In human and non-human primates (NHPs), fMRI has been used for many decades to localize the cortical regions that are preferentially involved in scene perception (Epstein and Kanwisher, 1998; Tsao et al., 2008; Rajimehr et al., 2009; Nasr et al., 2011). Early studies focused mainly on larger activity sites that were more easily reproducible across sessions and individuals, ignoring smaller sites that were not detectable in all subjects and/or were not reproducible across scan sessions, based on the techniques available at that time. This led to relatively simple models of neuronal processing solely based on larger visual areas.

These models suggested three scene-selective areas within the human visual cortex, with possible homologs in NHPs (Nasr et al., 2011; Kornblith et al., 2013; Li et al., 2022). The human cortical areas were originally named parahippocampal place area (PPA) (Epstein and Kanwisher, 1998), retrosplenial cortex (RSC) (Maguire, 2001), and transverse occipital sulcus (TOS) (Grill-Spector, 2003), based on the local anatomical landmarks. However, subsequent studies noticed the discrepancy between the location of these functionally defined areas and the anatomical landmarked, and instead named those regions temporal place area (TPA), medial place area (MPA), and occipital place area (OPA) (Nasr et al., 2011; Dilks et al., 2013; Silson et al., 2016).

The idea that scene-selective areas are limited to these three regions is based largely on group-averaged activity maps, generated after applying large surface/volume-based smoothing to the data from individual subjects. In such group-averaged data, originally based on fixed- rather than random-effects, thresholds tended to be high to reduce the impact of nuisance artifacts (Nasr et al., 2011). Thus, though well founded, this approach conceivably may not have identified smaller scene-selective areas (Figure 1A).

Figure 1

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However, at the single-subject level, multiple smaller scene-selective sites can be detected outside these scene-selective areas, especially when drastic spatial smoothing is avoided (Figure 1B). This phenomenon is highlighted in a recent neuroimaging study in NHPs (Li et al., 2022) in which the authors took advantage of high-resolution neuroimaging techniques using implanted head coils. Their findings suggested that scene-selective areas are likely not limited to the three expected sites, and that other, smaller, scene-selective areas may also be detected across the brain. Still, the reliability in the detection of these smaller sites, their spatial consistency across large populations, and their specific role in scene perception that distinguishes them from the other scene-selective areas remain unclear.

Here, we used conventional (based on a 3T scanner) and high-resolution (based on a 7T scanner) fMRI to localize and study additional scene-selective site(s) that were detected outside PPA/TPA, RSC/MPA, and TOS/OPA. We focused our efforts on the posterior portion of the intraparietal cortex mainly because multiple previous studies reported indirect evidence for scene and/or scene-related information processing within this region (Lescroart and Gallant, 2019; Pitzalis et al., 2020; Sulpizio et al., 2020; Park et al., 2022). Consistent with these studies, we found at least one additional scene-selective area within the posterior intraparietal gyrus, adjacent to the motion-selective area V6 (Pitzalis et al., 2010). This site was termed PIGS, reflecting its location (posterior intraparietal gyrus) and function (scene-selectivity). PIGS was detected consistently across individual subjects and populations and localized reliably across scan sessions. Besides its distinct location relative to two major anatomical landmarks (i.e., intraparietal sulcus [IPS] and parieto-occipital sulci [POS]) and the retinotopic visual areas (IPS0-4) that distinguishes it from other scene-selective areas (e.g., TOS/OPA and RSC/MPA), PIGS showed sensitivity to ego-motion within naturalistic visual scenes, a phenomenon not detectable in other scene-selective areas.

This study consists of seven experiments. Experiment 1 focused on localizing the scene-selective site (PIGS) within the posterior intraparietal region. Experiment 2 showed consistency in the spatial location of PIGS across sessions. Experiment 3 examined PIGS location relative to V6, an area involved in motion coherency and optic flow encoding, and also relative to the retinotopic visual areas IPS0-4. Experiment 4 showed that, despite its small size, PIGS is detectable in group-averaged maps in large populations. Experiment 5 showed that scenes and non-scene objects are differentiable from each other based on the evoked response evoked within PIGS. Experiment 6 tested the response in PIGS to ego-motion in scenes, yielding a result that differentiated PIGS from the other scene-selective regions. Finally, Experiment 7 showed that PIGS does not respond selectively to biological motion.

Experiment 1: Small scene-selective sites are detectable within the posterior intraparietal gyrus

When the level of spatial smoothing is relatively low, scene-selective sites (other than PPA/TPA, TOS/OPA, and RSC/MPA) are detectable across the brain, especially within the posterior intraparietal gyrus (Figure 1B). To test the consistency in location of these scene-selective sites across individuals, 14 subjects were presented with scene and face stimuli while we collected their fMRI activity. Considering the expected small size of the scene-selective sites within the intraparietal region, we used limited signal smoothing in our analysis (FWHM = 2 mm; see ‘Methods’) to increase the chance of detecting these sites.

Figure 2 shows the activity maps evoked by the ‘scenes > faces’ contrast in seven exemplar subjects. All activity maps were overlaid on a common brain template to clarify the consistency in the location of scene-selective sites across individuals. In all tested individuals, besides areas RSC/MPA and TOS/OPA, we detected at least one scene-selective site within the posterior portion of the intraparietal gyrus, close to (but outside) the POS. Accordingly, we named this site the posterior interparietal gyrus scene-selective site (PIGS).

