Where is parahippocampal place area

To anticipate, our results show that the PPA is indeed sensitive to rectilinearity for arrays of 2-d shapes, but rectilinearity does not suffice to explain scene-sensitivity of the PPA.

Five subjects that participated in Experiment 1 also participated in Experiment 2 separated by around 3 months. All subjects that participated in Experiment 1 also participated in Experiment 3 during the same testing session, with Experiment 3 preceding Experiment 1. Subjects had normal or corrected-normal vision and had radiologically normal brains, without history of neuropsychological disorder. All participants provided written consent according to procedures approved by the University of Pennsylvania institutional review board.

Scanning was performed at the Hospital of the University of Pennsylvania using a 3T Siemens Trio scanner equipped with a channel head coil. High-resolution T1-weighted images for anatomical localization were acquired using a three-dimensional magnetization-prepared rapid acquisition gradient echo pulse sequence [repetition time TR , ms; echo time TE , 3.

Subjects viewed stimuli through a mirror attached to the head coil. Scan runs were divided into sixteen 15 s blocks; in each, subjects viewed 15 stimuli from the same condition presented one at a time for ms each followed by a ms interstimulus interval. The experimental blocks were interspersed with five 15 s fixation blocks in which a black fixation cross was presented at the middle of a uniform gray screen. Subject attention was maintained by asking them to perform a one-back image repetition detection task during the experimental blocks.

Stimulus repetitions occurred twice per block; thus, there were 52 unique stimuli per condition. Following the experimental runs, subjects completed two functional localizer runs in which they viewed scenes, objects, faces, and scrambled objects in separate blocks. Data from these runs were used to identify the location of the PPA and other scene-selective regions. These runs had the same length, design, timing, and task as the main experimental runs.

To replicate Nasr and colleagues finding of an effect of rectilinearity on PPA response during viewing of geometric shapes, subjects were presented with arrays of computer-generated 2D squares high-rectilinearity or circles low-rectilinearity; Figure 1A.

Because it is unknown whether the PPA rectilinearity bias depends on the spatial extent of the rectilinear edges, the squares and circles in the two shape arrays were generated at two different sizes large and small; Figure 1A.

Widths of squares and diameters of circles were ten times larger, on average, in the large shape conditions than the small shape conditions. Fifty two unique images were generated per condition. Each individual shape in an array in a given image was randomly assigned a gray-scale fill.

Figure 1. Stimulus conditions left and example right angle convolution intensities right for Experiments 1—3. A Experiment 1 stimuli consisted of squares high-rectilinearity and circles low-rectilinearity that were either large or small in size. B Experiment 2 stimuli consisted of naturalistic high- and low- rectilinearity scene and face images.

C Experiment 3 stimuli consisted of pixilated high-rectilinearity and pointillized low-rectilinearity scene and face images. Rectilinearity for each condition was calculated using the methods outlined in Nasr et al. Rather than using a square root function to produce curved filters, however, an absolute value function was used to produce angled filters.

Edges in the images were then extracted using Canny edge detection at a threshold of 0. Intensities from the resultant convolved matrix were averaged across edge points and orientations to generate orientation-invariant wavelet coefficients.

These coefficients were then normalized within spatial scale across the image set for each experiment by subtracting the minimum value within spatial scale and dividing by the range. The final rectilinearity index for each image was determined by averaging these normalized coefficients across the four spatial scales.

To test whether rectilinearity effects could be found for naturalistic stimuli, subjects were presented with grayscale images of faces and scenes that were grouped by rectilinearity Figure 1B. Specifically, 52 high-rectilinearity scenes, 52 low-rectilinearity scenes, 52 high-rectilinearity faces, and 52 low-rectilinearity faces were chosen from a larger image set faces; scenes based on their rectilinearity values.

Thus, response differences between faces and scenes could not be explained by differences in rectilinearity. To ensure that all stimuli had equal retinotopic extent, faces were displayed on a phase-scrambled variation of a single scene image, which was included in the rectilinearity calculation. All scene stimuli depicted natural outdoor scenes e. To further test whether the PPA and other scene regions are sensitive to the rectilinearity of naturalistic stimuli, we created a new set of grayscale images of natural faces and scenes, which had rectilinearity artificially enhanced or reduced.

