Departament de Cognició, Desenvolupament i Psicologia de l'Educació, Faculty of Psychology, University of Barcelona, Barcelona, Spain.
Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.
PLoS Comput Biol. 2021 Apr 26;17(4):e1007907. doi: 10.1371/journal.pcbi.1007907. eCollection 2021 Apr.
The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.
视觉系统对于编码亮度模式的空间上下文非常敏感。上下文敏感性激发了许多用于解释亮度（明度）感知的神经机制的提出。在这里，我们提出了一种用于估计许多视觉错觉亮度的新的计算模型。我们假设，许多亮度方面可以通过动态滤波过程来解释，该过程一方面减少了边缘表示中的冗余，另一方面增强了非冗余活动。动态滤波器是针对每个输入图像学习的，并且实现了上下文敏感性。动态滤波应用于（模型）复杂细胞的响应，以构建增益控制图。然后，增益控制图作用于简单细胞的响应，然后再通过活动传播来创建亮度图。我们的方法成功地预测了许多具有挑战性的视觉错觉，包括对比效应、同化和反向对比，使用的是同一组模型参数。
