John Searle’s Chinese Room argument challenges the claim that computational systems can understand language. A machine following syntactic rules may produce correct responses in Chinese without understanding a word. Searle concludes that symbol manipulation is insufficient for meaning.
Relational ontology reframes the debate: the problem arises because classical thinking conflates value coordination (computation) with meaning-making (construal). Once the distinction is made, the paradox dissolves.
1. The Classical Trap: Representational Misreading
Searle’s thought experiment assumes:
-
Meaning is an inherent property of symbols.
-
Correct outputs imply understanding if the system were intelligent.
-
Machines manipulate symbols without experiencing phenomena, therefore lacking understanding.
This relies on representational assumptions: meaning is an object to be “contained” in computation, rather than a first-order relational phenomenon.
2. System, Instance, and Construal in the Chinese Room
Within relational ontology:
-
System: the structured potential of symbolic, cognitive, and environmental resources (syntax, grammar, rules).
-
Instance: the actual execution of a program or manipulation of symbols in context.
-
Construal: the first-order phenomenon of interpretation — the experience of meaning, which is absent in purely syntactic operation.
Computation alone is a relationally empty actualisation: it can coordinate outputs, but it does not generate construal. Meaning is enacted only in relational cuts that instantiate construal.
3. Dissolving the Problem
From this perspective:
-
The computer’s manipulation of symbols is value coordination, not meaning-making.
-
Understanding emerges only when first-order construal occurs — when a system participates relationally in interpreting symbolic potential.
-
Searle’s room is a pseudo-problem: it misidentifies the locus of meaning as computation rather than relational actualisation.
Meaning is not intrinsic to symbols or programs; it arises from the perspectival cut actualising potential in context.
4. Implications for AI and Cognitive Science
Relational ontology clarifies the landscape:
-
AI can manipulate symbols without construal; this is coordination, not understanding.
-
True semantic engagement requires relational participation in systemic potential, i.e., relationally enacted construal.
-
Human understanding is first-order, not reducible to syntax or rule-following.
Thus, the Chinese Room is not a barrier to AI but a lesson in distinguishing computation from relational meaning-making.
5. Construal in Practice
Consider a chatbot producing correct responses:
-
System: the linguistic and cognitive potential embedded in training data.
-
Instance: the chatbot’s output in a given interaction.
-
Construal: the human experience of interpreting or making sense of that output.
Meaning exists only in relational engagement, not within the chatbot itself. Computation can generate apparent intelligence, but construal actualises meaning.
6. Conclusion
Searle’s Chinese Room highlights a crucial distinction:
-
Value coordination (computation) ≠ meaning-making (construal).
-
Meaning emerges relationally, not syntactically.
-
Machines that act on potential without perspectival actualisation can coordinate symbols but cannot instantiate understanding.
Relational ontology preserves the insight of the Chinese Room while dissolving the paradox: meaning is always first-order, perspectival, and relationally enacted.
No comments:
Post a Comment