  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.