These images were pseudorandomly drawn from the same image set as in Experiment 2 and from the SUN image database Xiao et al. The same images were presented to each participant. For each stimulus category, half of the images were decomposed into square pixels that were larger than the original pixels high-rectilinearity and half were decomposed into round points low-rectilinearity. The result of these manipulations is to shift the perceptual salience of high spatial frequency rectilinearity up or down, respectively Figure 1C.

Pixelated images were divided into pixels aligned by row and column across the image. Pointillized images consisted of imbricated circles to cover the full image. Pixels and points had edges or diameters, respectively, of 6 pixels each at display resolution size. Pixilation and pointillization was executed using Pixelmator Software v3.

There were 52 unique images generated per condition pixelated scene, pointillized scenes, pixelated faces, pointillized faces. Functional MR images for both the main experiments and functional localizer were preprocessed using the following steps.

First, they were corrected for differences in slice timing by resampling slices in time to match the first slice of each volume. Third, the timecourses for each voxel were high-pass filtered to remove low temporal frequency fluctuations in the BOLD signal that exceeded lengths of s.

Data from the functional localizer scan were smoothed with a 5 mm full-width at half-maximum FWHM Gaussian filter. We examined univariate responses within several regions of interest ROI known to be involved in visual processing. ROIs were defined individually for each subject using data from the functional localizer scans. Specifically, each ROI was defined as the top voxels in each hemisphere that exhibited the defining contrast and fell within the group-parcel mask for that ROI.

The voxels comprising each ROI did not need to be contiguous. This method ensured that all ROIs could be defined in both hemispheres in every subject and that all ROIs contained the same number of voxels. All ROIs were combined across hemispheres unless otherwise noted. All contrasts were performed in the native anatomical space for each subject and the group-parcel map was mapped into that space using a linear transformation. We then used general linear models GLMs implemented in FSL 1 to estimate the response of each voxel to the four experimental conditions for each experiment.

Each condition was modeled as a boxcar function convolved with a canonical hemodynamic response function. Subsequent comparisons between individual conditions were based on paired-sampled t- tests.

For tests of the rectilinearity hypothesis, significance was assessed using 1-tailed tests in the direction of the rectilinearity hypothesis i. For all other tests, significance was assessed using 2-tailed tests. In addition to the ROI analyses, we also performed a whole-brain group analysis to test for effects of rectilinearity and category in Experiments 2 and 3 outside of our ROIs. For this analysis, data from those subjects who participated in both Experiments 2 and 3 were first combined via a within-subject fixed-effects analysis prior to the group analysis.

In addition to univariate analyses, we also assessed whether there was information about rectilinearity and stimulus category represented in the multivoxel patterns of response in each ROI in Experiments 2 and 3. To do so, for each participant, we used GLMs to estimate the response pattern evoked by each stimulus condition separately for each of the two fMRI runs.

Multivoxel pattern analyses MVPA were then performed through split-half pattern comparison Haxby et al. Individual patterns were normalized prior to this computation by subtracting the grand mean pattern i. For each ROI, we then computed the correlation between the response patterns resulting from the same stimulus conditions and from different stimulus conditions. To test for coding of rectilinearity controlling for stimulus category, we computed a discrimination index that was the difference in the average correlation between the same rectilinearity condition and the corresponding different rectilinearity condition i.

This rectilinearity discrimination index was computed separately for scenes and faces. Likewise, to test for coding of stimulus category controlling for rectilinearity we computed a discrimination index that was the difference in the average correlation between the same category condition and the corresponding different category condition i.

This category discrimination index was computed separately for high and low rectilinearity stimulus conditions. To assess statistical significance, t -tests were used evaluate if the discrimination indices were greater than zero. In our first experiment, we sought to replicate the PPA rectilinearity bias for simple shapes reported by Nasr et al. To do so, we scanned participants while they viewed arrays of computer-generated gray-scale squares high-rectilinearity and circles low-rectilinearity presented at two different sizes small or large; Figure 1A.

