Journal of Experimental Psychology: Learning ...

Journal of Experimental Psychology: Learning, Memory, and Cognition Geometry Three Ways: An fMRI Investigation of Geometric Information Processing During Reorientation Jennifer E. Sutton, Alexandra D. Twyman, Marc F. Joanisse, and Nora S. Newcombe Online First Publication, May 14, 2012. doi: 10.1037/a0028456

CITATION Sutton, J. E., Twyman, A. D., Joanisse, M. F., & Newcombe, N. S. (2012, May 14). Geometry Three Ways: An fMRI Investigation of Geometric Information Processing During Reorientation. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. doi: 10.1037/a0028456

Journal of Experimental Psychology: Learning, Memory, and Cognition 2012, Vol. ●, No. ●●, 000 – 000

© 2012 American Psychological Association 0278-7393/12/$12.00 DOI: 10.1037/a0028456

Geometry Three Ways: An fMRI Investigation of Geometric Information Processing During Reorientation Jennifer E. Sutton

Alexandra D. Twyman and Marc F. Joanisse

Brescia University College

The University of Western Ontario

Nora S. Newcombe Temple University

The geometry formed by the walls of a room is known to be a potent cue in reorientation, yet little is known about the use of geometric information gleaned from other contexts. We used functional magnetic resonance imaging to examine neural activity in adults while reorienting in 3 different environments: the typical rectangular walled room, a rectangular configuration of pillars in an open field, and a rectangular floor in an open field. Behavioral response patterns for the 3 environments were similar, but pairwise contrasts of brain activation revealed differences at the neural level. We observed greater medial temporal lobe (MTL) involvement when reorienting with the pillars versus the walls and floor. In addition, the walled room selectively engaged areas of posterior parahippocampal cortex corresponding to the parahippocampal place area, when compared with the floor. Finally, a conjunction analysis of the 3 geometry conditions, compared with a control task, revealed activation in the primary auditory cortex that was common to all geometry conditions. These findings add to growing evidence that adults use verbal processes to encode environment geometry and that the reorientation tasks that young children find difficult are particularly hippocampus-dependent. Keywords: reorientation, geometry, spatial cognition, fMRI

While smaller items would be more useful for pinpointing a precise location within a larger area, the usefulness of these large, distal items for general reorienting is intuitively appealing. Little is known, however, about the use of topographic features in the natural environment for reorientation. Instead, most evidence of sensitivity to environment geometry relies on studies closely resembling the initial laboratory demonstration by Cheng (1986). Rats learned the location of food in one of four distinctly marked corners of a rectangular arena, and the rats were subsequently disoriented. When they then had the opportunity to search for the hidden food, they searched in both the correct corner and the corner diagonally opposite it. To explain this suboptimal search strategy, Cheng proposed that reorientation was accomplished primarily via the geometry of the arena (e.g., remembering a corner with a long wall on the left and a short wall on the right) rather than by its distinctive feature (a distinct scent or visual cue). If features were ever used, Cheng proposed they were pasted onto a metric frame in a process requiring extensive training. Thus, a response pattern where searches are divided between the correct corner and its rotational equivalent within the rectangular space is now considered the marker of sensitivity to environment geometry. It has been demonstrated by an impressive diversity of species, including both young and adult humans, birds, fish, ants, and rodents (reviewed by Cheng & Newcombe, 2005). A number of models have been proposed to account for animals’ reorientation behavior (reviewed by Cheng & Newcombe, 2005, and Tommasi, Chiandetti, Pecchia, Sovrano, & Vallortigara, 2011), and how animals perceive environment geometry continues to be debated (reviewed by Sutton, 2009, and continuing to be

An important component of spatial navigation is the ability to keep track of one’s position in space. Losing a sense of position, or becoming disoriented, can occur gradually, as when position is lost when attention is directed at a fascinating conversation rather than the path being navigated, or suddenly, as when emerging from a subway station in an unfamiliar location. Sensitivity to the geometry formed by items in the environment is one way organisms regain orientation. According to Gallistel (1990), the topographic features of the environment that are the most useful for extracting geometric information are those that are large and that extend horizontally, such as rivers, tree lines, and mountain ranges. The advantage of using these types of spatial cue is that they remain informative over long periods of time due to their large size, stable position, and easily recognizable appearance. For instance, snow cover that may obscure smaller plants and other spatial cues would do little to obscure a river or a line of trees.

Jennifer E. Sutton, Department of Psychology, Brescia University College, Ontario, Canada; Alexandra D. Twyman and Marc F. Joanisse, Centre for Brain and Mind, The University of Western Ontario, Ontario, Canada; Nora S. Newcombe, Department of Psychology, Temple University. This research was supported by an operating grant from the Canadian Institutes for Health Research and by the Spatial Intelligence and Learning Centre via National Science Foundation Grant SBE0541957. Correspondence concerning this article should be addressed to Jennifer E. Sutton, Department of Psychology, Brescia University College, 1285 Western Road, London, Ontario N6G 1H2, Canada. E-mail: jennifer [email protected] 1

