Methodologies for the Computer Modeling of Human Cognitive Processes

Researchers interested in human cognitive processes have long used computer simulations to try to identify the principles of cognition. The strategy has been to build computational models that embody a set of principles and then the examine how well the models capture human performance in cognitive tasks. Computational modeling has both strengths and weaknesses in its usefulness in facilitating the development of cognitive theories. Perhaps its most important strength is that, in developing mechanistic theories of cognitive processes, it is critical to have a language or formalism for describing dynamic information processing in detail. Developing a working implementation of a model makes it possible both to verify the completeness and internal coherence of its underlying theoretical principles, and to generate detailed, quantitative predictions of the model when tested in novel circumstances. More generally, computational modeling can lead to new theoretical discoveries by providing a means of exploring the implications of a set of ideas or principles. There are, however, a number of potential pitfalls in the application of computational modeling to cognitive processes that must be kept in mind. First, developing an explicit implementation always involves augmenting core theoretical claims with less central, ancillary assumptions, and it can be difficult to evaluate the extent to which the behavior of the model depends on the latter instead of the former. Moreover, models may be underconstrained and are applied to modeling data post hoc without a sufficient consideration of whether there are patterns of data that the model could not produce (see Roberts & Pashler, 2000). Finally, models are often put forward as theories in an of themselves, without sufficient analysis and explanation of why the model accounts for the data (see McCloskey, 1991). In general, computational modeling would seem to be most productive when it is carried out in the service of clarifying theoretical claims and when it it tightly integrated with corresponding empirical studies. A number of formalisms have been applied to modeling cognitive processes, including production systems, discrimination nets, exemplar-based models, and connectionist models. The current chapter presents a brief summary of each of these approaches along with some illustrati ve examples. Particular emphasis is place on connectionist modeling as this is the framework that has been most widely applied to simulating neuropsychological phenomena.