Consistent with the rectilinearity hypothesis, the PPA responded more strongly to arrays of squares than to arrays of circles Figure 2. Figure 2. Results for Experiment 1. The parahippocampal place area PPA and occipital place area OPA both showed a significant main effect of shape, with greater overall responses to squares than circles.

The reason for this preference for large shapes is unclear. It may indicate a preference for larger objects Konkle and Oliva, , or it might be driven by uncontrolled variables such as spatial frequency or numerosity. After replicating the rectilinearity bias for shapes in the PPA, we next moved on to test whether there is a rectilinearity effect for naturalistic images, by scanning participants while they viewed images of high- and low-rectilinearity scenes and high- and low-rectilinearity faces Figure 1B.

Importantly, rectilinearity was matched between the scenes and faces; that is, the high-rectilinearity scenes and faces had a similar level of rectilinearity, as did the low-rectilinearity scenes and faces. Thus, our design not only allowed us to examine rectilinearity effects for scenes and faces, it also provided a strong test of the rectilinearity hypothesis. On the other hand, if the PPA responds strongly to scenes in part because it is tuned to scenes as a category, then it should continue to exhibit a preferential response to scenes even after rectilinear matching.

Results are plotted in Figure 3A. Figure 3. Univariate results for Experiments 2 and 3. No main effect of rectilinearity was observed in any ROI, although all ROIs showed a greater response to scenes than faces. Points that fall below the unity line are voxels with greater category-selectivity than rectilinearity-selectivity shown in purple , and points that fall above the unity line are voxels with greater rectilinearity-selectivity than category-selectivity show in red.

Few voxels exhibited greater rectilinearity-selectivity than scene-selectivity. The bottom row shows a histogram of rectilinearity- and category-selective voxels in each participant. In all subjects, the number of category-selective voxels far exceeded the number of rectilinearity-selective voxels. As in Experiment 2, few voxels exhibited greater rectilinearity-selectivity than scene-selectivity.

Further, in all participants the number of category-selective voxels again far exceeded the number of rectilinearity-selective voxels bottom row. We considered the possibility that a subregion in the vicinity of the PPA, but not the most scene-selective part of the region, might be selective for the presence of right angles. Such a subregion might not be included in the PPA as defined by the top voxels showing scene-selectivity in each hemisphere in the functional localizers.

To address this possibility, we compared rectilinearity-selectivity high vs. Figure 3B shows the results of this comparison. Only a small fraction of voxels was more rectilinearity-selective than scene selective.

Further, in each participant the number of category-selective voxels far exceeded the number of rectilinearity-selective voxels Figure 3B. Thus, it is unlikely that the failure to find rectilinearity-selectivity in the PPA was due to a bias in ROI definition induced by our analysis methods.

We also considered the possibility that the PPA might be sensitive to rectilinearity at the representational level. That is, even though the overall level of activity in PPA did not distinguish between high vs. Journal of Vision August , Vol.

Alerts User Alerts. What is the function of the parahippocampal place area? You will receive an email whenever this article is corrected, updated, or cited in the literature.

You can manage this and all other alerts in My Account. This feature is available to authenticated users only. Get Citation Citation. Get Permissions. Epstein, R. This area contained both upper and lower visual field representations. The maps are displayed on a flattened view of human visual cortex right hemisphere in two representative subjects. The black boundary indicates the location of high SF activity in the same subjects, based on the comparison between high SF and middle SF faces see Figure 5.

Classifying natural scenes based on their power spectra. In order to compare the power values across images, the high SF power in an image was normalized by the total power in that image.

A shows the percentage of the normalized high SF power for all images. Eight images were randomly selected from each scene class to be used in a blocked-design fMRI experiment. The location of mPPA on the inflated cortical surface. As expected from the flattened cortical maps, mPPA was located immediately ventral to mFFA, as shown on a magnified view of macaque posterior IT cortex in the right hemisphere.

Evidence for mPPA in the subject average, using additional control stimuli. In a separate experiment, we did a blocked-design comparison between places and single faces, objects, and body parts. In each stimulus block, multiple examples of each category were presented. The fMRI activity was measured in three macaque monkeys, and data from all monkeys were averaged using a random-effects model. The anatomical curvature pattern underlay in the maps was also averaged across subjects.