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SUTTON, TWYMAN, JOANISSE, AND NEWCOMBE

debated, e.g., Kelly, Chiadetti, & Vallortigara, 2010; Sturz & Bodily, 2011). Similar to Cheng’s (1986) experiments with rats, demonstrations of sensitivity to environment geometry in humans most often utilize an enclosed rectangular room and reveal subtleties in how humans reorient in such an environment. Initial reports showed a developmental difference in how features and room geometry were combined to reorient: Adults used information from a uniquely colored feature wall to limit searches to the correct corner, while young children appeared to ignore the feature and rely on the geometric information alone (Hermer & Spelke, 1994, 1996). It now appears, however, that the relative weights given to features and geometry in this task depend on environment specifics such as the size of the room (Learmonth, Nadel, & Newcombe, 2002; Learmonth, Newcombe, Sheridan, & Jones, 2008), the particular shape of the room (Hupbach & Nadel, 2005), how the features in the room vary (Huttenlocher & Lourenco, 2007), and previous experience with the cues (Ratliff & Newcombe, 2008; Twyman, Friedman, & Spetch, 2007). Thus, the conditions that support the integration of geometry and feature information and, especially, whether geometric information is processed by a specialized module (e.g., Cheng, 1986; Lee & Spelke, 2010a) or is combined with other cues in the most adaptive way (Newcombe & Ratliff, 2007) have been the major focus of research in reorientation in humans. Less is known about whether the geometry effect obtained under standard laboratory conditions is generalized to natural environments. Rooms with perfectly vertical walls that are sometimes positioned very close to the participant are a powerful, and very specific, instantiation of environment geometry. Comparatively few studies have explored the use of geometric information derived from cues other than walls, although some data suggest that the geometry of such spaces may be processed differently from the typical room. For instance, in a small space, 3- to 4-year-old children who usually reorient using the geometry of vertical walls fail to do so when four objects delineate the rectangular search space, although adults are successful with the corner objects (Gouteux & Spelke, 2001; Lee & Spelke, 2008; Lee & Spelke, 2011). Furthermore, young children also fail to reorient when the rectangular geometry of the small space is delineated by lines on the floor (Lee & Spelke, 2008) or by a thin vinyl mat placed on the floor (Lee & Spelke, 2011). Since environment size is known to be an important factor in reorientation, and the spaces in these studies were small, whether this pattern of results would hold with a larger search space is unclear. Nonetheless, some researchers have explained this pattern of failure to use discrete objects and twodimensional cues for reorientation by suggesting that extended three-dimensional (3D) structures, as opposed to those that lie flat on the ground, are the preferred cues for reorientation (Lee & Spelke, 2008). They have argued that flat surface features in the natural world change too often and are unreliable cues for reorientation, so humans have evolved to reorient based on threedimensional cues. Gallistel (1990), however, made no distinction between the height of natural features and their usefulness for reorientation. Indeed, one might imagine a stream that is almost flush with the ground as a potent cue to be incorporated in a representation of an environment’s layout, one that would be salient in both summer and winter. Yet the stream bank’s vertical extent might be quite

minimal. Furthermore, there are no data available on adults’ use of items like the lines on the floor or the vinyl mat to extract geometric cues, and if an inability to use such cues is unique to young children under certain conditions, the source of their failure might be more accurately attributed to ongoing cognitive and neural development, rather than a peculiarity of human adaptation. Until recently, hypotheses about the neural underpinnings of geometric information processing during reorientation have been largely indirect, in that they are based on data not generated during the reorientation task (Doeller, King, & Burgess, 2008; Epstein, 2008; Epstein & Kanwisher, 1998) or on studies with patients in which the neural characteristics are inferred but not directly assessed (Lakusta, Dessalegn, & Landau, 2010). For instance, Epstein and Kanwisher (1998) observed brain activation while participants viewed a series of static images including objects, empty rooms, and furnished rooms. Activation in a region of posterior parahippocampal cortex, which they called the parahippocampal place area (PPA), showed increased activation for empty rooms versus objects. They hypothesized that this sensitivity to the walls of a room indicated a crucial role for the PPA in geometric information processing. In a very different dynamic navigation task, Doeller et al. (2008) had participants learn object locations in an environment with a landmark and a low wall and then later replace the object where they had seen it. On conflict tests, the two cues indicated different positions for the object. When participants used the low wall to replace the object, hippocampal activation was greater than when they used the landmark. Hippocampal activation driven by the low boundary has been interpreted by some as evidence that geometric reorientation is hippocampus-dependent and that its neural underpinnings are distinct from feature use in the reorientation paradigm (Lee & Spelke, 2010b). Extending the Doeller et al. finding to the typical reorientation paradigm is problematic, however, since there were distal orienting cues beyond the low wall in the Doeller et al. task, and no such cues are available when people rely on geometry in the reorientation task. Those distal cues could have played a role in the increased hippocampal activation observed. Furthermore, in the only study to date that directly measured brain activation during the reorientation task, Sutton, Joanisse, and Newcombe (2010) found increased hippocampal activation when a feature was present in the form of a red wall versus a geometryonly condition in which all walls were gray. This supports the idea that the hippocampal activation seen by Doeller et al. (2008) may be due to the orienting cues beyond the boundary, since the feature wall served as an orienting cue in this paradigm. Importantly, the Sutton et al. data should not be interpreted to mean that the hippocampus is not involved in geometry processing, only that its involvement was greater for conditions with a feature present. On the other hand, the geometry condition revealed increased activation in prefrontal cortex and left inferior frontal gyrus when compared with conditions with a feature. Overall, the Sutton et al. findings suggest caution when generalizing patterns of brain activation across spatial tasks. In the current study, we investigated how adults assess the geometry of different kinds of spaces when reorienting using both behavioral and neural measures. Building on the findings of Sutton et al. (2010), we compared performance across three versions of a rectangular search space, as shown in Figure 1: the traditional, enclosed rectangular room with four identical floor-to-ceiling

