Inference in Connectionist Networks

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We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency—as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? We briefly review work in connectionist modeling that attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds.

収録刊行物

  • 認知科学

    認知科学 10 (1), 45-57, 2003

    日本認知科学会

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