  There is continuing controversy about whether Connectionism has in-principle limitations that disqualify it as a general model of cognitive processing (see Fodor &amp; Pylyshyn 1988 and the literature this critique spawned, which is reviewed in Garson 2010). But there is a practical problem that is less controversial, and to understand it, we need to consider more closely how Connectionist nets learn. Imagine that one wants the net to learn the difference between (photos of) male and female faces. A set of input nodes will code the photo, activation will pass through a set of intermediate nodes, and an answer will appear on the output nodes. If the output on a particular input is incorrect (a male is misidentified as a female for example), the algorithm that governs the dynamics of the network automatically adjusts the weights of the connections between the various intermediate-layer nodes ‘in the right direction’ and more inputs are cycled through the system. When the output is correct over some range of inputs, the net has been ‘trained up’; it has successfully learned to tell male from female faces in photos. The art in Connectionist modeling is to discover the best network structure and the right algorithm for adjusting the weightings. The problem is that such networks learn very slowly; they often need hundreds of thousands of cycles of inputs, outputs, and weight adjustments. But humans and animals learn many things very quickly, sometimes even from one instance and often from a small set of instances (Garcia et al. 1955; Markman 1989).