The right hemisphere is shown on the left. Topographic maps of SF sensitivity in macaque visual cortex. The maps show the pattern of activity produced by high SF versus low SF checkerboards.

The activity maps are displayed on a flattened view of macaque visual cortex left hemisphere [LH] and right hemisphere [RH]. The maps also reflect the large-scale central-versus-peripheral bias in SF sensitivity also shown in Figure S8 for human data , with an additional high SF extension into presumptive monkey TOS see Figure 8. Comparison between the FFT power spectra of building and face images. The power spectrum of each image was normalized by the power of the DC component.

Then the building-versus-face spectral map was generated by subtracting the averaged power spectrum of buildings from the averaged power spectrum of faces both in a decibel format.

Points near the center of the Fourier image correspond to low SFs. Abstract Defining the exact mechanisms by which the brain processes visual objects and scenes remains an unresolved challenge. Results The first sign of this lower-level selectivity arose serendipitously, when we were testing fMRI responses to very simple geometrical shapes including cubes and spheres.

Download: PPT. Figure 1. The higher-order cortical area PPA responds differentially to cubes versus spheres. Figure 2. PPA responses to a range of computer-generated 3-D shapes. Figure 3.

Figure 5. High-pass-filtered face images activate human PPA selectively. Figure 6. High-pass-filtered place images also activate human PPA selectively. Figure 7. PPA activity reflects the spectral variation in natural scenes. Figure 8. Evidence for a monkey homolog of PPA in individual maps. Figure 9. Figure Detection of high SFs is crucial for primate place perception. Materials and Methods Subjects Seven human subjects with normal or corrected-to-normal vision and three juvenile 5—7 kg male rhesus monkeys Macaca mulatta were tested, in several experimental sessions each.

Table 1. The root mean square contrast of SF-filtered stimuli. Supporting Information. Figure S1. Figure S2. Figure S3. Figure S4. Figure S5. Figure S6. Figure S7. Figure S8. Figure S9. Figure S References 1. M The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci — View Article Google Scholar 2. Tsao D. Y, Freiwald W. A, Knutsen T. A, Mandeville J. B, Tootell R. B Faces and objects in macaque cerebral cortex.

Nat Neurosci 6: — View Article Google Scholar 3. A, Tootell R. B, Livingstone M. S A cortical region consisting entirely of face-selective cells. Science — View Article Google Scholar 4. Epstein R, Kanwisher N A cortical representation of the local visual environment. Nature — View Article Google Scholar 5. Aguirre G. Neuron — View Article Google Scholar 6. Downing P. View Article Google Scholar 7. Grossman E. D, Blake R Brain areas active during visual perception of biological motion.

View Article Google Scholar 8. Martin A, Wiggs C. L, Ungerleider L. G, Haxby J. V Neural correlates of category-specific knowledge. View Article Google Scholar 9. Chao L. L, Haxby J. V, Martin A Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nat Neurosci 2: — View Article Google Scholar Brain — Ishai A, Ungerleider L. G, Martin A, Schouten J. V Distributed representation of objects in the human ventral visual pathway.

A Macaque inferior temporal neurons are selective for three-dimensional boundaries and surfaces. Kayaert G, Biederman I, Vogels R Representation of regular and irregular shapes in macaque inferotemporal cortex. Cereb Cortex — Schwartz E.

L, Desimone R, Albright T. D, Gross C. G Shape recognition and inferior temporal neurons. Kobatake E, Tanaka K Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. J Neurophysiol — Brincat S. L, Connor C.

E Underlying principles of visual shape selectivity in posterior inferotemporal cortex. Nat Neurosci 7: — J Cogn Neurosci — Sigala N, Logothetis N. K Visual categorization shapes feature selectivity in the primate temporal cortex. Koida K, Komatsu H Effects of task demands on the responses of color-selective neurons in the inferior temporal cortex.

Nat Neurosci — DiCarlo J. J, Maunsell J. H Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. Wilkinson F, James T. W, Wilson H. R, Gati J. S, Menon R. S, et al.