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[2206.13174] Towards Unifying Perceptual Reasoning and Logical Reasoning
Summary
The paper presents a probabilistic model that unifies perceptual reasoning and logical reasoning, highlighting their shared processes of knowledge derivation from both prior knowledge and data.
Why It Matters
This research is significant as it bridges two fundamental aspects of artificial intelligence—perception and logic—by framing them within a Bayesian inference framework. Understanding this relationship can enhance AI systems' reasoning capabilities and improve their performance in complex tasks.
Key Takeaways
- The model integrates perceptual and logical reasoning under a Bayesian framework.
- It characterizes the processes of knowledge derivation from prior knowledge and data.
- The research supports the view of perception as unconscious inference, aligning with Helmholtz's theories.
- Unifying these reasoning types can lead to advancements in AI applications.
- The findings may influence future AI research directions and methodologies.
Computer Science > Artificial Intelligence arXiv:2206.13174 (cs) [Submitted on 27 Jun 2022 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Towards Unifying Perceptual Reasoning and Logical Reasoning Authors:Hiroyuki Kido View a PDF of the paper titled Towards Unifying Perceptual Reasoning and Logical Reasoning, by Hiroyuki Kido View PDF HTML (experimental) Abstract: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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2206.13174 [cs.AI] (or arXiv:2206.13174v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2206.13174 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hiroyuki Kido [view email] [v1] Mon, 27 Jun 2022 10:32:47 UTC (1,465 KB) [v2] Mon, 23 Feb 2026 18:36:24 UTC (433 KB) Full-text li...