Figure 2

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When measured at the same threshold levels (p<10^–2), the relative size of PIGS was 73.86% ± 49.01% (mean ± SD) of RSC/MPA, 28.26% ± 15.67% of TOS/OPA, and 19.45% ± 8.43% of PPA/TPA. Considering the proximity of PIGS to the skull and head coil surface (Figure 1), the relatively small size of PIGS could not be ascribed to the lower signal-/contrast-to-noise ratio in that region.

To better clarify the consistency of PIGS localization across subjects, we also generated group-averaged activity maps based on random-effects, and after correction for multiple comparisons. As demonstrated in Figure 3A, PIGS was also detectable in the group-averaged activity maps, in almost the same location as in the individual subject maps. Overall, these results suggest that, despite the relatively small size of this scene-selective site, PIGS is consistently detectable across subjects in the same cortical location.

Figure 3

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Experiment 2: PIGS reproducibility across scan sessions

To test the reproducibility of our results, four subjects were selected randomly from those who participated in Experiment 1. These subjects were scanned again (on a different day) using a 7T (rather than a 3T) scanner and a different set of scenes and faces (Figure 4A).

Figure 4

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As demonstrated in Figure 4, despite utilizing a different scanner and a different set of stimuli, PIGS was still detectable in the same location (Figure 4B–D). Here again, PIGS was localized within the posterior portion of the intraparietal gyrus and close to the posterior lip of POS. Considering the higher contrast/signal-to-noise ratio of 7T (compared to 3T) scans, this result strongly suggested that the PIGS evidence was not simply a nuisance artifact in fMRI measurements.

Experiment 3: Localization of areas PIGS vs. V6 and retinotopic visual areas

Posterior intraparietal cortex accommodates area V6, which is involved in motion coherency (optic-flow) encoding (Pitzalis et al., 2010). Recent studies have suggested that scene stimuli evoke a strong response within V6 (Sulpizio et al., 2020). Moreover, the intraparietal cortex accommodates multiple retinotopically organized visual areas (Swisher et al., 2007), including IPS0-4 that are believed to be involved in spatial attention control and higher-level object information processing (Silver et al., 2005; Konen and Kastner, 2008). Previous studies have suggested that the area TOS/OPA overlaps with the retinotopic visual areas V3A/B and IPS0 (V7) (Nasr et al., 2011; Silson et al., 2016). In Experiment 3, we clarified the location of PIGS relative to these regions.

In Experiment 3a, we localized V6 in all subjects who participated in Experiment 1, based on visual presentation of random vs. radially moving dots (see ‘Methods’). Figure 4D shows the co-localization of V6 and PIGS in four individual subjects. Consistent with previous studies (Pitzalis et al., 2010; Pitzalis et al., 2015), V6 was localized within the posterior portion of the POS without any overlap between its center and PIGS. To test the relative localization of these two regions at the group level, we generated probabilistic labels for PIGS and V6 (see ‘Methods’). As demonstrated in Figure 5, the probabilistic label for PIGS was localized within the intraparietal gyrus and outside the POS (Figure 5A), while V6 was located within the POS (Figure 5B). We also did not find any overlap between area V6 and areas RSC/MPA and TOS/OPA (Figure 5C). Thus, despite the low threshold level used to generate these labels (probability >20%), the areas PIGS and V6 were located side-by-side (Figure 5D), without any overlapping between their centers.

Figure 5

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In Experiment 3b, we scanned two subjects, randomly selected from those who had participated in Experiment 2, using a 7T scanner to map the borders of retinotopic visual areas (see ‘Methods’). As demonstrated in Figure 6, in both subjects, PIGS was located adjacent to IPS3 and IPS4. In comparison, TOS/OPA was located more ventrally relative to PIGS, overlapping with areas V3A/B and IPS0 (V7). Considering these differences in the localization of PIGS vs. TOS/OPA, relative to the anatomical and functionally defined landmarks, our results further suggest that PIGS and TOS/OPA are two distinct visual areas.

Figure 6

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Experiment 4: PIGS localization in a larger population

The results of Experiments 1–3 suggest that PIGS can be localized consistently across individual subjects, and this area appears to be distinguishable from the adjacent area V6. However, considering the small size of this area, it appears necessary to test whether this area was detectable based on group averaging in a larger population. Accordingly, in Experiment 4 we scanned 31 individuals (other than those who participated in Experiments 1–3) while they were presented with the same stimuli as in Experiment 1 (Figure 2).

As demonstrated in Figure 3B, PIGS was also detectable in this new population in almost the same location as in Experiment 1. Specifically, PIGS was detected bilaterally within the posterior portion of the intraparietal gyrus, adjacent to the POS. We did not find a significant difference between the two populations in the size of PIGS when normalized either relative to the size of RSC/MPA (t(43) = 0.98, p=0.33), or TOS/OPA (t(43) = 0.26, p=0.80) or PPA/TPA (t(43) = 0.52, p=0.61). Thus, the location and relative size of PIGS appeared to remain unchanged across populations.

