A source activation theory of working memory: cross-task prediction of performance in ACT-R

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 paper, we describe a model that achieves the next step in this progression: predicting individual participants’ performance across multiple tasks after estimating a single individual difference parameter. We demonstrate this capability in the context of a model of working memory, where the individual difference parameter for each participant represents working memory capacity. Specifically, our model is able to make zero-parameter predictions of individual participants’ performance on a second task after separately fitting performance on a preliminary task. We argue that this level of predictive ability offers an important test of the theory underlying our model. ∗ This work was supported by Air Force Office of Scientific Research Grant F49620-97-1-0455 and by Office of Naval Research Grant N00014-95-0233. We would like to thank Scott Filippino and Becky Stoltzer for their assistance in collecting the data and Christian Lebiere for many useful discussions. We would also like to thank John R. Anderson for comments on an earlier draft of this paper. Correspondence concerning this article should be addressed to Marsha C. Lovett, Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213. Electronic mail may be sent via the Internet to [email protected].