Ambiguous visual images can generate dynamic and stochastic switches in perceptual interpretation known as perceptual rivalry. Such dynamics have primarily been studied in the context of rivalry between two percepts, but there is growing interest in the neural mechanisms that drive rivalry between more than two percepts. In recent experiments, we showed that split images presented to each eye lead to subjects perceiving four stochastically alternating percepts (Jacot-Guillarmod et al. Vision research, 133, 37–46, 2017): two single eye images and two interocularly grouped images. Here we propose a hierarchical neural network model that exhibits dynamics consistent with our experimental observations. The model consists of two levels, with the first representing monocular activity, and the second representing activity in higher visual areas. The model produces stochastically switching solutions, whose dependence on task parameters is consistent with four generalized Levelt Propositions, and with experiments. Moreover, dynamics restricted to invariant subspaces of the model demonstrate simpler forms of bistable rivalry. Thus, our hierarchical model generalizes past, validated models of binocular rivalry. This neuromechanistic model also allows us to probe the roles of interactions between populations at the network level. Generalized Levelt’s Propositions hold as long as feedback from the higher to lower visual areas is weak, and the adaptation and mutual inhibition at the higher level is not too strong. Our results suggest constraints on the architecture of the visual system and show that complex visual stimuli can be used in perceptual rivalry experiments to develop more detailed mechanistic models of perceptual processing.

中文翻译：

---

涉及眼间分组的感知多稳定性的分层模型。

模糊的视觉图像可以在知觉解释中产生动态和随机的切换，称为知觉竞争。这种动力学主要是在两种感知之间竞争的背景下进行研究的，但人们对驱动两种以上感知之间竞争的神经机制越来越感兴趣。在最近的实验中，我们发现呈现给每只眼睛的分割图像会导致受试者感知四种随机交替的感知（Jacot-Guillarmod 等人视觉研究，133, 37–46, 2017)：两个单眼图像和两个眼间分组图像。在这里，我们提出了一种分层神经网络模型，该模型表现出与我们的实验观察一致的动力学。该模型由两个层次组成，第一个代表单眼活动，第二个代表更高视觉区域的活动。该模型产生随机切换解决方案，其对任务参数的依赖性与四个广义 Levelt 命题一致，并且与实验一致。此外，仅限于模型不变子空间的动力学表现出更简单的双稳态竞争形式。因此，我们的分层模型概括了过去经过验证的双眼竞争模型。这种神经力学模型还使我们能够在网络级别探索群体之间相互作用的作用。广义 Levelt 命题只要从较高视觉区域到较低视觉区域的反馈较弱，且较高层次的适应和相互抑制不太强，就成立。我们的结果表明对视觉系统架构的限制，并表明复杂的视觉刺激可用于知觉竞争实验，以开发更详细的知觉处理机械模型。