Learning in parallel networks: simulating learning in a probabilistic system
BYTE - Lecture notes in computer science Vol. 174
Artificial intelligence: the very idea
Artificial intelligence: the very idea
Artificial intelligence
Logic programming and knowledge engineering
Logic programming and knowledge engineering
Intelligence without representation
Artificial Intelligence
The owl and the electric encyclopedia
Artificial Intelligence
Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution
Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution
Handbook of AI
Genuine Representation in Artificial Systems
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
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The confusion between cognitive states and the content of cognitive states that gives rise to psychologism also gives rise to reverse psychologism. Weak reverse psychologism says that we can study cognitive states by studying content – for instance, that we can study the mind by studying linguistics or logic. This attitude is endemic in cognitive science and linguistic theory. Strong reverse psychologism says that we can generate cognitive states by giving computers representations that express the content of cognitive states and that play a role in causing appropriate behaviour. This gives us strong representational, classical AI (REPSCAI), and I argue that it cannot succeed. This is not, as Searle claims in his Chinese Room Argument, because syntactic manipulation cannot generate content. Syntactic manipulation can generate content, and this is abundantly clear in the Chinese Room scenano. REPSCAI cannot succeed because inner content is not sufficient for cognition, even when the representations that carry the content play a role in generating appropriate behaviour.