Inverse Bayesian inference as a key of consciousness featuring a macroscopic quantum logical structure.
Yukio-Pegio Gunji, Shuji Shinohara, Taichi Haruna, Vasileios Basios
Bio Systems February 1, 2017 DOI: 10.1016/j.biosystems.2016.12.003 (opens in new tab) via PubMed
Summary
AI-generated from the abstractA measurement-oriented inference system that combines Bayesian and inverse Bayesian inferences can bridge the gap between mind and matter without relying on quantum mechanics. Bayesian inference contracts probability space while inverse inference relaxes it, enabling an agent to make decisions that adapt to immediate environmental changes. This process generates a pattern of joint probability for data and hypotheses, forming a nondistributive orthomodular lattice equivalent to quantum logic. The model shows that such a lattice can reveal information generated by inverse syllogism and address the frame and symbol-grounding problems. This is the first model to connect macroscopic cognitive processes with the mathematical structure of quantum mechanics without additional assumptions.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key finding | Proposes that a measurement-oriented inference system combining Bayesian and inverse Bayesian inferences generates an orthomodular lattice equivalent to quantum logic, connecting macroscopic cognitive processes with the mathematical structure of quantum mechanics. |
Abstract
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.