Do Cortical Neurons Process Luminance or Contrast to Encode Surface Properties?

Vladusich, Tony; Lucassen, Marcel P.; Cornelissen, Frans W.

doi:10.1152/jn.01016.2005

Cited by 24 publications

(29 citation statements)

References 63 publications

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“…Friedman et al (2003), however, found no evidence for color filling-in signals in V1 and V2 of awake behaving monkeys. Recent modeling work suggests that the majority of V1 responses reported by Kinoshita and Komatsu (2001) can be understood on the basis of local and mean luminance processing and that only a small minority of responses are consistent with edge-driven surface activity, such as brightness filling-in (Vladusich et al, 2006). We speculate that the properties of these previously determined surface-responsive neurons (Rossi et al, 1996;MacEvoy et al, 1998;Rossi and Paradiso, 1999;Hung et al, 2001;Kinoshita and Komatsu, 2001;Roe et al, 2005) may in fact arise from the mechanisms underlying the extended edge responses we observed in our study, and so are presumably not directly related to our perception of brightness, color, or filling-in.…”

Section: Comparison To Neurophysiological Resultsmentioning

confidence: 99%

No Functional Magnetic Resonance Imaging Evidence for Brightness and Color Filling-In In Early Human Visual Cortex

Cornelissen¹,

Wade²,

Vladusich³

et al. 2006

J. Neurosci.

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The brightness and color of a surface depends on its contrast with nearby surfaces. For example, a gray surface can appear very light when surrounded by a black surface or dark when surrounded by a white surface. Some theories suggest that perceived surface brightness and color is represented explicitly by neural signals in cortical visual field maps; these neural signals are not initiated by the stimulus itself but rather by the contrast signals at the borders. Here, we use functional magnetic resonance imaging (fMRI) to search for such neural "filling-in" signals. Although we find the usual strong relationship between local contrast and fMRI response, when perceived brightness or color changes are induced by modulating a surrounding field, rather than the surface itself, we find there is no corresponding local modulation in primary visual cortex or other nearby retinotopic maps. Moreover, when we model the obtained fMRI responses, we find strong evidence for contributions of both local and long-range edge responses. We argue that such extended edge responses may be caused by neurons previously identified in neurophysiological studies as being brightness responsive, a characterization that may therefore need to be revised. We conclude that the visual field maps of human V1 and V2 do not contain filled-in, topographical representations of surface brightness and color.

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Section: Comparison To Neurophysiological Resultsmentioning

confidence: 99%

No Functional Magnetic Resonance Imaging Evidence for Brightness and Color Filling-In In Early Human Visual Cortex

Cornelissen¹,

Wade²,

Vladusich³

et al. 2006

J. Neurosci.

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“…Geisler et al (2007) also showed that most of the neurons in the primary visual cortex carry substantial local luminance information, although we note that our local luminance changes are much smaller than the ones reported in this study. Vladusich et al (2006) also concluded that luminance processing predominates over contrast integration in the vast majority of surface-responsive neurons in V1, and Tucker and Fitzpatrick (2006) showed that layer 2/3 neurons of tree shrews' primary visual cortex, are sensitive to large-scale changes in luminance.…”

Section: Discussionmentioning

confidence: 99%

Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing

et al. 2012

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The primary visual cortex (V1) is extensively studied with a large repertoire of stimuli, yet little is known about its encoding of natural images. Using voltage-sensitive dye imaging in behaving monkeys, we measured neural population response evoked in V1 by natural images presented during a face/scramble discrimination task. The population response showed two distinct phases of activity: an early phase that was spread over most of the imaged area, and a late phase that was spatially confined. To study the detailed relation between the stimulus and the population response, we used a simple encoding model to compute a continuous map of the expected neural response based on local attributes of the stimulus (luminance and contrast), followed by an analytical retinotopic transformation. Then, we computed the spatial correlation between the maps of the expected and observed response. We found that the early response was highly correlated with the local luminance of the stimulus and was sufficient to effectively discriminate between stimuli at the single trial level. The late response, on the other hand, showed a much lower correlation to the local luminance, was confined to central parts of the face images, and was highly correlated with the animal's perceptual report. Our study reveals a continuous spatial encoding of low-and high-level features of natural images in V1. The low level is directly linked to the stimulus basic local attributes and the high level is correlated with the perceptual outcome of the stimulus processing.

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“…Simple cells in cortical area V1, which are often modeled as linear spatial filters followed by a threshold nonlinearity—have also been alternatively modeled as having a linear dependence on log luminance (Kinoshita and Komatsu, 2001; Vladusich et al, 2006b), or log contrast (Tolhurst et al, 1983, their Figure 1). This suggests that the hypothesized logarithmic transformation might occur prior to the simple cell stage in V1.…”

Section: The Basic Neural Edge-integration Modelmentioning

confidence: 99%

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

Rudd

2014

Front. Hum. Neurosci.

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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.

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Do Cortical Neurons Process Luminance or Contrast to Encode Surface Properties?

Cited by 24 publications

References 63 publications

No Functional Magnetic Resonance Imaging Evidence for Brightness and Color Filling-In In Early Human Visual Cortex

No Functional Magnetic Resonance Imaging Evidence for Brightness and Color Filling-In In Early Human Visual Cortex

Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

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