These results suggest that one may rely on the probabilistically generated labels to examine the evoked activity within PIGS. To test this hypothesis, we measured the level of scene-selective activity in PIGS, along with the areas TOS/OPA, RSC/MPA, and V6, using the probabilistic labels generated based on the results of Experiments 1 and 3a (see ‘Methods’ and Figure 5). As demonstrated in Figure 7A and B, results of this region of interest (ROI) analysis showed a significant scene-selective activity in PIGS (t(31) = 8.11, p<10^–8), TOS/OPA (t(31) = 7.91, p<10^–7), and RSC/MPA (t(31) = 9.11, p<10^–8). Importantly, despite the proximity of PIGS and V6, the level of scene-selective activity in PIGS was significantly higher than that in V6 (t(11) = 5.03, p<10^–4). Thus, it appears that the probabilistically generated ROIs can be used to examine PIGS response and differentiate it from adjacent areas such as V6 (see also Experiment 5).

Figure 7

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Experiment 5: Selective response to scenes compared to non-scene objects in PIGS

Thus far, we localized PIGs in multiple experiments by contrasting the response evoked by scenes vs. faces. In Experiments 5a and 5b, we examined whether PIGS also showed a selective response to scenes compared to objects (not just faces). In Experiment 5a, 12 individuals, other than those who participated in Experiments 1–3, were scanned while viewing pictures of scenes (other than those used to localize PIGS) and everyday objects (Figure 8A; see Methods).

Figure 8

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As demonstrated in Figure 8B and C for one individual subject, ‘scenes vs. objects’ and ‘scenes vs. faces’ (Experiment 4) contrasts generated similar activity maps. Importantly, in both maps, PIGS was detectable in a consistent location adjacent to (but outside) the POS. Moreover, results of an ROI analysis, using the probabilistically generated labels based on the results of Experiments 1 and 3a, yielded significant scene-selective activity within PIGS (t(11) = 6.57, p<10^–4), RSC/MPA (t(12) = 11.00, p<10^–6), and TOS/OPA (t(12) = 6.26, p<10^–3) (Figure 9A and B). We also found that the level of scene-selective activity within PIGS is significantly higher than that in the adjacent area V6 (t(11) = 2.42, p=0.03). Thus, scenes and (non-face) objects are differentiable from each other, based on the activity evoked within PIGS.

Figure 9

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In Experiment 5b, 15 individuals (other than those who participated in Experiments 1 and 5a), were scanned while viewing a new set of stimuli that included pictures of scenes, faces, everyday objects, and scrambled objects (Figure 8D). In contrast to Experiment 5a in which the number of objects within each image could vary, here, each image contained only one object (see ‘Methods’). Despite this change, contrasting the response to scene vs. non-scene images (averaged over objects, scrambled objects, and faces) evoked a similar activity pattern, as scene vs. faces (Figure 8E and F). Moreover, the ROI analysis yielded a significant scene-selective activity within PIGS (t(14) = 2.37, p=0.03), RSC/MPA (t(14) = 10.33, p<10^–7), and TOS/OPA (t(14) = 4.79, p<10^–3) (Figure 9). Here again, the level of scene-selective activity within PIGS was higher than V6 (t(14) = 2.27, p=0.04). Together, results of Experiments 1–5 suggest that PIGS responds selectively to a wide range of scenes compared to non-scene objects, and that the level of this activity is higher than in the adjacent area V6.

Experiment 6: PIGS response to ego-motion

Experiments 1–5 clarified the location of PIGS and its general functional selectivity for scenes. However, a more specific role of this area in scene perception remains undefined. Experiment 6 tested the hypothesis that area PIGS is involved in encoding ego-motion within scenes. This hypothesis was motivated by the fact that PIGS is located adjacent to V6 (Figure 5D), an area involved in encoding optic flow. Other studies have also suggested that ego-motion may influence the scene-selective activity within this region, without clarifying whether this activity was centered either within or outside V6 (Pitzalis et al., 2020; Sulpizio et al., 2020).

Twelve individuals, from those who participated in Experiment 1, took part in this experiment (see ‘Methods’). These subjects were presented with coherently changing scene stimuli that implied ego-motion across different outdoor trails (Figure 10). In separate blocks, they were also presented with incoherently changing scenes and faces. Figure 9 shows the group-averaged scene-selective activity, evoked by coherently (Figure 11A) and incoherently changing scene stimuli (Figure 11B). Consistent with our hypothesis, PIGS showed a significantly stronger response (bilaterally) to coherently (compared to incoherently) changing scenes that implied ego-motion (Figure 11C). However, the level of activity within RSC/MPA and TOS/OPA did not change significantly between these two conditions.

Figure 10

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Figure 11

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Consistent with the group-averaged activity maps, results of an ROI analysis (Figure 12) yielded a significantly stronger response to coherently (vs. incoherently) changing scenes in PIGS (t(11) = 5.97, p<10^–4) but not in RSC/MPA (t(11) = 0.12, p=0.90) and TOS/OPA (t(11) = 0.48, p=0.64). Interestingly, area PPA/TPA showed a stronger response to incoherently (compared to coherently) changing scenes (t(11) = 3.48, p<0.01). To better clarify the difference between scene-selective areas, we repeated this test by applying a one-way repeated measures ANOVA to the differential response to ‘coherently vs. incoherently changing scenes’, measured across these four scene-selective areas. This test yielded a significant effect of area on the evoked differential activity (F(3, 11) = 53.89, p<10^–10). Post hoc analysis, with Bonferroni correction, showed that the level of differential activity evoked by ‘coherently vs. incoherently changing scenes’ was significantly higher within PIGS than all other scene-selective areas (p<10^–6). These results suggest a distinctive role for area PIGS in ego-motion encoding, which differentiates it from the other scene-selective areas. The absence of activity modulation in the other scene-selective areas also ruled out the possibility that the activity increase in PIGS was simply due to attentional modulation during coherently vs. incoherently changing scenes (see ‘Discussion’).

