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## The Computer Metaphor of Mind

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**The Computer Metaphor of Mind**By Aaron Clarke**The Computer Metaphor of Mind**• What is the computer metaphor of mind? • How did this metaphor come about? • Key players: • George A. Miller – Language and the computer metaphor of mind • McCulloch – Pitts – Neural Networks. • Concluding remarks**The Computer Metaphor of Mind**Q: What is the computer metaphor of mind? A: The brain is a computer.**How Did This Metaphor Come About?**• Key Players: • George A. Miller – Language and the computer metaphor of mind • McCulloch – Pitts – Neural Networks.**George A. Miller**• Started off as a behaviourist. • Interested in information theory. • Helped develop the idea that the similarities between humans and machines were real**Language**• The rules of language are analogous to a computer program. • The set of universal rules in grammar is analogous to the operating system of the human mind. • Mind was thus necessary to explain language. • Minds operate according to a set of universal logical rules. • A theory of mind could be modeled in the form of a computer program.**Warren McCulloch**• A neuropsychiatrist. • Provided the biological information necessary to appropriately model the behaviours of neurons.**Walter Pitts**• A mathematician. • Provided the math necessary to model the behaviour of neurons.**Body**Axon Model Neuron**Logical Operations**“If A then B” Neuron A Neuron B**Logical Operations**“If A or B then C” Neuron A Neuron B Neuron C**Logical Operations**“If A and B then C” Neuron A Neuron C Neuron B**Logical Operations**Neuron A “If A and not B then C” Neuron C Neuron B**Summary**• The functional relationships between neurons were represented in terms of Boolean logic. • The mental functions of reasoning could be seen as being embodied in the physiology of the brain.**Concluding Remarks**• McCulloch and Pitts’s networks were possible neural networks, and were useful models. • Their model is a useful tool for rigorous symbolic treatment of known neural nets and provides an easy method for constructing hypothetical neural networks with known properties.