"Programming Cognition: Computer Simulations of Individual Differences in Human Working Memory Performance"
Over the decades, computational models of human cognition have advanced from programs that produce output similar to that of human problem solvers to systems that mimic both the products and processes of human performance. In this presentation, a model is described that achieves the next step in this progression: predicting individual participants' performance across multiple tasks. This capability will be demonstrated in the context of a model of working memory built within the framework of Anderson's (Anderson & Lebiere, 1998) ACT-R theory. Working memory resources are needed for the processing and maintenance of information during cognitive tasks. One salient characteristic of working memory resources is that they are limited and, as task complexity increases, task performance decreases due to the limit on working memory resources (Anderson, Reder, & Lebiere, 1996). While many models have been developed to capture the effects of limited working memory resources on performance, most do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. The model presented here accounts for differences in working memory capacity in terms of a quantity called source activation, which is used to maintain goal-relevant information in an available state. This model will be shown to capture the working memory performance of individual subjects at a fine level of detail across several experiments.