Terrence C. Stewart

Postdoc

Terrence C. Stewart

  • tcstewar@uwaterloo.ca
  • c.v./resume (as of Nov, 2012)

  • I am one of the researchers behind Spaun, the first biologically realistic brain simulation that can perform multiple tasks.

  • I am one of the developers behind Nengo, an open-source software package for developing simulated brains.

Bio

I am a post-doctoral research associate working with Chris Eliasmith and Paul Thagard. My initial training was as an engineer (B.A.Sc. in Systems Design Engineering, University of Waterloo, 1999), my masters involved applying experimental psychology on simulated robots (M.Phil. in Computer Science and Artificial Intelligence, University of Sussex, 2000), and my Ph.D. was on cognitive modelling (Ph.D. in Cognitive Science, Carleton University, 2007).

Research Interests

I believe that making progress in understanding any phenomenon as complex as cognition requires the construction of computational models. The primary role of these models is to test theories: as theories become more complex, we need computational models to let us determine the quantitative predictions of these theories. These models should also be mechanistic. That is, they should be process models where the behaviour of the overall system is caused by the interaction of internal components over time. These components should then correspond to the components of the real system. To achieve this, my main research interests are in the development of modelling tools to support large- scale cognitive models. This has involved both high-level cognitive architectures (such as ACT-R) and detailed neural models (such as the Neural Engineering Framework). Of particular interest is models involving cognitive reasoning, experience-based learning, and reinforcement learning to interact with a complex environment. In concert with this, I have worked on statistical tools for comparing modelling results with empirical results, and how such results should be interpretted. In particular, instead of the standard approaches of finding best-fit parameter settings based on minimizing the mean squared error, I advocate finding a range of parameter settings for which the model and reality are not statistically significantly different.

Publications