Proceedings of the Annual Meeting of the Cognitive Science Society

One of the most salient and well-recognized features of humangoal-directed behavior is our limited ability to conduct mul-tiple demanding tasks at once. Previous work has identifiedoverlap between task processing pathways as a limiting fac-tor for multitasking performance in neural architectures. Thisraises an important question: insofar as shared representationbetween tasks introduces the risk of cross-talk and thereby lim-itations in multitasking, why would the brain prefer shared taskrepresentations over separate representations across tasks? Weseek to answer this question by introducing formal considera-tions and neural network simulations in which we contrast themultitasking limitations that shared task representations incurwith their benefits for task learning. Our results suggest thatneural network architectures face a fundamental tradeoff be-tween learning efficiency and multitasking performance in en-vironments with shared structure between tasks.