• The tests for terminating and exhaustive processes of Townsend.
• The theory of parallel/serial equivalences of Townsend.
• The dynamical decision modeling of Busemeyer and Townsend.
• The multi-stage memory model of Atkinson and Shiffrin.
• The automaticity/attention theory of Shiffrin and Schneider.
• The memory retrieval theory (SAM) of Raaijmakers and Shiffrin.
• The multidimensional-scaling and context model for classification of Nosofsky.
• The neural network modeling of learning and concepts by Kruschke.
• The similarity/alignment models of Goldstone.

The need for researchers trained in the modeling of cognitive processes has increased significantly in recent years due to the rapid development of two new sub-disciplines: neural net and connectionist modeling, and non-linear dynamical systems modeling. These approaches have been used increasingly to further cognitive theorizing and develop applications for clinical, educational, and industrial purposes.

Our core faculty have expertise in a range of techniques useful for these fields as well as traditional areas including:

• Dynamic systems and non-linear dynamical modeling (utilizing differential equations, linear and nonlinear systems and sometimes transform theory and complex variables);
• Probability theory and stochastic processes (including random walk and diffusion processes);
• Abstract algebra and foundational measurement theory (including the use of real variables and measure theory);
• Point set and algebraic topology and differential geometry and topology;
• Mathematical logic and game theory;
• Neural net, distributed memory and connectionist modeling.