This proposal seeks funds for instrumentation needed for the further development and assessment of parallel distributed processing (PDP) models of a number of different aspects of human cognitive processes. We seek to study a class of PDP models in which processing occurs through the interaction of a large number of intrinsically variable processing units whose activation is based on a time-average of their input from other units. The knowledge that governs skilled processing is stored in the strengths of the connections among the units, and the acquisition of the knowledge that underlies skilled processing occurs through gradual adjustments in the strengths of the connections triggered by processing experience. Previous work has shown the potential of the approach for a number of applications, but continued progress requires an increase in computational resources. In some cases, it has not been possible to explore a realistically diverse range of training examples, thus limiting the opportunity to test the adequacy of the principles to model human performance data. In other cases, simplified assumptions about processing have been made, and it is now clear that these simplifications can result in inadequate fits to experimental data. In both kinds of cases, the ability to assess the degree to which the models can account for experimental data has been compromised. We therefore seek an upgrade to our existing 5-year old vectorizing 'minisupercomputer' to a newer, more powerful model.
