Computational models as aids to better reasoning in psychology.
We are using Stephan Lewandowsky’s book in my computational modeling course to introduce some of the basic issues of developing a formal model and fitting it to data. The book is a great, practical introduction to modeling for beginners. There’s a related article that I ran across in Current Directions in Psychological Science that points out just how hard reasoning about scientific concepts can be, and how models can be “assistive technology” for thinking about abstract ideas. If you are wondering why people are so exciting about this “computational modeling” idea, this is a nice argument!
Farrell, S. and Lewandowsky, S. (2010). “Computational models as aids to better reasoning in psychology” Current directions in psychological science, 19(5), 329-335. [pdf]
Abstract: “Scientists can reason about natural systems, including the mind and brain, in many ways, with each form of reasoning being associated with its own set of limitations. The limitations on human reasoning imply that the process of reasoning about theories and communicating those theories will be error prone; we must therefore be concerned about the reproducibility of theories whose very nature is shaped by constraints on human reasoning. The problem of reproducibility can be alleviated by computational modeling, which maximizes correspondence between the actual behavior of a posited system and its behavior inferred through reasoning and increases the fidelity of communication of our theories to others.”
Choice quote: “…computational modeling helps ensure reproducibility in scientific thinking. By implementing a model as a computer program or a set of equations, another researcher can take our model and exactly reproduce our predictions.”