Department of Psychology, New York University, New York, NY 10003;
Center for Neural Science, New York University, New York, NY 10003.
Proc Natl Acad Sci U S A. 2017 Jul 25;114(30):E6192-E6201. doi: 10.1073/pnas.1620475114. Epub 2017 Jul 10.
When the corresponding retinal locations in the two eyes are presented with incompatible images, a stable percept gives way to perceptual alternations in which the two images compete for perceptual dominance. As perceptual experience evolves dynamically under constant external inputs, binocular rivalry has been used for studying intrinsic cortical computations and for understanding how the brain regulates competing inputs. Converging behavioral and EEG results have shown that binocular rivalry and attention are intertwined: binocular rivalry ceases when attention is diverted away from the rivalry stimuli. In addition, the competing image in one eye suppresses the target in the other eye through a pattern of gain changes similar to those induced by attention. These results require a revision of the current computational theories of binocular rivalry, in which the role of attention is ignored. Here, we provide a computational model of binocular rivalry. In the model, competition between two images in rivalry is driven by both attentional modulation and mutual inhibition, which have distinct selectivity (feature vs. eye of origin) and dynamics (relatively slow vs. relatively fast). The proposed model explains a wide range of phenomena reported in rivalry, including the three hallmarks: () binocular rivalry requires attention; () various perceptual states emerge when the two images are swapped between the eyes multiple times per second; () the dominance duration as a function of input strength follows Levelt's propositions. With a bifurcation analysis, we identified the parameter space in which the model's behavior was consistent with experimental results.
当两只眼睛的对应视网膜位置呈现出不兼容的图像时，稳定的感知会让位于感知交替，其中两个图像会争夺感知主导地位。由于在恒定的外部输入下，感知体验会动态地演变，双眼竞争已被用于研究内在的皮层计算，并用于理解大脑如何调节竞争输入。趋同的行为和 EEG 结果表明，双眼竞争和注意力是交织在一起的：当注意力从竞争刺激上转移开时，双眼竞争就会停止。此外，一只眼睛中的竞争图像通过与注意力诱导相似的增益变化模式来抑制另一只眼睛中的目标。这些结果需要对当前的双眼竞争计算理论进行修正，在该理论中，注意力的作用被忽略了。在这里，我们提供了一个双眼竞争的计算模型。在该模型中，竞争是由注意力调制和相互抑制共同驱动的，它们具有不同的选择性（特征与起源眼）和动态性（相对较慢与相对较快）。所提出的模型解释了竞争中报告的广泛现象，包括三个特征：（）双眼竞争需要注意力；（）当两只眼睛每秒多次交换图像时，会出现各种感知状态；（）作为输入强度函数的优势持续时间遵循 Levelt 的命题。通过分岔分析，我们确定了模型行为与实验结果一致的参数空间。
