The Idea

Cognition is computation: rule-governed manipulation of symbolic representations, software running on neural hardware. Fodor’s language of thought gives the strongest version. Mental representations are compositional and systematic like sentences; if you can think “A loves B” you can think “B loves A,” and finite symbols yield unbounded thoughts. Newell and Simon’s physical symbol system hypothesis makes the engineering bet: any system that manipulates symbols by rules has what it takes for intelligent action.

This is the classical charter of cognitive science. If minds run algorithms, you can reverse-engineer them from behavior, model them in code, and study the computational level without waiting for neuroscience. The standing objections define the field’s later history: Searle’s Chinese Room (rule-following without understanding), the frame problem (you cannot explicitly represent everything relevant), and connectionism (networks learn without discrete symbols or rules).

Current standing: weakened as the sole story, not rejected. Symbolic computation still describes explicit reasoning, planning, and syntax well; it struggles with perception, motor control, and implicit, context-soaked cognition.

Why I Keep It

It is one of the fixed positions I triangulate theories of cognition against, and it sets a working question in my own domain: is code-switching rule-governed computation over two grammars, or something statistical and messier? Same question for grammatical knowledge generally: explicit rules or implicit patterns. The honest answer so far is “depends on the level,” which is why I keep the frame rather than the doctrine.

Tensions

• The symbol grounding problem: what makes internal symbols mean anything?
• Embodied and dynamical critiques: maybe cognition is continuous coupling with the world, not internal symbol shuffling.

Key Sources

• Newell, A., & Simon, H. (1976). “Computer Science as Empirical Inquiry: Symbols and Search”
• Fodor, J. (1975). The Language of Thought
• Searle, J. (1980). “Minds, Brains, and Programs”