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NEURAL UNDERPINNINGS OF GEOMETRY PROCESSING

Figure 1. Example view of each of the four virtual environments during the encoding phase.

walls (walls condition); a rectangular search area delineated by four identical pillars, each placed at a corner in an open field (pillars condition); and a shaded rectangular area flush with the ground, identical to the floor from the walls condition and also placed in an open field (floor condition). We measured adults’ ability to reorient with these different instantiations of environment geometry both at the behavioral and neural levels, using a functional magnetic resonance imaging (fMRI)-compatible virtual reality (VR) version of the reorientation task. In the encoding phase of each trial, participants looked around the space and observed the location of a pylon. After a brief break that disrupted their orientation in the space, they were returned to a novel location in the same area for a retrieval phase in which they picked up the pylon and placed it in the location observed during the prior encoding phase. To compare the three conditions, we measured the proportion of pylon placements in each corner while simultaneously monitoring brain activity throughout the trial. Neural data were analyzed separately for the encoding and retrieval portions of the trials as in Sutton et al. (2010). Since behavioral output is limited to the final placement of an object in the to-be-remembered corner, analyzing the phases separately has the potential to provide otherwise unavailable information about the different demands in each phase of the task. We were also interested in the common regions of activation among the three geometry conditions and included a beacon control condition for comparison. The beacon condition was a room identical to the walls condition, except that it was square instead of rectangular and, therefore, contained no geometric information relevant to the task. Two beacons, identical to the pillars used in the pillars condition, were placed in the opposite corners of the square room. Encoding and retrieval phases were the same as in the other conditions, but participants remembered whether the pylon was positioned in a corner with or without a beacon. Thus, the beacon condition incorporated elements of the other three

conditions yet required a different kind of spatial coding to complete successfully. Based on the existing data from children using similar search spaces (Gouteux & Spelke, 2001; Lee & Spelke, 2008; Lee & Spelke, 2011), we predicted that adults might show a weaker geometric response pattern (a concentration of placements in the correct and rotationally correct corners of the rectangular space) in the floor and pillars conditions than with the walls. We were also interested in whether the three conditions would reveal different degrees of involvement of the medial temporal lobe (MTL). Sutton et al. (2010) found that conditions that were associated with more hippocampal activity in adults (i.e., using a colored wall feature to code location) corresponded to conditions with which young children typically have problems in behavioral studies. They suggested that the performance differences between adults and children under some circumstances might therefore be due to the immaturity of the hippocampus in young children. Extending that logic, if young children’s failure to reorient using just pillars and a mat on the floor in Lee and Spelke’s studies (Gouteux & Spelke, 2001; Lee & Spelke, 2008; Lee & Spelke, 2011) is also due to immature hippocampi, we would expect the pillars and floor conditions of the current study to result in increased hippocampal activation in adults.

Method Participants Sixteen neurologically healthy adults (9 women, 7 men) ages 19 –38 years (M ! 24 years) were recruited from the University of Western Ontario community. Eleven were right-handed and five were left-handed, by self-report. All procedures were approved by the University of Western Ontario Medical Research Ethics Board, and informed consent was obtained from each participant prior to testing.

Behavioral Task and Data Analysis During scanning, participants performed a navigation task implemented within a nonimmersive, interactive virtual reality environment. A first-person perspective was rendered on a video display that was projected at 1,024 " 768 resolution onto a screen mounted at the head of the scanner bore. Participants viewed the display through a mirror placed above the head coil. The 3D environment was rendered in real-time using the Half-Life 2 game engine and the Source Software Development Kit (Valve Software, Bellevue, WA), using a Windows PC equipped with an AMD Athlon dual core processor and a 128 MB nVidia GeForce 8800 graphics processor. Movement was controlled via a fourbutton directional keypad, and a fifth button was provided to allow interaction with objects within the environment. At the start of each trial, participants were placed at the center of the environment randomly facing one of the four cardinal directions. They were instructed to move about in order to determine the location of an object during this encoding phase (a traffic pylon; see Figure 1), which was presented in one of the four corners of the space. Movement was controlled with four directional arrow keys to move forward and backward and to turn left and right. After 10 s, the screen was cleared and blank. After 2 s,

