Rudd Michael E
Howard Hughes Medical Institute and Department of Physiology and Biophysics University of Washington, Seattle, WA USA.
Adv Cogn Psychol. 2008 Jul 15;3(1-2):327-47. doi: 10.2478/v10053-008-0034-z.
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 good quantitative account of the results of several brightness matching experiments. Adding to this model the realistic dynamical assumptions that 1) the neurons that encode local contrast exhibit transient firing rate enhancement at the onset of an edge, and 2) that the effects of contrast gain control take time to spread between edges, results in a dynamic model of brightness computation that predicts the existence Broca-Sulzer transient brightness enhancement of the target, Type B metacontrast masking, and a form of paracontrast masking in which the target brightness is enhanced when the mask precedes the target in time.
本文回顾了近期的理论和实验工作,这些工作支持了亮度是在一系列涉及边缘整合和对比度增益控制的神经阶段进行计算的观点。本文提出,元对比和副对比掩蔽是这些神经机制动态特性的副产品。更具体地说,亮度计算模型假设,视网膜以及皮层区域V1和V2中的早期视觉神经元对局部边缘信号进行编码,这些信号的大小与视网膜图像中亮度边缘处亮度比的对数成正比。这些局部边缘信号会产生次级神经明度和暗度空间诱导信号,在皮层处理的后期阶段,这些信号会进行累加,以产生表面颜色的神经表征,或者对于此处考虑的颜色中性刺激而言,产生非彩色的神经表征。在对这些基于边缘的诱导信号进行空间累加之前,通过涉及神经边缘检测器之间的横向相互作用和自上而下的注意力控制的皮层增益机制,对分配给局部边缘对比度的权重进行调整。我们之前基于这些原理构建并计算机模拟了一个非彩色感知的神经模型,并表明我们的模型对几个亮度匹配实验的结果给出了很好的定量解释。在这个模型中加入现实的动态假设:1)编码局部对比度的神经元在边缘出现时表现出瞬时放电率增强;2)对比度增益控制的效果在边缘之间传播需要时间,这样就得到了一个亮度计算的动态模型,该模型预测了目标的布罗卡 - 叙尔泽瞬时亮度增强、B型元对比掩蔽以及一种当掩蔽在时间上先于目标时目标亮度增强的副对比掩蔽形式。