The “Parahippocampal Place Area” Responds Preferentially to High Spatial Frequencies in Humans and Monkeys

Rajimehr, Reza; Devaney, Kathryn J.; Bilenko, Natalia Y.; Young, Jeremy C.; Tootell, Roger B. H.

doi:10.1371/journal.pbio.1000608

Cited by 174 publications

(156 citation statements)

References 47 publications

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“…Thus, the reduction of higher frequency component is likely to have little effect on the difference in the responses observed in this study. Furthermore, an additional analysis confirmed that the specularity-induced activations were not restricted in the regions representing central visual field which are more sensitive to high spatial frequencies than those representing more peripheral visual field (Henriksson et al, 2008;Rajimehr et al, 2011;Sasaki et al, 2001); the effect of the scrambling on the responses was even consistently larger in the peripheral regions than in the central regions (Supplementary analysis 2 and Supplementary Fig. 2).…”

Section: Discussionmentioning

confidence: 61%

“…However, this is not likely the case considering the spatial frequency tunings of BOLD responses in the visual cortex. Several fMRI studies have shown that early visual areas are most sensitive to relatively low spatial frequency (typically peaked at about 1-2 cycles/°) and are less sensitive to high spatial frequency (7 cycles/°or higher) (Henriksson et al, 2008;Rajimehr et al, 2011;Sasaki et al, 2001;Singh et al, 2000). Moreover, the amplitudes of higher frequency were relatively smaller in our stimuli.…”

Section: Discussionmentioning

confidence: 64%

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Selective responses to specular surfaces in the macaque visual cortex revealed by fMRI

2012

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 64%

Selective responses to specular surfaces in the macaque visual cortex revealed by fMRI

2012

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“…34), which are thought to be homologous to scene-selective sites in humans, namely, (i) the transverse occipital area (TOS) and (ii) the parahippocampal place area (PPA) (35), respectively. In human subjects, rectilinear objects and shapes also preferentially activate these scene-selective areas (36)(37)(38). However, as scene-selective areas were not localized in our monkeys, this idea could not be directly tested.…”

Section: Significancementioning

confidence: 82%

Curvature-processing network in macaque visual cortex

Yue

Pourladian

Tootell

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Our visual environment abounds with curved features. Thus, the goal of understanding visual processing should include the processing of curved features. Using functional magnetic resonance imaging in behaving monkeys, we demonstrated a network of cortical areas selective for the processing of curved features. This network includes three distinct hierarchically organized regions within the ventral visual pathway: a posterior curvature-biased patch (PCP) located in the near-foveal representation of dorsal V4, a middle curvature-biased patch (MCP) located on the ventral lip of the posterior superior temporal sulcus (STS) in area TEO, and an anterior curvature-biased patch (ACP) located just below the STS in anterior area TE. Our results further indicate that the processing of curvature becomes increasingly complex from PCP to ACP. The proximity of the curvature-processing network to the well-known face-processing network suggests a possible functional link between them.curvature patches | face patches | curved Gabor filters D ecades of research have focused on understanding visual feature processing, particularly along the ventral visual pathway. Such studies have shown that neurons in lower-order visual areas (e.g., V1) respond strongly to simple oriented contours (1), whereas neurons in higher-order visual areas (e.g., inferior temporal cortex) respond selectively to more complex image features and/or visual categories (2-4), in ways that are not yet fully understood. To link these extremes in visual information processing, many studies have aimed to clarify the optimal "trigger" features at intermediate levels of the visual cortical hierarchy.Among these features, stimulus curvature has not been well studied. This is surprising because, strictly, all lines are curved to some extent, except for the single exception of a perfectly straight line. This ubiquity of curved shapes also extends to 3D surfaces (5). In nature, where much of our visual system presumably evolved, perfectly flat surfaces are rare. Even the flattest of natural features (e.g., oceans, sandy beaches) are often curved to some extent, due to wind, water motion, and even the curvature of the earth. Thus, it is important to understand curvature processing to fully unravel the steps in cortical visual processing.Among the few studies to test single neuron responses to curvature per se, Gallant et al. (6,7) reported that a significant percentage of neurons in macaque cortical area V4 is selective for curved stimuli. Intriguingly, these authors also noted that neurons preferring curved patterns were often anatomically clustered together. Subsequently, demonstrated that neurons in the parafoveal representation of dorsal V4 respond robustly to the curvature component of complex shapes. To our knowledge, there have been no systematic studies of curvature at levels below V4 in macaques.Intriguingly, some evidence suggests that the processing of curvature may interact selectively with the processing of faces. For instance, perceptual deficits in face reco...

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“…It has already been established that the fusiform and parahippocampal gyri are involved in face, object and scene recognition (Epstein, Harris, Stanley, & Kanwisher, 1999;Epstein & Kanwisher, 1998;Grill-Spector, 2003;Haxby et al, 2001;Tanaka, 1996). These regions were also recently shown to be sensitive to low-level properties of stimuli such as spatial frequencies (Goffaux et al, 2010;Kauffmann, Ramanoël, Guyader, Chauvin, & Peyrin, 2015;Musel et al, 2014;Rajimehr, Devaney, Bilenko, Young, & Tootell, 2011). These activations are therefore consistent with Bar and colleagues' model of visual perception (Bar, 2003(Bar, , 2007Bar et al, 2006) which postulate that visual analysis begins with the parallel extraction of different elementary attributes at different spatial frequencies, and that, at neurobiological level, LSF content of an image is rapidly projected through magnocellular pathways from the occipital cortex to the orbitofrontal cortex, where it activates plausible interpretations of the visual input.…”

Section: Connectionmentioning

confidence: 99%

Effective connectivity in the neural network underlying coarse-to-fine categorization of visual scenes. A dynamic causal modeling study

Kauffmann

Chauvin

Pichat

et al. 2015

Brain and Cognition

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The “Parahippocampal Place Area” Responds Preferentially to High Spatial Frequencies in Humans and Monkeys

Abstract: A visual brain area that is thought to encode higher-level "place" information responds instead to lower-level "edge" information. A corresponding brain area is demonstrated in non-human species.

Cited by 174 publications

References 47 publications

Selective responses to specular surfaces in the macaque visual cortex revealed by fMRI

Selective responses to specular surfaces in the macaque visual cortex revealed by fMRI

Curvature-processing network in macaque visual cortex

Effective connectivity in the neural network underlying coarse-to-fine categorization of visual scenes. A dynamic causal modeling study

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