Hugh Wilson has proposed a class of models that treat higher-level decision making as a competition between patterns coded as levels of a set of attributes in an appropriately defined network (Cortical Mechanisms of Vision, pp. 399-417, 2009; The Constitution of Visual Consciousness: Lessons from Binocular Rivalry, pp. 281-304, 2013). In this paper, we propose that symmetry-breaking Hopf bifurcation from fusion states in suitably modified Wilson networks, which we call rivalry networks, can be used in an algorithmic way to explain the surprising percepts that have been observed in a number of binocular rivalry experiments. These rivalry networks modify and extend Wilson networks by permitting different kinds of attributes and different types of coupling. We apply this algorithm to psychophysics experiments discussed by Kovács et al. (Proc. Natl. Acad. Sci. USA 93:15508-15511, 1996), Shevell and Hong (Vis. Neurosci. 23:561-566, 2006; Vis. Neurosci. 25:355-360, 2008), and Suzuki and Grabowecky (Neuron 36:143-157, 2002). We also analyze an experiment with four colored dots (a simplified version of a 24-dot experiment performed by Kovács), and a three-dot analog of the four-dot experiment. Our algorithm predicts surprising differences between the three- and four-dot experiments.

中文翻译：

---

网络对称性和双眼竞争实验。

休·威尔逊 (Hugh Wilson) 提出了一类模型，将更高级别的决策视为编码为适当定义网络中一组属性级别的模式之间的竞争（Cortical Mechanisms of Vision, pp. 399-417, 2009; TheConstitution of视觉意识：双眼竞争的教训，第 281-304 页，2013 年）。在本文中，我们提出在适当修改的威尔逊网络（我们称为竞争网络）中融合状态的对称破坏 Hopf 分岔可以以算法方式用于解释在许多双眼竞争实验中观察到的令人惊讶的感知. 这些竞争网络通过允许不同类型的属性和不同类型的耦合来修改和扩展威尔逊网络。我们将此算法应用于 Kovács 等人讨论的心理物理学实验。(Proc. Natl. 阿卡德。科学。USA 93:15508-15511, 1996)、Shevell 和 Hong (Vis. Neurosci. 23:561-566, 2006; Vis. Neurosci. 25:355-360, 2008)，以及 Suzuki 和 Grabowecky (Neuron) (Neuron) , 2002)。我们还分析了一个带有四个彩色点的实验（Kovács 进行的 24 点实验的简化版本）和四点实验的三点模拟。我们的算法预测了三点和四点实验之间惊人的差异。