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the retrieval phase began; participants viewed the same environment as in the encoding phase, but they were placed at a randomly predetermined corner facing into the environment with the pylon in the center of the space. Participants moved to and picked up the pylon (via a keypress) and placed it in the place where they had previously viewed it in the encoding phase. This sequence was accomplished by moving toward the pylon using the directional keys on the keypad, pressing the “pick up/drop” key to pick it up, moving to the desired corner using the directional keys, and finally pressing the “pickup/drop” key again to drop the pylon. A total of 16 s was provided for the retrieval phase, at which point the screen went black with a large red circle in preparation for the next trial 4 s later. Figure 1 shows the environments used in the four conditions. In the walls condition, participants completed the task in a room with a rectangular geometry such that there were two longer walls and two shorter walls rendered as 765 " 513 units of measure, where each unit corresponds to a perceived size of approximately 1.9 cm. Therefore, the entire room appeared as 14.54 m " 9.76 m, and the software provided an apparent eye level that was approximately 64 units, or 1.21 m, above ground level, with a 75° horizontal field of view. All walls were shaded with the same light gray cinderblock texture. The room was 326 units (6.19 m) tall, with a dark gray ceiling and a medium gray textured floor. The floor condition was not an enclosed room, but it consisted of the same floor used in the walls condition, placed in an open, outdoor field. The horizon was equidistant from all four corners of the floor, and light and shading in the sky was controlled so that it offered no orienting cues. Participants’ movements were restricted to the floor itself, though no visible barrier was present. In the pillars condition, participants were again in an open field, but four columns marked the corners of a rectangle identical in size to the floor in the floor condition and the room in the walls condition. There was no floor present; the pillars appeared to sit directly on the grass in the field. Participants’ movements were restricted to the rectangle shape formed by the pillars, though again, there was no visible barrier present. Finally, the beacon condition consisted of a square room (513 " 513 units). The color and texture of the ceiling, floor, and walls, and the height of the room were identical to the walls condition. Two opposite corners contained a single pillar, identical to the pillars used in the pillars condition. Participants performed seven runs of eight trials each (two of each condition per run) for a total of 56 trials, 14 of each condition. Trials were presented in random order with the constraint that no condition was presented twice in a row, and each condition preceded and followed the other three conditions with equal frequency. The participant’s initial orientation during the encoding and retrieval phases was also counterbalanced across conditions. In keeping with other studies of reorientation, participants’ placements of the pylon in the retrieval phase of the walls, floor, and pillars conditions were characterized as C (correct corner), N (near correct corner error), R (rotational equivalent), or F (far corner error), as shown in Figure 2. It should be noted that in a room with informative geometry but no orienting features (i.e., the walls, pillars, and floor conditions used here), both the C and R corners were considered “geometrically correct” and were therefore combined for accuracy analyses, although data for each are displayed separately. For scoring purposes, the search space was divided into four equal quadrants that each contained one of the

Walls F

R

.10

Pillars .40

(.03)

(.04)

.37

.12

(.05)

(.03)

C

N

Floor F .10

(.03)

R

.44

(.04)

F

.06

.49

(.02)

R

C

(.04)

.35

.10

(.02)

(.03)

N

Beacon .36

C

(.04)

.11

(.03)

Pillar = .98 (.05) No pillar = .95 (.10)

N

Figure 2. Proportion of total placements in each corner of the four environments. Parentheses indicate standard errors. Note that each corner served as the correct corner an equal number of times within each condition. C ! correct; N ! near error; R ! rotational equivalent; F ! far error. For the beacon condition, accuracy data when the correct corner contained a pillar and when it did not are shown separately.

corners. The pylon was considered “in a corner” if it was anywhere in the corresponding quadrant. Using the proportion of total searches in each corner, a one-way repeated-measures analysis of variance (ANOVA) was conducted to compare placements in the geometrically correct corners (C # R) across the walls, floor, and pillars conditions. In addition, geometrically correct placements for each condition were compared with chance (.50) using a one-sample, two-tailed, t test. For the beacon condition, accuracy was measured by the proportion of correct placements in a corner with or without a pillar.

fMRI Data Acquisition and Analysis Imaging was performed at 3 Tesla using a Siemens Tim Trio scanner and a 32-channel head-coil for transmit/receive. T2*weighted functional scans were acquired in an axial orientation with single-shot echo-planar imaging (EPI) using an iPAT parallel acquisition sequence (GRAPPA, generalized autocalibrating partially parallel acquisition; acceleration factor ! 2). We acquired 36 slices per volume (voxel size ! 3 " 3 " 3.5 mm; FOV ! 240 " 240 mm; TR ! 2 s; TE ! 30 ms), providing full coverage of the cerebrum and cerebellum. A total of 896 functional scans were acquired for each participant, divided over seven runs (4.27 min per run). Prior to the first functional scan, a whole-head highresolution 3D anatomical scan was acquired within the same orientation as the functional scans, using a 3D pulse sequence weighted for T1 contrast (MPRAGE; GRAPPA acceleration factor ! 2; voxel size ! 1 " 1 " 0.875 mm; FOV ! 256 " 240 mm; 192 slices; TR ! 2.3 s; TE ! 4.31 ms). Data were analyzed using AFNI (Cox, 1996). Functional scans were preprocessed as follows: Motion correction was performed concurrently with scanning, relative to the first functional scan of each sequence; slice timing was adjusted offline (AFNI 3dTshift, quintic interpolation); finally each volume was reregistered to the functional volume immediately preceding the anatomic scan (AFNI 3dvolreg), to correct for movement between scanning runs.

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All participants produced movements of less than 3 mm and 3° across runs. Statistical parametric maps were created in two steps. The first step created a general linear model (GLM, AFNI 3dDeconvolve, linear drift corrected) for each participant. Eight GLM predictors were created, representing the crossing of “encoding” and “retrieval” phases of the walls, pillars, floor, and beacon trial types. Each was modeled as a boxcar function convolved with a canonical hemodynamic response function. The encoding phase was modeled as the first 5 volumes (10 s) of the trial. The retrieval phase began at the seventh volume, posttrial onset, and continued until the end of the trial for a total of eight volumes. Anatomical coregistration was performed by spatially transforming each participant’s anatomical scan to standard 3D space (Talairach & Tournoux, 1988). This transformation was next applied to each participant’s statistical maps, following resampling to 1 mm3 resolution. Finally, a spatial filter was applied (Gaussian blur, 5 mm FWHM). The second analysis step obtained groupwise statistical maps using a two-way repeated-measures ANOVA model (AFNI 3dANOVA3) for main effects and interactions of the factors Trial Type (Walls, Pillars, Floor, Beacon) and Trial Phase (Encoding, Retrieval). All statistical maps were thresholded at an uncorrected alpha level of p ! .001. As in Sutton et al. (2010), the first set of analyses focused on activated voxels within bilateral MTL structures, including the amygdala and hippocampus (identified using Pruessner et al., 2000), and the temporopolar, perirhinal, entorhinal, and parahippocampal cortices (Pruessner et al., 2002). Given narrow anatomical restrictions and the a priori nature of this region of interest (ROI) analysis, a minimum cluster size was not enforced. For non-MTL regions, correction for multiple contrasts was obtained using a Monte Carlo simulation (AFNI AlphaSim; 10,000 iterations), which provided a corrected alpha level of p $ .05 using a minimum cluster size of 243 mm3. We also conducted a conjunction analysis to identify regions showing significantly greater activation that were common to all three geometry conditions, compared with the beacon condition. Analyses were performed to identify voxels reaching significance for each of the floor % beacon, walls % beacon, and pillars % beacon contrasts, separately for encoding and retrieval phases.

