Beyond Symbolic Mind: Re-evaluating the Logical Model of Intelligence

Symbolic logic has long been treated as a model of intelligence, motivating rule-based inference and classical approaches to artificial intelligence. Yet systems built on explicit symbolic derivation have proven difficult to optimize, brittle under variation, and unable to generalize beyond tightly specified domains. These limitations are not merely engineering obstacles. The validity of long-chain symbolic inference depends on conditions that the world rarely affords: each step requires jointly realizable states and transition relations that must remain constructible throughout the chain. When any such condition cannot be instantiated, collapse is systemic rather than local. Human cognition, by contrast, operates under partial information, indeterminate predicates, and incomplete state specification, functioning without the requirement that all intermediate relations be jointly defined. This work re-evaluates the long-standing assumption that logical form constitutes the operative architecture of intelligence and clarifies why symbolic inference, though formally coherent, cannot serve as an operative account of cognition.