Figure 12

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In addition to PIGS, we also found a significantly stronger response to coherently (rather than incoherently) changing scenes in area V6 (t(11) = 3.57, p<0.01). However, the level of this selectivity was significantly weaker in V6 compared to that in PIGS (t(11) = 2.63, p=0.02). Moreover, in the group-averaged activity maps, the contrast between coherently vs. incoherently changing scenes yielded a stronger response outside (rather than inside) the POS and also in area MT, located at the tip of medial temporal sulcus (Figure 11C). Together, these results suggest that the impact of ego-motion on scene processing is stronger in PIGS than that in V6.

In the same session (but different runs), we also tested the selectivity of the PIGS response for simpler forms of motion. In different blocks, subjects were presented with radially moving vs. stationary concentric rings (see ‘Methods’). Consistent with the previous studies of motion perception (Pitzalis et al., 2010; Korkmaz Hacialihafiz and Bartels, 2015), the results of an ROI analysis here did not yield any strong (significant) motion-selective activity within PIGS (t(11) = 1.84, p=0.10), RSC/MPA (t(11) = 1.97, p=0.08), PPA/TPA (t(11) = 1.93, p=0.08), and V6 (t(11) = 2.03, p=0.07). In contrast, we found strong motion selectivity within area TOS/OPA (t(11) = 4.57, p<10^–3), likely due to its overlap with the motion-selective area V3A/B (Nasr et al., 2011). Thus, in contrast to optic flow and ego-motion, simpler forms of motion only evoke weak-to-no selective activity within PIGS and V6.

Experiment 7: PIGS response to biological motion

The results of Experiment 6 showed that PIGS responds selectively to ego-motion in scenes, but not strongly to radially moving rings. However, it could be argued that PIGS may also respond to the other types of complex motion, for example, biological motion. To test this hypothesis, we measured the PIGS response to biological vs. translational motion in 12 subjects (see ‘Methods’). As illustrated in Figure 13, and consistent with the previous studies of biological motion (Puce et al., 1998; Beauchamp et al., 2003; Puce and Perrett, 2003; Pelphrey et al., 2005; Jastorff and Orban, 2009; Kamps et al., 2016), biological motion evoked a stronger response bilaterally within area MT and superior temporal sulcus but not within the posterior intraparietal gyrus. Consistent with the maps, an ROI analysis (based on the functionally defined labels) showed no significant difference between the response to biological vs. translational motion within PIGS (t(11) = 1.27, p=0.23), TOS/OPA (t(11) = 1.63, p=0.13), RSC/MPA (t(11) = 1.40, p=0.18), and PPA/TPA (t(11) = 0.41, p=0.69). These results indicated that PIGS does not respond to all types of complex motion.

Figure 13

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These data suggest that selective scene processing is not limited to areas PPA/TPA, RSC/MPA, and TOS/OPA, and that additional smaller scene-selective sites can also be found across the visual system. By focusing on one small scene-selective site, we showed that this site (PIGS) was consistently identifiable across individuals and groups. We also showed that inclusion of this site in the models of scene processing may clarify how ego-motion influences scene perception.

The stimuli, significance maps, generated probabilistic labels, and the measured activity across ROIs, as presented in Figures 1-13, are already available to the public (https://doi.org/10.5061/dryad.xwdbrv1mj; see also https://mesovision.martinos.org/datasets). MATLAB (RRID:SCR_001622; https://www.mathworks.com); FreeSurfer (RRID:SCR_001847; https://surfer.nmr.mgh.harvard.edu/fswiki/FsFast); and Psychophysics Toolbox (RRID:SCR_002881; http://psychtoolbox.org/docs/Psychtoolbox).

1. 1. Nasr S

   (2024) Dryad Digital Repository

   A previously undescribed scene-selective site is the key to encoding ego-motion in naturalistic environments.

   https://doi.org/10.5061/dryad.xwdbrv1mj

1. 1. Adams WJ
   2. Elder JH
   3. Graf EW
   4. Leyland J
   5. Lugtigheid AJ
   6. Muryy A

   (2016) The southampton-york natural scenes (syns) dataset: Statistics of surface attitude

   Scientific Reports 6:35805.

   https://doi.org/10.1038/srep35805

   • PubMed
   • Google Scholar
2. 1. Albright TD
   2. Desimone R
   3. Gross CG

   (1984) Columnar organization of directionally selective cells in visual area MT of the macaque

   Journal of Neurophysiology 51:16–31.

   https://doi.org/10.1152/jn.1984.51.1.16

   • PubMed
   • Google Scholar
3. 1. Baldauf D
   2. Desimone R

   (2014) Neural mechanisms of object-based attention

   Science 344:424–427.

   https://doi.org/10.1126/science.1247003

   • PubMed
   • Google Scholar
4. 1. Bar M
   2. Aminoff E
   3. Schacter DL

   (2008) Scenes unseen: the parahippocampal cortex intrinsically subserves contextual associations, not scenes or places per se

   The Journal of Neuroscience 28:8539–8544.

   https://doi.org/10.1523/JNEUROSCI.0987-08.2008

   • PubMed
   • Google Scholar
5. 1. Beauchamp MS
   2. Lee KE
   3. Haxby JV
   4. Martin A

   (2003) FMRI responses to video and point-light displays of moving humans and manipulable objects