Note that the conjunction analysis approach differs from a single contrast of (floor # walls # pillars) % beacon, which averages over the three geometry conditions in a way that leaves open the possibility for an effect to be carried by only a subset of the conditions. Instead, the conjunction analysis reveals only those voxels that are significant across all three pairwise contrasts.

Results Behavioral Task Figure 2 shows the mean proportion of placements in each corner for the three geometric cue conditions and in the correct corners for the beacon condition. Participants placed the pylon in the correct (C) and rotational equivalent (R) corners significantly more than would be expected by chance (.50) in the walls condition (M ! .77, SE ! .06), t(15) ! 4.73, p $ .001, the pillars condition (M ! .85, SE ! .04), t(15) ! 9.62, p $ .001, and the floor condition (M ! .79, SE ! .05), t(15) ! 5.60, p $ .001. A repeated-measures ANOVA showed no differences in tendency to place the pylon in the geometrically correct corners (C # R) across the three conditions, F(2, 30) ! 1.73, ns. Participants were highly accurate on beacon trials both when the pylon was observed in a corner with a pillar (M ! .98, SE ! .01) and when the pylon was observed in a corner without a pillar (M ! .95, SE ! .03). Placements with both types of target locations were significantly above chance (.50), beacon: t(15) ! 39.52, p $ .001; no beacon: t(15) ! 17.80, p $ .001.

Imaging Walls versus pillars contrasts. The walls versus pillars contrasts examined brain regions that showed a significant activation difference between two rectangular spaces that were either delineated by floor-to-ceiling walls (walls) or a single pillar placed at each of the four corners of the space (pillars). Therefore, this contrast revealed differences at the neural level when people used solid, continuous vertical surfaces to code the geometry of a space versus when people used discrete objects whose configuration formed the same geometric shape. The ROI analysis for areas of

Table 1 Clusters of Significant Activation in the Medial Temporal Lobe Region of Interest Analysis Contrast

Region

Talairach coordinates

Environment

Phase

Activation

L/R

Area

x

y

z

Size (mm3)

Walls vs. pillars

Encoding

Pillars % walls

Walls vs. floor

Retrieval Encoding

Pillars % walls Walls % floor

Pillars vs. floor

Retrieval Encoding

Walls % floor Pillars % floor

L R R R L L L R L L L

Hippocampus (posterior) Hippocampus (dentate gyrus) Perirhinal cortex PHC Hippocampus (posterior) Hippocampus (posterior) PHC PHC PHC Hippocampus/tail of caudate Entorhinal cortex

&29 31 34 34 &21 &35 &14 14 &13 &21 &20

&30 &32 &13 &23 &28 &28 &44 &39 &42 &22 3

&4 0 &20 &17 &2 &4 &8 &8 &3 &6 &22

216 54 84 35 25 19 118 90 76 120 37

Note. There were no clusters of significant activation in the medial temporal lobe region of interest during the retrieval phase of the pillars versus floor contrast. Coordinates indicate location of peak activation for each cluster. L/R ! left/right; PHC ! parahippocampal cortex.

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Figure 3. Brain regions showing significant activation differences in the pillars % walls and pillars % floor contrasts. Statistical maps are overlaid on a Talairach-transformed standard brain. Bar charts show mean conditionwise beta weights for each cluster indicated with a red arrow. fMRI ! functional magnetic resonance imaging.

MTL revealed multiple clusters of significant activation that were greater for the pillars condition (see Table 1 and Figure 3). In the encoding phase, we observed more activation on pillar trials in bilateral posterior hippocampus and right parahippocampal cortex and perirhinal cortex. In the retrieval phase of the trial, we observed similarly greater left hippocampus activation for the pillars condition. The second analysis examined clusters of activation across all brain regions (see Table 2). We observed a number of significant activation differences during encoding, including more left superior parietal activation (extending bilaterally) on pillars trials, as well as clusters in areas of the occipital cortex, in the cerebellum, and along the superior temporal sulcus. During the retrieval phase, the walls condition resulted in a cluster of activation in the lingual gyrus (with peak in the left hemisphere but extending bilaterally). Walls versus floor contrasts. The walls versus floor contrasts compared regions of activation on trials that took place in the rectangular space rendered as an enclosed room (walls) or on the rectangular, shaded floor in an open field (floor). Therefore, this contrast revealed underlying neural differences when people processed geometry formed by extended surfaces with vertical extent versus no vertical extent. During the encoding phase of the trial,

