ABSTRACT
An important goal of cognitive science is to understand human cognition. Good models of cognition can be predictive — describing how people are likely to react in different scenarios — as well as prescriptive — describing limitations in cognition and potentially ways in which the limitations might be overcome. In a sense, the benefits of having cognitive models are similar to the benefits individuals accrue in building their own internal model. To quote Craik [1943]:
If the organism carries a ‘small-scale model’ of external reality and of its own possible actions within its head, it is able to try out various alternatives, conclude which is the best of them, react to future situations before they arise, utilize the knowledge of past events in dealing with the present and future, and in every way to react in a much fuller, safer, and more competent manner to the emergencies which face it. (p. 61)
Among the important questions facing cognitive scientists are how such models are created and how they are represented internally. Craik emphasizes the importance of the predictive power of models, and it is the model’s ability to make accurate predictions that is the ultimate measure of the model’s value. One important value of computers in cognitive science is that computer simulations provide a means to instantiate theories and to concretely test their predictive power. Further, implementation of a theory in a computer model forces theoreticians to face practical issues that they may never have otherwise considered.
