The Reasoning Error About Reasoning: Why Different Types of Reasoning Require Different Representational Structures
Yiling Wu

TL;DR
This paper proposes a framework identifying four key structural properties of representational systems, explaining how different reasoning types impose distinct demands and boundaries on these structures across disciplines.
Contribution
It introduces a novel framework with four structural properties that delineate reasoning capabilities and failures, bridging psychology, AI, and philosophy of mind.
Findings
Reasoning types below the boundary can use associative, probabilistic representations.
Higher reasoning types require all four structural properties for effective operation.
Scaling statistical learning alone cannot achieve the structural guarantees needed for deductive reasoning.
Abstract
Different types of reasoning impose different structural demands on representational systems, yet no systematic account of these demands exists across psychology, AI, and philosophy of mind. I propose a framework identifying four structural properties of representational systems: operability, consistency, structural preservation, and compositionality. These properties are demanded to different degrees by different forms of reasoning, from induction through analogy and causal inference to deduction and formal logic. Each property excludes a distinct class of reasoning failure. The analysis reveals a principal structural boundary: reasoning types below it can operate on associative, probabilistic representations, while those above it require all four properties to be fully satisfied. Scaling statistical learning without structural reorganization is insufficient to cross this boundary,…
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