System makes a good match or indicates no good matches in memory. Can account for low frequency information. TOP Connectionist Models or Parallel distributed processing model Computational power of the parallel processing computer had made research involving these models more feasible. Particularly suited to pattern recognition problems. Connectionist models are composed of a large number of units.
Information is represented by a pattern of activation across input, output, and hidden units.
All units are connected by links Each link has a weight associated with it. Weights change as the system learns the domain representation. Learning involves adjusting connection weights to new values after presentation of input and output pairs. The model has learned when it consistently produces the correct output in response to a pattern of inputs. Don't know whether this model provides a viable model of neuropsychological decision making.