Proceedings of the Annual Meeting of the Cognitive Science Society

People learn and use complex sequential actions on a daily basis, despite living in a high-dimensional environment andbody. Sequential action learning is sometimes studied in cognitive psychology using button-pressing tasks such as Nissenand Bullemers (1987) serial respone time (SRT) task. However, the SRT task only measures the speed of button presses,neglecting the richand difficult to controltrajectory of the arm, which can show predictive movements and other contextualeffects. In this study, we evolve neural networks to carry out a mouse-based SRT task under conditions of differingprediction uncertainty. We replicate behaviors found in a recent human experiment, and explore ramifications for humansequence learning.