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

Okazawa, Gouki; Goda, Naokazu; Komatsu, Hidehiko

doi:10.1016/j.neuroimage.2012.07.052

Cited by 43 publications

(43 citation statements)

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“…We propose that the third substream in the lower bank of STS processes 3D shape (Sereno et al, 2002; Yamane et al, 2008). This proposal is consistent with the presence in the lower bank of a small patch concerned with gloss ( Figure 2E ; Okazawa et al, 2012), a marker of 3D convexity for certain materials, and TEs, a region extracting curvature from disparity (Janssen et al, 2000). This substream overlaps with action-processing regions located in both banks of the STS, especially their deeper regions (Nelissen et al, 2011).…”

Section: Representations Of Real-world Entities In Tesupporting

confidence: 83%

“…Initially it was assumed that the non-face and non-body objects were processed outside these patches (Ishai et al, 1999; Tsao et al, 2003), implying that RWE of different types were processed in different compartments of TE. This view, however, is inconsistent with recent evidence for patches for color, 3D shape from disparity, or gloss (Harada et al, 2009; Joly et al, 2009; Okazawa et al, 2012). A recent study by Srihasam et al (2012) sheds new light on the exact organization of TE.…”

Section: Representations Of Real-world Entities In Tecontrasting

confidence: 78%

“…Purple curved lines in B–E : approximate caudal boundary of TE. From Denys et al (2004; B ), Harada et al (2009; C ), Goda et al (2014; D ), and Okazawa et al (2012; E ).…”

Section: Retinotopic Organization Of the Visual Systemmentioning

confidence: 99%

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The transition in the ventral stream from feature to real-world entity representations

2014

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We propose that the ventral visual pathway of human and non-human primates is organized into three levels: (1) ventral retinotopic cortex including what is known as TEO in the monkey but corresponds to V4A and PITd/v, and the phPIT cluster in humans, (2) area TE in the monkey and its homolog LOC and neighboring fusiform regions, and more speculatively, (3) TGv in the monkey and its possible human equivalent, the temporal pole. We attribute to these levels the visual representations of features, partial real-world entities (RWEs), and known, complete RWEs, respectively. Furthermore, we propose that the middle level, TE and its homolog, is organized into three parallel substreams, lower bank STS, dorsal convexity of TE, and ventral convexity of TE, as are their corresponding human regions. These presumably process shape in depth, 2D shape and material properties, respectively, to construct RWE representations.

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Section: Representations Of Real-world Entities In Tesupporting

confidence: 83%

Section: Representations Of Real-world Entities In Tecontrasting

confidence: 78%

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The transition in the ventral stream from feature to real-world entity representations

2014

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“…It is well established that the representation of complex shapes and contours is gradually built up along the ventral visual pathway (2)(3)(4)(5). On the other hand, how textural information is processed in the cortex is largely unknown, although some recent studies have examined the representation of natural textures and surface properties in the macaque ventral visual areas (6)(7)(8)(9)(10)(11). Because, unlike contours, textures cannot be described based on combinations of edge fragments, we need to consider different underlying cortical processing.…”

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confidence: 99%

Image statistics underlying natural texture selectivity of neurons in macaque V4

Okazawa

Tajima

Komatsu

2014

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

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Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron's preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception.texture perception | material perception | visual area V4 | single-cell recording | image analysis I n the visual world, objects are characterized in part by their shapes, but also by their textures (1). The wide variety of textures we experience enables us to segment objects from backgrounds, perceive object properties, and recognize materials. It is well established that the representation of complex shapes and contours is gradually built up along the ventral visual pathway (2-5). On the other hand, how textural information is processed in the cortex is largely unknown, although some recent studies have examined the representation of natural textures and surface properties in the macaque ventral visual areas (6-11). Because, unlike contours, textures cannot be described based on combinations of edge fragments, we need to consider different underlying cortical processing.In contrast to the limited knowledge available from physiology, computational descriptions of textures have been extensively developed in the fields of psychophysics (12-16) and computer vision (17)(18)(19). In one such description, Portilla and Simoncelli (20) proposed that textures could be represented using an ensemble of summary statistics, including features derived from the luminance histogram and the amplitudes of the outputs of Gabor-like filters, as well as higher-order statistics such as the correlations across the filter outputs (see Fig. 3 for details; hereafter, we call this collection of statistics "PS statistics," using the authors' initials). Portilla and Simoncelli successfully generated new textures that were visually indistinguishable from the originals solely by making their PS statistics identical. Their algorithm is particularly inspiring because PS statistics use filters and computations that share biological properties. It was recently shown, for example, that a version of their synthesis algorithm can generate perceptually indistinguishable visual images (visual metamers) (21) and that naturalisti...

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“…An important question was whether these gloss-selective neurons are organized in a cluster in marmoset FSTv, as the gloss-selective neuron in other species (Hiramatsu et al, 2011; Nishio et al, 2012; Okazawa et al, 2012). First, we investigated whether multiunits with similar category preference cluster along the cortical plane by comparing the similarity of depth-averaged category responses to the distance of the recorded electrode shanks (Figure 6).…”

Section: Resultsmentioning

confidence: 99%

Representation of Glossy Material Surface in Ventral Superior Temporal Sulcal Area of Common Marmosets

Miyakawa

Banno

Abe

et al. 2017

Front. Neural Circuits

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The common marmoset (Callithrix jacchus) is one of the smallest species of primates, with high visual recognition abilities that allow them to judge the identity and quality of food and objects in their environment. To address the cortical processing of visual information related to material surface features in marmosets, we presented a set of stimuli that have identical three-dimensional shapes (bone, torus or amorphous) but different material appearances (ceramic, glass, fur, leather, metal, stone, wood, or matte) to anesthetized marmoset, and recorded multiunit activities from an area ventral to the superior temporal sulcus (STS) using multi-shanked, and depth resolved multi-electrode array. Out of 143 visually responsive multiunits recorded from four animals, 29% had significant main effect only of the material, 3% only of the shape and 43% of both the material and the shape. Furthermore, we found neuronal cluster(s), in which most cells: (1) showed a significant main effect in material appearance; (2) the best stimulus was a glossy material (glass or metal); and (3) had reduced response to the pixel-shuffled version of the glossy material images. The location of the gloss-selective area was in agreement with previous macaque studies, showing activation in the ventral bank of STS. Our results suggest that perception of gloss is an important ability preserved across wide range of primate species.

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

Cited by 43 publications

References 73 publications

The transition in the ventral stream from feature to real-world entity representations

The transition in the ventral stream from feature to real-world entity representations

Image statistics underlying natural texture selectivity of neurons in macaque V4

Representation of Glossy Material Surface in Ventral Superior Temporal Sulcal Area of Common Marmosets

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