 In the 1960s and 1970s, cognitive scientists almost universally modeled mental activity as rule-governed symbol manipulation. In the 1980s, connectionism gained currency as an alternative computational framework. Connectionists employ computational models, called neural networks, that differ quite significantly from Turing-style models. There is no central processor. There are no memory locations for symbols to be inscribed. Instead, there is a network of nodes bearing weighted connections to one another. During computation, waves of activation spread through the network. A node’s activation level depends upon the weighted activations of the nodes to which it is connected. Nodes function somewhat analogously to neurons, and connections between nodes function somewhat analogously to synapses. One should receive the neurophysiological analogy cautiously, as there are numerous important differences between neural networks and actual neural configurations in the brain (Bechtel &amp; Abramson 2002: 341–343; Bermúdez 2010: 237–239; Clark 2014: 87–89; Harnish 2002: 359–362).