the walls condition was associated with more activation in bilateral areas of posterior parahippocampal cortex (see Table 1 and Figure 4) that appear to roughly correspond to the PPA (Epstein & Kanwisher, 1998). A similar pattern of PPA activation, limited to the left side, was seen during retrieval. Bar charts in Figure 4 show the normalized BOLD response for the walls and floor conditions in these regions, and similar data from the pillars condition are included for illustrative purposes. Activation in these areas during encoding on pillars trials was intermediate to the walls and floor conditions. During retrieval, the pillars condition showed numerically similar activation to the floor condition. the pillars condition was not significantly different from either of the other two conditions, however, as is evident in the lack of PPA effects in the walls versus pillars and pillars versus floor contrasts (the latter are described below). In the whole brain analysis (see Table 2), areas of the left lingual gyrus were significantly more activated for the walls condition than for the floor condition during both encoding and retrieval. Pillars versus floor contrasts. The pillars versus floor contrasts examined brain regions that showed significant activation differences between the two rectangular environments set in an open field, one delineated by a shaded floor (floor condition) and the other, by a pillar at each corner (pillars). Comparing these two environments revealed differences in the underlying neural response to using the configuration of discrete objects to code geometry versus the continuous surface with no vertical extent formed by the floor. Children have shown difficulty in using geometric information from both kinds of context. The ROI analysis revealed a significant cluster of activation in the left hippocampus extending into the tail of the caudate and a small cluster in entorhinal cortex on pillars trials during the encoding phase of the trial (see Table 1, Figure 3) and no differences during the Retrieval phase. The whole-brain analysis (see Table 2) revealed greater activation during the encoding phase of pillars trials in the right middle occipital, inferior frontal, fusiform, and inferior parietal areas, along with the left cuneus. During the retrieval phase, there was also greater activation in the right cuneus on pillar trials than on floor trials. Conjunction analyses. Figure 5 and Table 3 show the significant clusters of activation common across all three geometric conditions versus the beacon condition. Clusters in the posterior portion of left superior temporal gyrus, as well as the left supramarginal gyrus, showed more activation for all three geometry conditions in the encoding phase of the trials. There were no areas common to all three contrasts for the retrieval phase.

Discussion In a virtual reality version of the spatial reorientation task, adult participants observed the location of an item and, after a short break that disrupted their orientation, replaced the item in the previously viewed location. In three conditions, participants could rely only on geometric cues provided by the walls of an enclosed room, the four pillars placed in an open field, or a rectangle-shaped floor placed within the open field. All three instantiations of environment geometry resulted in the typical behavioral response pattern seen in other reorientation studies, and there were no significant differences detectable at the behavioral level between the three conditions. A control condition contained many of the

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Table 2 Clusters of Significant Activation in the Whole-Brain Analysis Contrast

Region

Talairach coordinates

Environment

Phase

Activation

L/R

Area

x

y

z

Size (mm3)

Walls vs. pillars

Encoding

Pillars % walls

L R L R R L R R L L L

Cerebellum Cerebellum Cuneus Middle temporal gyrus Inferior frontal gyrus Middle temporal gyrus Fusiform gyrus Inferior temporal gyrus Inferior frontal gyrus Precuneus Middle temporal gyrus/ Superior temporal sulcus Middle occipital gyrus Cerebellum Lingual gyrus Lingual gyrus Lingual gyrus Cuneus Cerebellum Middle occipital gyrus Inferior frontal gyrus Middle frontal gyrus Inferior parietal Middle frontal gyrus Cerebellum Cuneus

&37 32 &1 59 47 &52 28 57 &32 &3 &51

&73 &85 &64 &51 14 &52 &3 &57 45 &50 &15

&40 &30 45 2 25 0 &31 &12 &9 53 &7

15,452 8,986 6,249 726 532 520 464 375 450 343 330

&30 50 &7 &5 &7 &2 32 32 50 45 40 &43 &39 2

&90 &56 &64 &68 &65 &92 &80 &72 9 35 &38 10 &46 &93

2 &32 2 &5 2 8 &23 11 33 14 40 48 &52 21

298 297 2,582 4,219 5,024 56,092 4,653 1,675 1,418 347 312 269 245 3,155

Walls vs. floor Pillars vs. floor

Retrieval Encoding Retrieval Encoding

Walls % pillars Walls % floor Walls % floor Pillars % floor

Retrieval

Pillars % floor

L R L L L L R R R R R L L R

Note. Coordinates indicate the location of peak activation for each cluster. L/R ! left/right.

same environmental elements as the geometry conditions but required memory for the presence or the absence of a beacon rather than memory for a geometric cue. By contrasting the three geometry conditions, we investigated how the brain differentiates between these cues. A conjunction analysis of all three geometry conditions versus the beacon control condition allowed us to investigate regions of activation common to all three instantiations of environment geometry. Together, these analyses revealed distinct neural responses to different instantiations of geometry, revealed a role for language in geometric information processing, and shed light on the typical developmental differences seen in the performance of the reorientation task.

