基于自由能原理的视觉感知系统
Sensorimotor Visual Perception on Embodied System Using Free Energy Principle
摘要：
我们提出了一个体现系统的基础上的自由能原理（FEP）的感觉运动视觉知觉。我们使用MNIST数据集在字符识别任务中对其进行了评估。尽管FEP成功地描述了生物在数学上遵守的规则，并声称生物系统不断改变其内部模型和行为，以最大限度地减少预测感官输入的差异，但它不足以模拟感觉运动视觉感知。该系统的实施例是实现感觉运动视觉感知的关键。所提出的具体化系统由主体和存储器配置。身体有一个控制眼睛注视方向的眼运动系统，这意味着眼睛只能观察环境的一个小的聚焦区域。记忆不是照相的，而是用变分自编码器实现的生成模型，其包含关于环境的先验知识，并且该知识被分类。通过限制身体和记忆能力并根据FEP进行操作，具体化的系统重复地采取行动以基于未来感觉输入的各种电位来获得下一个感觉输入。在评估中，环境的推断被表示为字符的近似后验分布（0 - 9）。随着重复次数的增加，注意区域不断移动，逐渐减少了字符的不确定性。最后，正确字符的概率成为字符中最高的。改变初始注意位置提供了不同的最终分布，这表明所提出的系统具有确认偏差。
英文摘要：
We propose an embodied system based on the free energy principle (FEP) for sensorimotor visual perception. We evaluated it in a character-recognition task using the MNIST dataset. Although the FEP has successfully described a rule that living things obey mathematically and claims that a biological system continues to change its internal models and behaviors to minimize the difference in predicting sensory input, it is not enough to model sensorimotor visual perception. An embodiment of the system is the key to achieving sensorimotor visual perception. The proposed embodied system is configured by a body and memory. The body has an ocular motor system controlling the direction of eye gaze, which means that the eye can only observe a small focused area of the environment. The memory is not photographic, but is a generative model implemented with a variational autoencoder that contains prior knowledge about the environment, and that knowledge is classified. By limiting body and memory abilities and operating according to the FEP, the embodied system repeatedly takes action to obtain the next sensory input based on various potentials of future sensory inputs. In the evaluation, the inference of the environment was represented as an approximate posterior distribution of characters (0 - 9). As the number of repetitions increased, the attention area moved continuously, gradually reducing the uncertainty of characters. Finally, the probability of the correct character became the highest among the characters. Changing the initial attention position provides a different final distribution, suggesting that the proposed system has a confirmation bias.
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