   Journal of Cognitive Neuroscience 15:991–1001.

   https://doi.org/10.1162/089892903770007380

   • PubMed
   • Google Scholar
6. 1. Behrmann M
   2. Geng JJ
   3. Shomstein S

   (2004) Parietal cortex and attention

   Current Opinion in Neurobiology 14:212–217.

   https://doi.org/10.1016/j.conb.2004.03.012

   • PubMed
   • Google Scholar
7. 1. Benson NC
   2. Jamison KW
   3. Arcaro MJ
   4. Vu AT
   5. Glasser MF
   6. Coalson TS
   7. Van Essen DC
   8. Yacoub E
   9. Ugurbil K
   10. Winawer J
   11. Kay K

   (2018) The human connectome project 7 tesla retinotopy dataset: Description and population receptive field analysis

   Journal of Vision 18:23.

   https://doi.org/10.1167/18.13.23

   • PubMed
   • Google Scholar
8. 1. Brainard DH

   (1997)

   The psychophysics toolbox

   Spatial Vision 10:433–436.

   • PubMed
   • Google Scholar
9. 1. Coggan DD
   2. Tong F

   (2023) Spikiness and animacy as potential organizing principles of human ventral visual cortex

   Cerebral Cortex 33:8194–8217.

   https://doi.org/10.1093/cercor/bhad108

   • PubMed
   • Google Scholar
10. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical surface-based analysis

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • PubMed
    • Google Scholar
11. 1. De Martino F
    2. Zimmermann J
    3. Muckli L
    4. Ugurbil K
    5. Yacoub E
    6. Goebel R

    (2013) Cortical depth dependent functional responses in humans at 7T: improved specificity with 3D GRASE

    PLOS ONE 8:e60514.

    https://doi.org/10.1371/journal.pone.0060514

    • PubMed
    • Google Scholar
12. 1. Dilks DD
    2. Julian JB
    3. Paunov AM
    4. Kanwisher N

    (2013) The occipital place area is causally and selectively involved in scene perception

    The Journal of Neuroscience 33:1331–6a.

    https://doi.org/10.1523/JNEUROSCI.4081-12.2013

    • PubMed
    • Google Scholar
13. 1. Engel SA
    2. Glover GH
    3. Wandell BA

    (1997) Retinotopic organization in human visual cortex and the spatial precision of functional MRI

    Cerebral Cortex 7:181–192.

    https://doi.org/10.1093/cercor/7.2.181

    • PubMed
    • Google Scholar
14. 1. Epstein R
    2. Kanwisher N

    (1998) A cortical representation of the local visual environment

    Nature 392:598–601.

    https://doi.org/10.1038/33402

    • PubMed
    • Google Scholar
15. 1. Fischl B
    2. Sereno MI
    3. Dale AM

    (1999) Cortical surface-based analysis II: inflation, flattening, and a surface-based coordinate system

    NeuroImage 9:195–207.

    https://doi.org/10.1006/nimg.1998.0396

    • PubMed
    • Google Scholar
16. 1. Fischl B
    2. Salat DH
    3. Busa E
    4. Albert M
    5. Dieterich M
    6. Haselgrove C
    7. van der Kouwe A
    8. Killiany R
    9. Kennedy D
    10. Klaveness S
    11. Montillo A
    12. Makris N
    13. Rosen B
    14. Dale AM

    (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain

    Neuron 33:341–355.

    https://doi.org/10.1016/s0896-6273(02)00569-x

    • PubMed
    • Google Scholar
17. 1. Fischl B

    (2012) FreeSurfer

    NeuroImage 62:774–781.

    https://doi.org/10.1016/j.neuroimage.2012.01.021

    • PubMed
    • Google Scholar
18. 1. Friston KJ
    2. Holmes AP
    3. Price CJ
    4. Büchel C
    5. Worsley KJ

    (1999) Multisubject fMRI studies and conjunction analyses

    NeuroImage 10:385–396.

    https://doi.org/10.1006/nimg.1999.0484

    • PubMed
    • Google Scholar
19. 1. Greve DN
    2. Fischl B

    (2009) Accurate and robust brain image alignment using boundary-based registration

    NeuroImage 48:63–72.

    https://doi.org/10.1016/j.neuroimage.2009.06.060

    • PubMed
    • Google Scholar
20. 1. Grill-Spector K

    (2003) The neural basis of object perception

    Current Opinion in Neurobiology 13:159–166.

    https://doi.org/10.1016/s0959-4388(03)00040-0

    • PubMed
    • Google Scholar
21. 1. Harel A
    2. Kravitz DJ
    3. Baker CI

    (2013) Deconstructing visual scenes in cortex: gradients of object and spatial layout information

    Cerebral Cortex 23:947–957.

    https://doi.org/10.1093/cercor/bhs091

    • PubMed
    • Google Scholar
22. 1. Holt DJ
    2. Cassidy BS
    3. Yue X
    4. Rauch SL
    5. Boeke EA
    6. Nasr S
    7. Tootell RBH
    8. Coombs G

    (2014) Neural correlates of personal space intrusion

    The Journal of Neuroscience 34:4123–4134.

    https://doi.org/10.1523/JNEUROSCI.0686-13.2014

    • PubMed
    • Google Scholar
23. 1. Jastorff J
    2. Orban GA

    (2009) Human functional magnetic resonance imaging reveals separation and integration of shape and motion cues in biological motion processing

