Object shape and surface properties are jointly encoded in mid-level ventral visual cortex

Pasupathy, Anitha; Kim, Taekjun; Popovkina, Dina

doi:10.1016/j.conb.2019.09.009

Cited by 29 publications

(23 citation statements)

References 59 publications

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“…Such complex object organization is embodied using combinations of structurally separated feature columns (Fujita et al, 1992;Rajalingham and DiCarlo, 2019;Tanaka, 2003;Tsunoda et al, 2001;Wang et al, 1996). Positioned in-between the local orientation architecture of V1 and the global object architecture of IT lies cortical area V4, exhibiting visual selectivity that demonstrates integration of simple-towards-complex information (Pasupathy et al, 2019;Roe et al, 2012;Yue et al, 2014), and extensive anatomical connectivity across the visual hierarchy (Gattass et al, 1990;Ungerleider et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Clustered functional domains for curves and corners in cortical area V4

Jiang

Andolina

et al. 2021

eLife

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The ventral visual pathway is crucially involved in integrating low-level visual features into complex representations for objects and scenes. At an intermediate stage of the ventral visual pathway, V4 plays a crucial role in supporting this transformation. Many V4 neurons are selective for shape segments like curves and corners, however it remains unclear whether these neurons are organized into clustered functional domains, a structural motif common across other visual cortices. Using two-photon calcium imaging in awake macaques, we confirmed and localized cortical domains selective for curves or corners in V4. Single-cell resolution imaging confirmed that curve or corner selective neurons were spatially clustered into such domains. When tested with hexagonal-segment stimuli, we find that stimulus smoothness is the cardinal difference between curve and corner selectivity in V4. Combining cortical population responses with single neuron analysis, our results reveal that curves and corners are encoded by neurons clustered into functional domains in V4. This functionally-specific population architecture bridges the gap between the early and late cortices of the ventral pathway and may serve to facilitate complex object recognition.

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Section: Introductionmentioning

confidence: 99%

Clustered functional domains for curves and corners in cortical area V4

Jiang

Andolina

et al. 2021

eLife

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“…We propose an alternative mechanism to explain the co-variation between cues for surface gloss and material class, inspired by converging evidence from independent lines of psychophysical and neuropsychological research that suggests computations of stimulus properties are inherently coupled (Anderson, 2020; Pasupathy et al, 2019). Specifically, monkey physiology studies have found that neurons in primate area V4 respond only to specific combinations of texture and shape but not to these properties separately, suggesting joint representations of shape and surface properties (for a review see Pasupathy et al, 2019). In line with this, a series of human psychophysics studies have demonstrated that percepts of 3D shape and surface properties (e.g., gloss or translucency) are mutually constrained by specific image gradient-contour relationships (i.e., the cues for each are not separate; for a review see Anderson, 2020).…”

Section: Discussionmentioning

confidence: 99%

Material category of visual objects computed from specular image structure

Schmid

Barla

Doerschner

2020

Preprint

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There is a growing body of work investigating the visual perception of material properties like gloss, yet practically nothing is known about how the brain recognises different material classes like plastic, pearl, satin, and steel, nor the precise relationship between material properties like gloss and perceived material class. We report a series of experiments that show that parametrically changing reflectance parameters leads to qualitative changes in material appearance beyond those expected by the reflectance function used. We measure visual (image) features that predict these changes in appearance, and causally manipulate these features to confirm their role in perceptual categorisation. Furthermore, our results suggest that the same visual features underlie both material recognition and surface gloss perception. However, the predictiveness of each feature to perceived gloss changes with material category, suggesting that the pockets of feature space occupied by different material classes affect the processing of those very features when estimating surface glossiness. Our results do not support a traditional feedforward view that assumes that material perception proceeds from low-level image measurements, to mid-level estimates of surface properties, to high-level material classes, nor the idea that material properties like gloss and material class are simultaneously "read out" from visual gloss features. Instead, we suggest that the perception and neural processing of material properties like surface gloss should be considered in the context of material recognition. Chadwick & Kentridge, 2015;Fleming 2014Fleming , 2017), yet practically nothing is known about how the brain recognises different material classes like plastic, pearl, satin, and steel. A likely reason for this is that studying properties like colour and gloss seems more tractable than discovering the necessary and sufficient conditions for recognising the many material classes in our environment. 2011;For example, previous research has discovered a limited set of conditions that trigger the perception of a glossy versus matte surface, involving the intensity, shape, position, and orientation of specular highlights (bright reflections;

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“…Various studies suggest that it encodes mid-level form features such as curvature (Gallant et al, 1993;Gallant et al, 2000), convexity (Pasupathy & Connor, 2001), and texture (Roe et al, 2012;Pasupathy et al, 2019). V4 also encodes color information (Brewer et al, 2005;Conway et al, 2007;Brouwer & Heeger, 2009;Brouwer & Heeger, 2013;Bannert & Bartels, 2018).…”

Section: Color and Form Processing Across The Visual Hierarchymentioning

confidence: 99%

Representation of Color, Form, and their Conjunction across the Human Ventral Visual Pathway

Taylor

2020

Preprint

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Despite decades of neuroscience research, our understanding of the relationship between color and form processing in the primate ventral visual pathway remains incomplete. Using fMRI multivoxel pattern analysis, this study examined the coding of color with both a simple form feature (orientation) and a mid-level form feature (curvature) in human early visual areas V1 to V4, posterior and central color regions, and shape areas in ventral and lateral occipito-temporal cortex. With the exception of the central color region (which showed color but not form decoding), successful color and form decoding was found in all other regions examined, even for color and shape regions showing univariate sensitivity to one feature. That said, all regions exhibited significant feature decoding biases, with decoding from color and shape regions largely consistent with their univariate preferences. Color and form are thus represented in neither a completely distributed nor a completely modular manner, but a biased distributed manner. Interestingly, coding of one feature in a brain region was always tolerant to changes in the other feature, indicating relative independence of color and form coding throughout the ventral visual cortex. Although evidence for interactive coding of color and form also existed, the effect was weak and only existed for color and orientation conjunctions in early visual cortex. No evidence for interactive coding of color and curvature was found. The predominant relationship between color and form coding in the human brain appears to be one of anatomical coexistence (in a biased distributed manner), but representational independence.

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Object shape and surface properties are jointly encoded in mid-level ventral visual cortex

Cited by 29 publications

References 59 publications

Clustered functional domains for curves and corners in cortical area V4

Clustered functional domains for curves and corners in cortical area V4

Material category of visual objects computed from specular image structure

Representation of Color, Form, and their Conjunction across the Human Ventral Visual Pathway

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