Integration of surface information in primary visual cortex (original) (raw)
Related papers
Early human visual cortex encodes surface brightness induced by dynamic context
2012
■ Visual scene perception owes greatly to surface features such as color and brightness. Yet, early visual cortical areas predominantly encode surface boundaries rather than surface interiors. Whether human early visual cortex may nevertheless carry a small signal relevant for surface perception is a topic of debate. We induced brightness changes in a physically constant surface by temporally modulating the luminance of surrounding surfaces in seven human participants. We found that fMRI activity in the V2 representation of the constant surface was in antiphase to luminance changes of surrounding surfaces (i.e., activity was in-phase with perceived brightness changes). Moreover, the amplitude of the antiphase fMRI activity in V2 predicted the strength of illusory brightness perception. We interpret our findings as evidence for a surface-related signal in early visual cortex and discuss the neural mechanisms that may underlie that signal in concurrence with its possible interaction with the properties of the fMRI signal. ■
Lightness constancy in primary visual cortex
Proceedings of the National Academy of Sciences, 2001
When the illumination of a visual scene changes, the quantity of light reflected from objects is altered. Despite this, the perceived lightness of the objects generally remains constant. This perceptual lightness constancy is thought to be important behaviorally for object recognition. Here we show that interactions from outside the classical receptive fields of neurons in primary visual cortex modulate neural responses in a way that makes them immune to changes in illumination, as is perception. This finding is consistent with the hypothesis that the responses of neurons in primary visual cortex carry information about surface lightness in addition to information about form. It also suggests that lightness constancy, which is sometimes thought to involve ''higher-level'' processes, is manifest at the first stage of visual cortical processing.
Seeing surfaces: The brain's vision of the world
Physics of Life Reviews, 2007
Surfaces of environmental objects are the key to understanding the visual experience of primates. Surfaces create structure in patterns of light available for sampling by visual systems, and delineate potential interactions that an animal can have with its environment, such as approaching goals, avoiding obstacles, grasping an object, or identifying members of a social group. Recent progress in modeling the perception of visual surfaces highlights the importance of feedforward and feedback connections in visual neural networks that segregate and group visual input into coherent regions related to corresponding surfaces in the visual world. Rich non-linear network dynamics in the brain underlie surface perception, including the detection, regularization, and grouping of visual boundaries between surfaces, the determination of "ownership" of a boundary by a closer surface that partially occludes a background, and the apprehension of a surface's visual quality, such as color or texture. Recent modeling efforts on these fronts are reviewed.
Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.
Intermediate-Level Visual Representations and the Construction of Surface Perception
Journal of Cognitive Neuroscience, 1995
Visual processing has often been divided into three stagesearly, intermediate, and high level vision, which roughly correspond to the sensation, perception, and cognition of the visual world. In this paper, we present a network-based model of intermediate-level vision that focuses on how surfaces might be represented in visual cortex. We propose a mechanism for representing surfaces through the establishment of "ownership"-a selective binding of contours and regions. The repre- all objects across the visual field. However, a major prob lem with this mechanism is that objects that are overlap ping or occluded, and that should otherwise be
Frontiers in human neuroscience, 2014
Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for to...
Stimulation in the Primary Visual Cortex
2012
Centre-surround interaction in the primary visual cortex (area V1) has been studied extensively using artificial, abstract stimulus patterns, such as bars, gratings and simple texture patterns. In this experiment, we extend the study of centre-surround interaction by using natural scene images. We systematically varied the contrast of natural image surrounds presented outside the classical receptive field (CRF), and recorded neuronal response to a natural image patch presented within the CRF in area V1 of awake, fixating macaques. For the majority of neurons (67 out of 111), the natural image surrounds profoundly modulated, mainly by suppressing, neuronal responses to CRF images. These modulatory effects started at the earliest stage of neuronal responses, and often depended on the contrast and higher-order structures of the surrounds. For 47 out of 67 neurons, randomising the phases of the Fourier spectrum of the natural image surround diminished the centre-surround interaction. Our results suggest that the centresurround interaction in area V1 can be extended to natural vision, and is sensitive to the higher-order structures of natural scene images, such as image contours.
Journal of Neurophysiology, 2011
Faced with an overwhelming amount of sensory information, we are able to prioritize the processing of select spatial locations and visual features. The neuronal mechanisms underlying such spatial and feature-based selection have been studied in considerable detail. More recent work shows that attention can also be allocated to objects, even spatially superimposed objects composed of dynamically changing features that must be integrated to create a coherent object representation. Much less is known about the mechanisms underlying such object-based selection. Our goal was to investigate behavioral and neuronal responses when attention was directed to one of two objects, specifically one of two superimposed transparent surfaces, in a task designed to preclude space-based and feature-based selection. We used functional magnetic resonance imaging (fMRI) to measure changes in blood oxygen level-dependent (BOLD) signals when attention was deployed to one or the other surface. We found that...
A functional circuitry for edge-induced brightness perception
Nature Neuroscience, 2007
The identification of visual contours and surfaces is central to visual scene segmentation. One view of image construction argues that object contours are first identified and then surfaces are filled in. Although there are psychophysical and single-unit data to suggest that the filling-in view is correct, the underlying circuitry is unknown. Here we examine specific spike-timing relationships between border and surface responses in cat visual cortical areas 17 and 18. With both real and illusory (Cornsweet) brightness contrast stimuli, we found a border-to-surface shift in the relative timing of spike activity. This shift was absent when borders were absent and could be reversed with relocation of the stimulus border, indicating that the direction of information flow is highly dependent on stimulus conditions. Furthermore, this effect was seen predominantly in 17-18, and not 17-17, interactions. These results demonstrate a border-to-surface mechanism at early stages of visual processing and emphasize the importance of interareal circuitry in vision.