 Moreover, connectionists argue that information processing as it occurs in connectionist networks more closely resembles some features of actual human cognitive functioning. For example, whereas on the classical view learning involves something like hypothesis formation and testing (Fodor 1981c), on the connectionist model it is a matter of evolving distribution of “weights” (strengths) on the connections between nodes, and typically does not involve the formulation of hypotheses regarding the identity conditions for the objects of knowledge. The connectionist network is “trained up” by repeated exposure to the objects it is to learn to distinguish; and, though networks typically require many more exposures to the objects than do humans, this seems to model at least one feature of this type of human learning quite well. (Cf. the sonar example in Churchland 1989.)