As noted earlier, the 1980s saw the re-emergence of connectionist computational models within mainstream cognitive science theory (e.g., Feldman and Ballard 1982; Rumelhart and McClelland 1986; Gluck and Rumelhart 1990). We have already briefly characterized connectionist models in our discussion of connectionist parsing. But the connectionist paradigm was viewed as applicable not only to specialized functions, but to a broad range of cognitive tasks including recognizing objects in an image,  recognizing speech, understanding language, making inferences, and guiding physical behavior. The emphasis was on learning, realized by adjusting the weights of the unit-to-unit connections in a layered neural network, typically by a back-propagation process that distributes credit or blame for a successful or unsuccessful output to the units involved in producing the output (Rumelhart and McClelland 1986).