Computational modeling of human performance in a sequence learning experiment
Rainer Spiegel, Ian P. L. McLaren · 2004
This paper follows on from earlier work that our colleagues and ourselves presented at IJCNN 2001, IJCNN 2002 and FUZZ-IEEE 2002. We referred to simulations of a recurrent network and of an adaptive system that was partly based on a recurrent network. Both models were successful in simulating human sequence learning in a reaction time paradigm that is widely used in cognitive science and experimental psychology. We argued that these models were not only successful in simulating human learning, but also in predicting successful generalization to novel sequences where humans show generalization, and a failure to generalize to novel sequences when humans fail. In this paper we present data from a novel experiment and novel simulations on a longer version of this serial reaction time task. Under these conditions the task appears to be more difficult to master for the human subjects and therefore more complex. Accordingly, humans were not able to learn that task at all. Their failure is predicted by both computational models. Combining these results with the earlier findings from the previous conferences suggests that both successful and unsuccessful human performance can be predicted by the computational models considered here.