    The Journal of Neuroscience 29:7315–7329.

    https://doi.org/10.1523/JNEUROSCI.4870-08.2009

    • PubMed
    • Google Scholar
24. 1. Jones CM
    2. Byland J
    3. Dilks DD

    (2023) The occipital place area represents visual information about walking, not crawling

    Cerebral Cortex 33:7500–7505.

    https://doi.org/10.1093/cercor/bhad055

    • PubMed
    • Google Scholar
25. 1. Kamps FS
    2. Lall V
    3. Dilks DD

    (2016) The occipital place area represents first-person perspective motion information through scenes

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 83:17–26.

    https://doi.org/10.1016/j.cortex.2016.06.022

    • PubMed
    • Google Scholar
26. 1. Kanwisher N
    2. McDermott J
    3. Chun MM

    (1997) The fusiform face area: a module in human extrastriate cortex specialized for face perception

    The Journal of Neuroscience 17:4302–4311.

    https://doi.org/10.1523/JNEUROSCI.17-11-04302.1997

    • PubMed
    • Google Scholar
27. 1. Kennedy B
    2. Bex P
    3. Hunter DG
    4. Nasr S

    (2023) Two fine-scale channels for encoding motion and stereopsis within the human magnocellular stream

    Progress in Neurobiology 220:102374.

    https://doi.org/10.1016/j.pneurobio.2022.102374

    • PubMed
    • Google Scholar
28. 1. Konen CS
    2. Kastner S

    (2008) Two hierarchically organized neural systems for object information in human visual cortex

    Nature Neuroscience 11:224–231.

    https://doi.org/10.1038/nn2036

    • PubMed
    • Google Scholar
29. 1. Konkle T
    2. Oliva A

    (2012) A real-world size organization of object responses in occipitotemporal cortex

    Neuron 74:1114–1124.

    https://doi.org/10.1016/j.neuron.2012.04.036

    • PubMed
    • Google Scholar
30. 1. Koopmans PJ
    2. Barth M
    3. Norris DG

    (2010) Layer-specific BOLD activation in human V1

    Human Brain Mapping 31:1297–1304.

    https://doi.org/10.1002/hbm.20936

    • PubMed
    • Google Scholar
31. 1. Korkmaz Hacialihafiz D
    2. Bartels A

    (2015) Motion responses in scene-selective regions

    NeuroImage 118:438–444.

    https://doi.org/10.1016/j.neuroimage.2015.06.031

    • PubMed
    • Google Scholar
32. 1. Kornblith S
    2. Cheng X
    3. Ohayon S
    4. Tsao DY

    (2013) A network for scene processing in the macaque temporal lobe

    Neuron 79:766–781.

    https://doi.org/10.1016/j.neuron.2013.06.015

    • PubMed
    • Google Scholar
33. 1. Lescroart MD
    2. Gallant JL

    (2019) Human scene-selective areas represent 3d configurations of surfaces

    Neuron 101:178–192.

    https://doi.org/10.1016/j.neuron.2018.11.004

    • PubMed
    • Google Scholar
34. 1. Levy I
    2. Hasson U
    3. Avidan G
    4. Hendler T
    5. Malach R

    (2001) Center-periphery organization of human object areas

    Nature Neuroscience 4:533–539.

    https://doi.org/10.1038/87490

    • PubMed
    • Google Scholar
35. 1. Li X
    2. Zhu Q
    3. Vanduffel W

    (2022) Submillimeter fMRI reveals an extensive, fine-grained and functionally-relevant scene-processing network in monkeys

    Progress in Neurobiology 211:102230.

    https://doi.org/10.1016/j.pneurobio.2022.102230

    • PubMed
    • Google Scholar
36. 1. Maguire EA

    (2001) The retrosplenial contribution to human navigation: a review of lesion and neuroimaging findings

    Scandinavian Journal of Psychology 42:225–238.

    https://doi.org/10.1111/1467-9450.00233

    • PubMed
    • Google Scholar
37. 1. Nasr S
    2. Liu N
    3. Devaney KJ
    4. Yue X
    5. Rajimehr R
    6. Ungerleider LG
    7. Tootell RBH

    (2011) Scene-selective cortical regions in human and nonhuman primates

    The Journal of Neuroscience 31:13771–13785.

    https://doi.org/10.1523/JNEUROSCI.2792-11.2011

    • PubMed
    • Google Scholar
38. 1. Nasr S
    2. Tootell RBH

    (2012a) A cardinal orientation bias in scene-selective visual cortex

    The Journal of Neuroscience 32:14921–14926.

    https://doi.org/10.1523/JNEUROSCI.2036-12.2012

    • PubMed
    • Google Scholar
39. 1. Nasr S
    2. Tootell RBH

    (2012b) Role of fusiform and anterior temporal cortical areas in facial recognition

    NeuroImage 63:1743–1753.

    https://doi.org/10.1016/j.neuroimage.2012.08.031

    • PubMed
    • Google Scholar
40. 1. Nasr S
    2. Echavarria CE
    3. Tootell RBH

    (2014) Thinking outside the box: rectilinear shapes selectively activate scene-selective cortex

    The Journal of Neuroscience 34:6721–6735.

    https://doi.org/10.1523/JNEUROSCI.4802-13.2014

    • PubMed
    • Google Scholar
41. 1. Nasr S
    2. Polimeni JR
    3. Tootell RBH

    (2016a) Interdigitated color- and disparity-selective columns within human visual cortical areas v2 and v3

