Humans perceive objects daily and communicate their perceptions using various channels. Here, we describe a computational model that tracks and simulates objects' perception and their representations as they are conveyed in communication. We describe two key components of our internal representation ("observed" and "seen") and relate them to familiar computer vision notions (encoding and decoding). These elements are joined together to form semiotics networks, which simulate awareness in object perception and human communication. Nowadays, most neural networks are uninterpretable. On the other hand, our model overcomes this limitation. The experiments demonstrates the visibility of the model. Our model of object perception by a person allows us to define object perception by a network. We demonstrate this with an example of an image baseline classifier by constructing a new network that includes the baseline classifier and an additional layer. This layer produces the images "perceived" by the entire network, transforming it into a perceptualized image classifier. Within our network, the internal image representations become more efficient for classification tasks when they are assembled and randomized. In our experiments, the perceptualized network outperformed the baseline classifier on MNIST training databases consisting of a restricted number of images. Our model is not limited to persons and can be applied to any system featuring a loop involving the processing from "internal" to "external" representations.

翻译：人类每日感知物体并通过多种渠道传达其感知。本文描述了一个计算模型，用于追踪和模拟物体感知及其在交流过程中形成的表征。我们定义了内部表征的两个关键组件（"观测"与"所见"），并将其与计算机视觉的经典概念（编码与解码）相关联。这些要素通过整合形成符号网络，从而模拟物体感知与人类交流中的意识。当今大多数神经网络缺乏可解释性，而我们的模型恰恰突破了这一局限。实验验证了模型的可视化能力。基于个体对物体感知的建模，我们得以定义网络对物体的感知。通过构建包含基准分类器与附加层的新网络，以图像基线分类器为例进行演示：该附加层生成网络整体"感知"到的图像，从而将其转化为具备感知能力的图像分类器。在我们的网络中，当内部图像表征经过聚合与随机化处理后，其分类任务效率得以提升。实验表明，在由有限图像构成的MNIST训练数据集上，该感知化网络的性能优于基准分类器。本模型不仅适用于个体，还可应用于任何涉及"内部"到"外部"表征循环处理的系统。