  One way to approach this discrepancy is to see it as due to the fact that in the typical Connectionist set up, the weights between nodes are initially set to random values, and are (very) slowly reset on the basis of small adjustments. But the fact that the initial weightings provide no prior information is arguably an artifact of the modeler’s Empiricist commitment to have all the learning ‘come from experience’. There is nothing in the general structure of Connectionist models that would prevent the modeler from starting with a highly constrained set of weightings—in this case one that already holistically contains information of the general features of human faces, and perhaps information about differences between male and female faces. The upshot, then, is that although most actual Connectionist models are Empiricist-friendly in their format and in their representational commitments, they can also be implemented in a way that is congenial to Nativist ideas. The prior information that the Nativist claims is part of the initial state of the organism can be realized by setting the initial patterns of weightings between the nodes in the network in such a way that learning will happen much more quickly. So while Connectionism may avoid the very general commitment to Nativism that some have argued is built into the Classical conception, it is neutral on the question of whether learning in a particular domain is wholly based on experience or uses innate information (suitably distributed across networks).