Humans perceive objects daily and communicate their perceptions using various channels. Here, we describe a computational model that track and simulate objects' perception, and their representations as they pass in communication. We describe two key components of our internal representation ('observed' and 'seen') and relate them to familiar computer vision terms (encoding and decoding). These elements joined together to form semiotic networks, which simulate awareness in object perception and human communication. Nowadays, most neural networks are uninterpretable. On the other hand, our model is free from this disadvantages. We performed several experiments and demonstrated the visibility of our model. We describe how our network may be used as preprocessing unit to any classification network. In our experiments the compound network overperforms in average the classification network at datasets with small training data. Future work would leverage our model to gain better understanding of human communications and personal representations.

通过计算模型跟踪和模拟物体的感知及其在通信中的表征，本研究描述了内部表征的两个关键组成部分（'observed' 和 'seen'），并将它们与计算机视觉术语（编码和解码）联系起来。这些元素相结合形成符号网络，模拟物体感知和人类沟通中的意识。通过实验证明了我们模型的可视性，并探讨了如何将我们的网络作为预处理单元用于任何分类网络以提高效果，以期通过我们的模型更好地理解人类沟通和个人表征。

可解释的符号网络表示意识