Medial Temporal Lobe Activation Across Geometry Conditions On the surface, behavioral performance across the floor, pillars, and walls conditions appears quite similar. This pattern shows that unlike children, adults are able to use geometric information from all three contexts, not only when walls enclose a space. Clear differences were revealed at the neural level, however. The pillars condition was associated with more hippocampal and parahippocampal cortex activation when compared with the floor and the walls conditions, especially when people were encoding the location of the pylon in the first part (i.e., the encoding phase) of the trials. In particular, the significant clusters in hippocampus suggest that when participants extracted geometric information from the four pillars, they might have formed a different representation than

during trials with extended surfaces. Data from other spatial paradigms have linked learning about the spatial configuration of objects with increased hippocampal activation (Bohbot, Iaria, & Petrides, 2004; Iaria, Petrides, Dagher, Pike, & Bohbot, 2003; Shelton & Gabrieli, 2002). In the current pillars condition, participants might have encoded the location of the pylon relative to the overall rectangular configuration formed by the pillars but used a less complete representation of the space on trials with extended surfaces. For instance, participants might only remember the relative lengths of the extended surfaces that meet at the target corner, such as a long wall on the left and a short wall on the right, rather than encoding the pylon relative to the entire rectangle on each trial. By changing the size of a virtual room between training and testing, Sturz and Kelly (2009) demonstrated that adults encoded room wall lengths in a relative, rather than absolute, manner, although it is unclear whether they encoded the entire room or only the walls near the target corner. Recently, however, Reichert and Kelly (2011) showed that adults did not use the rectangular shape formed by discrete objects to reorient. In their experiment, the discrete objects were not identical as in the current pillars condition, so object characteristics could be used to help remember the target corner. These additional cues may have led to a heavier weighting of local (object properties) cues and less weight for global (overall configuration shape) cues in memory than in the current pillars condition, in which local information from the individual pillars was uninformative. In any case, the pillars condition clearly recruited MTL resources when forming a represen-

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Figure 4. Brain regions showing significant activation differences in the walls % floor contrasts. Statistical maps are overlaid on a Talairach-transformed standard brain. Bar charts show mean conditionwise beta weights for the clusters indicated with a red arrow. L ! left; R ! right; PPA ! parahippocampal place area; fMRI ! functional magnetic resonance imaging.

tation of the pylon’s location in a way that a room with walls or a floor alone did not. Similar distinctions were less frequently observed during retrieval, suggesting that the major difference across conditions was the initial encoding of the environment geometry rather than the retrieval of the representation from working memory to guide a response.

Implications of MTL Effects for Processing Environment Geometry Across Development A difference at the neural level between geometric cues conveyed via discrete objects, and that same information conveyed via continuous vertical surfaces is consistent with data showing that young children appear less sensitive to geometric cues provided by the former than by the latter (Gouteux & Spelke, 2001; Lee & Spelke, 2008; Lee & Spelke, 2011). This pattern of increased hippocampal activation in adults for a condition in which young children show poor behavioral performance was also seen in Sutton et al. (2010), in which increased hippocampal activation was found when adults used a feature in the reorientation task relative to room geometry alone. Taken together with the current study, these fMRI data suggest that multiple findings from young

children in the reorientation task may be attributable to the hippocampal involvement necessitated by certain types of spatial cue. It is now known that the hippocampus develops over the course of childhood, until about age 8 or 9 (Alvarado & Bachevalier, 2000; Gogtay et al., 2006; Seress, 2001; Utsunomiya, Takano, Okazaki, & Mitsudome, 1999). The small, less salient, search spaces that seem to be crucial for demonstrating children’s insensitivity to either a room feature or certain instantiations of geometry may be particularly challenging for an immature hippocampus. Brain imaging data from children performing the reorientation task will be necessary to more fully evaluate this hippocampal development hypothesis. Reorientation in the floor condition, which children struggled with in “real world” experiments but adults performed with apparent ease here, did not involve more MTL activation compared with the walls condition, however. This appears to present a problem for the hippocampal development argument for children’s poor reorientation performance without vertical walls: We should expect any condition children have trouble with to be especially hippocampus-dependent in adults. It should be emphasized that failure to show significant differences in hippocampal activation


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Figure 5. Brain regions showing significant effects for the conjunction analysis (floor % beacon, walls % beacon, pillars % beacon). Statistical maps are overlaid on a Talairach-transformed standard brain. Bar charts show mean conditionwise beta weights for the two clusters. L ! left; STG ! superior temporal gyrus; SMG ! supramarginal gyrus; fMRI ! functional magnetic resonance imaging.

across conditions does not indicate that the hippocampus is not involved in reorientation on floor trials, just that it is not significantly different from the walls condition. In addition, however, the floor trials did not result in significantly greater brain activation across any region in the contrasts with the other conditions, making it difficult to use the neural data to hypothesize about the cognitive processes involved. We suspect that the lower salience of the floor cue may have resulted in adults compensating with other strategies to remember the correct corner. For instance, they might have encoded the relative lengths of the floor on each side of a target corner (e.g., long wall on the left, short wall on the right), similar to the walls condition, but then relied more heavily on verbal coding to remember the floor lengths in the absence of highly salient visual stimuli (such as walls). More work will be necessary to understand how adults process the geometry of a Table 3 Clusters of Significant Activation in the Conjunction Analysis Talairach coordinates

Region L/R

Area

x

y

z

Size (mm3)

L L

Superior temporal gyrus Supramarginal gyrus

&42 &47

&32 &16

4 15

833 316

Note. Clusters of significant activation during the encoding phase are shown; there were no significant clusters during the retrieval phase. Coordinates indicate the location of peak activation for each cluster. L/R ! left/right.

stimulus flush with the ground, both in a virtual environment and in the standard laboratory task, however.

