Gunji Yukio-Pegio, Shinohara Shuji, Haruna Taichi, Basios Vasileios
Department of Intermedia Art and Science, School of Fundamental Science and Technology, Waseda University, Ohkubo 3-4-1, Shinjuku-ku, Tokyo 169-8555, Japan.
Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Biosystems. 2017 Feb;152:44-65. doi: 10.1016/j.biosystems.2016.12.003. Epub 2016 Dec 29.
To overcome the dualism between mind and matter and to implement consciousness in science, a physical entity has to be embedded with a measurement process. Although quantum mechanics have been regarded as a candidate for implementing consciousness, nature at its macroscopic level is inconsistent with quantum mechanics. We propose a measurement-oriented inference system comprising Bayesian and inverse Bayesian inferences. While Bayesian inference contracts probability space, the newly defined inverse one relaxes the space. These two inferences allow an agent to make a decision corresponding to an immediate change in their environment. They generate a particular pattern of joint probability for data and hypotheses, comprising multiple diagonal and noisy matrices. This is expressed as a nondistributive orthomodular lattice equivalent to quantum logic. We also show that an orthomodular lattice can reveal information generated by inverse syllogism as well as the solutions to the frame and symbol-grounding problems. Our model is the first to connect macroscopic cognitive processes with the mathematical structure of quantum mechanics with no additional assumptions.
为了克服心物二元论并在科学中实现意识，必须将一个物理实体嵌入测量过程。尽管量子力学被视为实现意识的一个候选理论，但宏观层面的自然现象与量子力学并不一致。我们提出了一个面向测量的推理系统，它包括贝叶斯推理和逆贝叶斯推理。贝叶斯推理会收缩概率空间，而新定义的逆贝叶斯推理则会放宽该空间。这两种推理使智能体能够做出与环境的即时变化相对应的决策。它们为数据和假设生成一种特定的联合概率模式，该模式由多个对角矩阵和噪声矩阵组成。这被表示为一个与量子逻辑等效的非分配正交模格。我们还表明，正交模格能够揭示由逆三段论产生的信息以及框架问题和符号接地问题的解决方案。我们的模型是首个在无额外假设的情况下将宏观认知过程与量子力学的数学结构联系起来的模型。
