Inspired by Bayesian approaches to brain function in neuroscience, we give a simple theory of probabilistic inference for a unified account of reasoning and learning. We simply model how data cause symbolic knowledge in terms of its satisfiability in formal logic. The underlying idea is that reasoning is a process of deriving symbolic knowledge from data via abstraction, i.e., selective ignorance. The logical consequence relation is discussed for its proof-based theoretical correctness. The MNIST dataset is discussed for its experiment-based empirical correctness.

翻译：受神经科学中贝叶斯脑功能理论的启发，我们提出了一种基于概率推理的简单理论，旨在统一解释推理与学习过程。该理论通过形式逻辑中的可满足性概念，简洁地建模了数据如何引发符号知识的产生。其核心思想在于：推理是从数据中通过抽象（即选择性忽略）推导出符号知识的过程。本文讨论了逻辑蕴涵关系在基于证明的理论正确性方面的表现，并基于MNIST数据集验证了其实验驱动的经验正确性。