Towards Unifying Perceptual Reasoning and Logical Reasoning
This work addresses a foundational issue in AI and cognitive science by bridging perception and logic, but it appears incremental as it builds on existing Bayesian inference frameworks.
The paper tackles the problem of unifying perceptual reasoning and logical reasoning by proposing a simple probabilistic model applicable to both, showing it unifies knowledge derivation processes and characterizing it in terms of logical consequence relations.
An increasing number of scientific experiments support the view of perception as Bayesian inference, which is rooted in Helmholtz's view of perception as unconscious inference. Recent study of logic presents a view of logical reasoning as Bayesian inference. In this paper, we give a simple probabilistic model that is applicable to both perceptual reasoning and logical reasoning. We show that the model unifies the two essential processes common in perceptual and logical systems: on the one hand, the process by which perceptual and logical knowledge is derived from another knowledge, and on the other hand, the process by which such knowledge is derived from data. We fully characterise the model in terms of logical consequence relations.