Proceedings of the Annual Meeting of the Cognitive Science Society

One of the most compelling characteristics of controlled pro-cessing is our limitation to exercise it. Theories of control allo-cation account for such limitations by assuming a cost of con-trol that constrains how much cognitive control is allocated toa task. However, this leaves open the question of why sucha cost would exist in the first place. Here, we use neural net-work simulations to test the hypothesis that constraints on cog-nitive control may reflect an optimal solution to the stability-flexibility dilemma: allocating more control to a task results ingreater activation of its neural representation but also in greaterpersistence of this activity upon switching to a new task, yield-ing switch costs. We demonstrate that constraints on controlimpair performance of any given task but reduce performancecosts associated with task switches. Critically, we show thatoptimal control constraints are higher in environments with ahigher probability of task switches.