    The Journal of Neuroscience 36:1841–1857.

    https://doi.org/10.1523/JNEUROSCI.3518-15.2016

    • PubMed
    • Google Scholar
42. 1. Nasr S
    2. Rosas HD

    (2016b) Impact of visual corticostriatal loop disruption on neural processing within the parahippocampal place area

    The Journal of Neuroscience 36:10456–10471.

    https://doi.org/10.1523/JNEUROSCI.0741-16.2016

    • PubMed
    • Google Scholar
43. 1. O’Craven KM
    2. Downing PE
    3. Kanwisher N

    (1999) fMRI evidence for objects as the units of attentional selection

    Nature 401:584–587.

    https://doi.org/10.1038/44134

    • PubMed
    • Google Scholar
44. 1. Park J
    2. Josephs E
    3. Konkle T

    (2022) Ramp-shaped neural tuning supports graded population-level representation of the object-to-scene continuum

    Scientific Reports 12:18081.

    https://doi.org/10.1038/s41598-022-21768-2

    • PubMed
    • Google Scholar
45. 1. Pelli DG

    (1997) The videotoolbox software for visual psychophysics: Transforming numbers into movies

    Spatial Vision 10:437–442.

    https://doi.org/10.1163/156856897X00366

    • PubMed
    • Google Scholar
46. 1. Pelphrey KA
    2. Morris JP
    3. Michelich CR
    4. Allison T
    5. McCarthy G

    (2005) Functional anatomy of biological motion perception in posterior temporal cortex: an FMRI study of eye, mouth and hand movements

    Cerebral Cortex 15:1866–1876.

    https://doi.org/10.1093/cercor/bhi064

    • PubMed
    • Google Scholar
47. 1. Pitzalis S
    2. Sereno MI
    3. Committeri G
    4. Fattori P
    5. Galati G
    6. Patria F
    7. Galletti C

    (2010) Human v6: the medial motion area

    Cerebral Cortex 20:411–424.

    https://doi.org/10.1093/cercor/bhp112

    • PubMed
    • Google Scholar
48. 1. Pitzalis S
    2. Fattori P
    3. Galletti C

    (2015) The human cortical areas V6 and V6A

    Visual Neuroscience 32:E007.

    https://doi.org/10.1017/S0952523815000048

    • PubMed
    • Google Scholar
49. 1. Pitzalis S
    2. Serra C
    3. Sulpizio V
    4. Committeri G
    5. de Pasquale F
    6. Fattori P
    7. Galletti C
    8. Sepe R
    9. Galati G

    (2020) Neural bases of self- and object-motion in a naturalistic vision

    Human Brain Mapping 41:1084–1111.

    https://doi.org/10.1002/hbm.24862

    • PubMed
    • Google Scholar
50. 1. Polimeni JR
    2. Fischl B
    3. Greve DN
    4. Wald LL

    (2010) Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1

    NeuroImage 52:1334–1346.

    https://doi.org/10.1016/j.neuroimage.2010.05.005

    • PubMed
    • Google Scholar
51. 1. Polimeni JR
    2. Bhat H
    3. Witzel T
    4. Benner T
    5. Feiweier T
    6. Inati SJ
    7. Renvall V
    8. Heberlein K
    9. Wald LL

    (2016) Reducing sensitivity losses due to respiration and motion in accelerated echo planar imaging by reordering the autocalibration data acquisition

    Magnetic Resonance in Medicine 75:665–679.

    https://doi.org/10.1002/mrm.25628

    • PubMed
    • Google Scholar
52. 1. Puce A
    2. Allison T
    3. Bentin S
    4. Gore JC
    5. McCarthy G

    (1998) Temporal cortex activation in humans viewing eye and mouth movements

    The Journal of Neuroscience 18:2188–2199.

    https://doi.org/10.1523/JNEUROSCI.18-06-02188.1998

    • PubMed
    • Google Scholar
53. 1. Puce A
    2. Perrett D

    (2003) Electrophysiology and brain imaging of biological motion

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 358:435–445.

    https://doi.org/10.1098/rstb.2002.1221

    • PubMed
    • Google Scholar
54. 1. Rajimehr R
    2. Young JC
    3. Tootell RBH

    (2009) An anterior temporal face patch in human cortex, predicted by macaque maps

    PNAS 106:1995–2000.

    https://doi.org/10.1073/pnas.0807304106

    • PubMed
    • Google Scholar
55. 1. Sereno MI
    2. Dale AM
    3. Reppas JB
    4. Kwong KK
    5. Belliveau JW
    6. Brady TJ
    7. Rosen BR
    8. Tootell RB

    (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging

    Science 268:889–893.

    https://doi.org/10.1126/science.7754376

    • PubMed
    • Google Scholar
56. 1. Silson EH
    2. Groen IIA
    3. Kravitz DJ
    4. Baker CI

    (2016) Evaluating the correspondence between face-, scene-, and object-selectivity and retinotopic organization within lateral occipitotemporal cortex

    Journal of Vision 16:14.

    https://doi.org/10.1167/16.6.14

    • PubMed
    • Google Scholar
57. 1. Silver MA
    2. Ress D
    3. Heeger DJ

    (2005) Topographic maps of visual spatial attention in human parietal cortex

    Journal of Neurophysiology 94:1358–1371.