The Parahippocampal Place Area and the Presence of Walls The floor versus walls contrast did reveal differences in MTL activation in the posterior parahippocampal cortex, where clusters in this bilateral region showed greater activation for the walls condition, compared with the floor condition during encoding, and the left side showed a similar pattern during retrieval. These clusters appear very similar to the parahippocampal place area (PPA) described by Epstein and Kanwisher (1998). The PPA is sensitive to scene information, in particular the layout of fixed items within the scene (Epstein, 2005, 2008; Epstein & Kanwisher, 1998). The current findings further indicate that the PPA is differentially activated when an environment has vertical walls, even when the spatial processing required is held constant. As shown in Figure 4, the PPA showed the greatest activation on trials with vertical walls and the least activation on floor trials, and the pillars condition resulted in an intermediate amount of activation during encoding. This suggests that it is the presence and size of vertical elements in the environment that drives the PPA response. Recently, however, Galati, Pelle, Berthoz, and Committeri (2010) found that PPA was associated with judging an object’s distance from an environment feature that was perceived as relatively permanent, such as a building, rather than the distance to a moveable object, such as a ball. PPA activation, however, was not affected when the building was made invisible, and participants

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were required to judge the distance of the target to it based on memory, leading Galati et al. to suggest that the region was not responding to the immediate presence or absence of the building in the scene but to its stability for spatial coding. While Galati et al.’s distance estimation task and the current task are quite different, it could be that participants in our task view the walls as highly stable and the pillars as somewhat less stable. In such an account, the floor in the open field would be viewed as the least stable of the three types of cue. It is also possible that the PPA activation in Galati et al.’s task was driven by the size of the relatively large, vertical building, rather than its perceived stability. The current data do not distinguish between the stability and the verticality hypotheses, and furthermore, the two characteristics could interact to produce PPA activation. Nonetheless, these findings taken together with Galati et al. suggest an interesting sensitivity in PPA to aspects of the built environment in dynamic spatial tasks.

Language-Related Brain Regions and Geometric Cues The conjunction analysis revealed the left superior temporal (STG) and left supramarginal gyri (SMG) as areas of significantly greater activation for all the geometric conditions when compared with the beacon control. This result may seem puzzling at first, given that these regions are better known for their involvement in perceiving auditory information including speech (see Scott & Johnsrude, 2003, for a review). However, neuroimaging studies also support the view that STG and SMG are involved in the short-term subvocal retention of verbal information (e.g., Buchsbaum, Olsen, Kock, & Berman, 2005; Hickok, Buchsbaum, Humphries, & Muftuler, 2003; Koelsch et al., 2009; Postle, Berger, & D’Esposito, 1999). Interestingly, the present findings suggest these effects do not necessarily reflect echoic processes in which previously heard auditory information is being retained in auditory cortex; here, we hypothesize that they reflect an internally generated verbal code being used to support the retention of geometry-specific spatial information. Further support for the association of verbal coding with room geometry processing comes from the data of Sutton et al. (2010), in which the left inferior frontal gyrus, also believed to be involved in verbal coding (Devlin, Matthews, & Rushworth, 2003), was significantly more active during the encoding of geometric cues than during the encoding of feature cues. Therefore, brain activation data from two studies now point to a role for language in geometry processing during reorientation. The role of language in the reorientation task has been of interest for some time, but the current results and those of Sutton et al. (2010) are particularly noteworthy as the first to find evidence consistent with verbal coding of geometry cues in isolation and at the neural level. Most investigations have focused on the involvement of language when both a feature and informative geometry are present during reorientation. Hermer-Vazquez, Spelke, and Katsnelson (1999) proposed that adults, but not children before they produce the terms “left” and “right,” use language to combine the two types of cue (also see Hermer-Vazquez, Moffet, & Munkholm, 2001; Hyde et al., 2011). Language ability, therefore, was argued to be the crucial component to mature performance in the task. The same children were able to reorient using only geometry presumably because it is not languagemediated. Evidence for this claim was based on a verbal interfer-

ence paradigm, however, and subsequent tests have shown that both spatial and verbal interference disrupt mature reorientation performance (Hupbach, Hardt, Nadel, & Bohbot, 2007; Ratliff & Newcombe, 2008). A more recent case study with a 13-year-old deaf individual was inconclusive with respect to the reorientation task, as it was concluded that uncontrolled cues appeared to be influencing his performance (Hyde et al., 2011). Given that adults may use a verbal coding process when reorienting using geometry, this leads to questions about whether the use of language during the spatial coding process by adults is an epiphenomenon that is an addition to the type of processing used by children or a fundamentally new way of processing geometric information. We believe that it is likely the former and that adults may use verbal coding to aid all sorts of spatial memory tasks. In fact, it may be especially important in a situation in which the visual cues are subtle, such as when the walls or pillars are identical. Language may therefore be a component of mature performance with all cues in the reorientation task, though it is not strictly necessary. Moreover, a precise characterization of the mechanisms used by young children with limited spatial language, whose behavioral data using room geometry are similar to adults, deserves further investigation. The current study demonstrates how behavioral and neural methods can combine to inform hypotheses about how people navigate and, specifically, how they regain orientation. By developing a virtual analogue of the reorientation task for fMRI, more accurate information about the brain bases of reorientation is gained than by generalizing from widely different tasks. Furthermore, our fMRI data showed distinctions not detectable with behavioral measures, and those data can, in turn, be used to inform developmental findings. Still, we know little of how people use environment geometry in natural settings, and future work will be needed to address how the cues we isolate in the virtual task are weighted and combined to produce effective real-world navigation.

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Received September 1, 2011 Revision received February 15, 2012 Accepted February 16, 2012 "