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Metacontrast masking and the cortical representation of surfacecolor: dynamical aspects of edge integration and contrast gaincontrol

机译:Metacontrast掩盖和表面的皮层表示颜色:边缘整合和对比度增益的动态方面控制

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摘要

This paper reviews recent theoretical and experimental work supporting the idea that brightness is computed in a series of neural stages involving edge integration and contrast gain control. It is proposed here that metacontrast and paracontrast masking occur as byproducts of the dynamical properties of these neural mechanisms. The brightness computation model assumes, more specifically, that early visual neurons in the retina, and cortical areas V1 and V2, encode local edge signals whose magnitudes are proportional to the logarithms of the luminance ratios at luminance edges within the retinal image. These local edge signals give rise to secondary neural lightness and darkness spatial induction signals, which are summed at a later stage of cortical processing to produce a neural representation of surface color, or achromatic color, in the case of the chromatically neutral stimuli considered here. Prior to the spatial summation of these edge-based induction signals, the weights assigned to local edge contrast are adjusted by cortical gain mechanisms involving both lateral interactions between neural edge detectors and top-down attentional control. We have previously constructed and computer-simulated a neural model of achromatic color perception based on these principles and have shown that our model gives a goodquantitative account of the results of several brightness matching experiments.Adding to this model the realistic dynamical assumptions that 1) the neuronsthat encode local contrast exhibit transient firing rate enhancement at theonset of an edge, and 2) that the effects of contrast gain control take time tospread between edges, results in a dynamic model of brightness computation thatpredicts the existence Broca-Sulzer transient brightness enhancement of thetarget, Type B metacontrast masking, and a form of paracontrast masking in whichthe target brightness is enhanced when the mask precedes the target in time.
机译:本文回顾了最近的理论和实验工作,支持了在一系列涉及边缘积分和对比度增益控制的神经阶段中计算亮度的想法。在此提出,元对比和副对比掩蔽是这些神经机制的动力学特性的副产品。亮度计算模型更具体地假设,视网膜中的早期视觉神经元以及皮层区域V1和V2对局部边缘信号进行编码,该局部边缘信号的大小与视网膜图像内亮度边缘处的亮度比的对数成正比。这些局部边缘信号引起次级神经明度和暗度空间感应信号,在此处考虑的色中性刺激的情况下,将它们在皮层处理的后期相加,以产生表面颜色或无色颜色的神经表示。在对这些基于边缘的感应信号进行空间求和之前,通过皮质增益机制调整分配给局部边缘对比度的权重,该机制涉及神经边缘检测器和自上而下的注意力控制之间的横向相互作用。我们先前根据这些原理构建并计算机模拟了消色差颜色感知的神经模型,并表明我们的模型能够提供良好的定量说明几个亮度匹配实验的结果。向该模型添加现实的动力学假设,即1)神经元编码局部对比度的图像在边缘的开始; 2)对比度增益控制的效果需要时间分布在边缘之间,形成亮度计算的动态模型预测存在Broca-Sulzer瞬态亮度增强目标,B型元对比遮罩和一种形式的副对比遮罩,其中当遮罩在时间上早于目标时,目标亮度会提高。

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