Rationalizing constraints on the capacity for cognitive control

Humans are remarkably limited in: (i) how many control-dependent tasks they can execute simultaneously, and (ii) how intensely they can focus on a single task. These limitations are universal assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive. This feature review draws on recent insights from psychology, neuroscience, and machine learning, to suggest that constraints on cognitive control may result from a rational adaptation to fundamental, computational dilemmas in neural architectures. The reviewed literature implies that limitations in multitasking may result from a trade-off between learning efficacy and processing efficiency and that limitations in the intensity of commitment to a single task may reflect a trade-off between cognitive stability and flexibility. To explain human behavior, most general theories of cognition assume, rather than explain, limitations in: (i) the number of control-dependent tasks that can be performed simultaneously (i.e., multitasked); and (ii) the amount of cognitive control that can be allocated to a single task.Limitations in the capability to multitask can be explained by representation sharing between tasks. Computational modeling suggests that neural systems trade the benefits of shared representation for rapid learning and generalization (a mechanism increasingly exploited in machine learning) against constraints on multitasking performance.Experimental studies posit a trade-off between cognitive stability and cognitive flexibility. Computational analyses of this trade-off suggest that adaptations to high demands for flexibility limit the amount of control that can be allocated to a single task.