 Middle temporal gyrus –38 –76  28  5.38 
 Middle occipital gyrus –30 –88  12  5.26 
 Superior occipital gyrus –38 –76  28  5.38 
 Angular gyrus –44 –66  36  4.58 
 Precuneus –18 –68  42  5.75 
 Cuneus –10 –76  34  4.97 
 Superior parietal lobule –30 –62  52  4.6 
 Inferior frontal gyrus –46  18   2  4.81 
 Middle frontal gyrus –30   6  54  6.22 
Right      
 Posterior cingulate   6 –52  10  4.62 
 Parahippocampus  28 –42 –12  4.95 
 Superior parietal lobule  25 –72  46  5.05 
 Middle temporal gyrus  40 –78  22  5.74 
 Angular gyrus  42 –74  32  5.37 
 Precuneus  14 –66  52  5.92 
 Middle frontal gyrus  32   2  48  5.79 
Table 3


 Two-sample t-tests comparing the route and survey perspectives: anatomical regions and steriotactic coordinates of the voxels of peak activation

Anatomical region MNI coordinates  Z-value 
 x y z   
Route–survey perspectives      
Left      
 Cuneus –18 –88 22  4.55 
Right      
 Cuneus  20 –86 20  4.95 
      
Survey–route perspectives      
Left      
 Middle temporal gyrus –42 –74 24  5.66 
 Angular gyrus –44 –66 32  4.73 
 Precuneus   0 –54 44  4.79 
 Cuneus –10 –70 30  4.54 
 Middle frontal gyrus –36  –2 48  4.64 
      
Right      
 Middle temporal gyrus  52 –68 16  5.5 
 Superior temporal gyrus  50 –55 24  4.72 
 Angular gyrus  44 –74 32  
 Precuneus  10 –62 38  5.75 
 Inferior parietal lobule  44 –60 44  4.61 
 Superior parietal lobule  36 –60 52  4.73 
Anatomical region MNI coordinates  Z-value 
 x y z   
Route–survey perspectives      
Left      
 Cuneus –18 –88 22  4.55 
Right      
 Cuneus  20 –86 20  4.95 
      
Survey–route perspectives      
Left      
 Middle temporal gyrus –42 –74 24  5.66 
 Angular gyrus –44 –66 32  4.73 
 Precuneus   0 –54 44  4.79 
 Cuneus –10 –70 30  4.54 
 Middle frontal gyrus –36  –2 48  4.64 
      
Right      
 Middle temporal gyrus  52 –68 16  5.5 
 Superior temporal gyrus  50 –55 24  4.72 
 Angular gyrus  44 –74 32  
 Precuneus  10 –62 38  5.75 
 Inferior parietal lobule  44 –60 44  4.61 
 Superior parietal lobule  36 –60 52  4.73 

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