    https://doi.org/10.1152/jn.01316.2004

    • PubMed
    • Google Scholar
58. 1. Steel A
    2. Billings MM
    3. Silson EH
    4. Robertson CE

    (2021) A network linking scene perception and spatial memory systems in posterior cerebral cortex

    Nature Communications 12:2632.

    https://doi.org/10.1038/s41467-021-22848-z

    • PubMed
    • Google Scholar
59. 1. Steel A
    2. Garcia BD
    3. Goyal K
    4. Mynick A
    5. Robertson CE

    (2023) Scene perception and visuospatial memory converge at the anterior edge of visually responsive cortex

    The Journal of Neuroscience 43:5723–5737.

    https://doi.org/10.1523/JNEUROSCI.2043-22.2023

    • PubMed
    • Google Scholar
60. 1. Sulpizio V
    2. Galati G
    3. Fattori P
    4. Galletti C
    5. Pitzalis S

    (2020) A common neural substrate for processing scenes and egomotion-compatible visual motion

    Brain Structure & Function 225:2091–2110.

    https://doi.org/10.1007/s00429-020-02112-8

    • PubMed
    • Google Scholar
61. 1. Swisher JD
    2. Halko MA
    3. Merabet LB
    4. McMains SA
    5. Somers DC

    (2007) Visual topography of human intraparietal sulcus

    The Journal of Neuroscience 27:5326–5337.

    https://doi.org/10.1523/JNEUROSCI.0991-07.2007

    • PubMed
    • Google Scholar
62. 1. Szczepanski SM
    2. Konen CS
    3. Kastner S

    (2010) Mechanisms of spatial attention control in frontal and parietal cortex

    The Journal of Neuroscience 30:148–160.

    https://doi.org/10.1523/JNEUROSCI.3862-09.2010

    • PubMed
    • Google Scholar
63. 1. Tootell RB
    2. Mendola JD
    3. Hadjikhani NK
    4. Ledden PJ
    5. Liu AK
    6. Reppas JB
    7. Sereno MI
    8. Dale AM

    (1997) Functional analysis of V3A and related areas in human visual cortex

    The Journal of Neuroscience 17:7060–7078.

    https://doi.org/10.1523/JNEUROSCI.17-18-07060.1997

    • PubMed
    • Google Scholar
64. 1. Tootell RBH
    2. Nasiriavanaki Z
    3. Babadi B
    4. Greve DN
    5. Nasr S
    6. Holt DJ

    (2022) Interdigitated columnar representation of personal space and visual space in human parietal cortex

    The Journal of Neuroscience 42:9011–9029.

    https://doi.org/10.1523/JNEUROSCI.0516-22.2022

    • PubMed
    • Google Scholar
65. 1. Troiani V
    2. Stigliani A
    3. Smith ME
    4. Epstein RA

    (2014) Multiple object properties drive scene-selective regions

    Cerebral Cortex 24:883–897.

    https://doi.org/10.1093/cercor/bhs364

    • PubMed
    • Google Scholar
66. 1. Tsao DY
    2. Moeller S
    3. Freiwald WA

    (2008) Comparing face patch systems in macaques and humans

    PNAS 105:19514–19519.

    https://doi.org/10.1073/pnas.0809662105

    • PubMed
    • Google Scholar
67. 1. Wolbers T
    2. Klatzky RL
    3. Loomis JM
    4. Wutte MG
    5. Giudice NA

    (2011) Modality-independent coding of spatial layout in the human brain

    Current Biology 21:984–989.

    https://doi.org/10.1016/j.cub.2011.04.038

    • PubMed
    • Google Scholar
68. 1. Yacoub E
    2. Shmuel A
    3. Logothetis N
    4. Uğurbil K

    (2007) Robust detection of ocular dominance columns in humans using hahn spin echo bold functional mri at 7 tesla

    NeuroImage 37:1161–1177.

    https://doi.org/10.1016/j.neuroimage.2007.05.020

    • PubMed
    • Google Scholar
69. 1. Yue X
    2. Cassidy BS
    3. Devaney KJ
    4. Holt DJ
    5. Tootell RBH

    (2011) Lower-level stimulus features strongly influence responses in the fusiform face area

    Cerebral Cortex 21:35–47.

    https://doi.org/10.1093/cercor/bhq050

    • PubMed
    • Google Scholar
70. 1. Yue X
    2. Robert S
    3. Ungerleider LG

    (2020) Curvature processing in human visual cortical areas

    NeuroImage 222:117295.

    https://doi.org/10.1016/j.neuroimage.2020.117295

    • PubMed
    • Google Scholar
71. 1. Zimmermann J
    2. Goebel R
    3. De Martino F
    4. van de Moortele PF
    5. Feinberg D
    6. Adriany G
    7. Chaimow D
    8. Shmuel A
    9. Uğurbil K
    10. Yacoub E

    (2011) Mapping the organization of axis of motion selective features in human area MT using high-field fMRI

    PLOS ONE 6:e28716.

    https://doi.org/10.1371/journal.pone.0028716

    • PubMed
    • Google Scholar

Author details

1. Bryan Kennedy

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States

   Contribution

   Formal analysis, Visualization, Writing – original draft

   Competing interests

   No competing interests declared

2. Sarala N Malladi

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

3. Roger BH Tootell

   1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
   2. Department of Radiology, Harvard Medical School, Boston, United States

   Contribution

   Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

4. Shahin Nasr

   1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
   2. Department of Radiology, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   shahin.nasr@mgh.harvard.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7546-9976

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