连接主义是一种关于思维如何运作的创新理论，它基于大脑及其神经元的工作方式。根据这一理论，尽管我们每个单独的神经元本身的计算能力非常小，但当它们相互组合在一起时，它们就有了巨大的计算能力。Ken和John在嘉宾James McClelland的陪同下讨论了连接主义者模型的优缺点。

理解我们学习的方式是心理学中一个古老的问题，根据麦克里兰的说法，围绕学习的问题激发了连接主义者的观点。老式的人工智能(AI)学习模型指出，因为我们的大脑从出生那天起就以特定的方式构建，我们的思想也必须以特定的方式预先构建。例如，有人认为，人类的语言是由我们的基因预先指定的。麦克里兰认为，不幸的是，这种人工智能方法没有接触到这样一个事实，即我们交谈和互动的方式是由我们的经验和我们所学到的东西所塑造的。

McClelland explains that connectionism took hold in the early 1980s when scientists began making better computer models of neurons and way neurons work together in systems. The connectionist theory of learning is that neuron’s are interconnected, and when neuron’s change connections the brain system learns.

John questions McClelland about the relation between connectionism and an older theory, associationism. McClelland agrees that connectionism is a modern version of the same idea but with one key distinction. Associationism is the theory that associations are formed in our minds when two events occur together; we learn by contiguity, and when something new happens we understand it by generalizing and approximating according to our previous association. According to McClelland, the weakness in the associationist argument is the fact that it doesn’t account for how we learn to re-associate events in our minds. We don’t just approximate to understand new information, we learn new information. The connectionist system learns by adjusting the connections between neurons.

John, Ken and McClelland continue the conversation. They discuss practical applications for connectionist systems in computer science, the effect our emotions have on learning, and some objections to connectionism.

• Roving Philosophical Report(seek to 5:00): April Dembosky talks to us about talking to computers.