On the Rational Boundedness of Cognitive Control: Shared Versus Separated Representations

One of the most fundamental and striking limitations of human cognition appears to be a constraint in the number of control-dependent processes that can be executed at one time. This constraint motivates one of the most influential tenets of cognitive psychology: that cognitive control relies on a central, limited-capacity processing mechanism that imposes a seriality constraint on processing. Here we provide a formally explicit challenge to this view. We argue that the causality is reversed: the constraints on control-dependent behavior reflect a rational bound that control mechanisms impose on processing, to prevent processing interference that arises if two or more tasks engage the same representations required to perform the tasks. We use both mathematical and numerical analyses of shared representations in neural network architectures to provide a formal grounding for this argument–historically known as "multiple-resource theory"–and demonstrate its ability to explain a wide range of phenomena associated with control-dependent behavior. Furthermore, we argue that the need for control, arising from the shared use of the same representations by different tasks, reflects the optimization of a fundamental trade-off intrinsic to network architectures: the increase in learning efficacy associated with the use of shared representations, versus the efficiency of parallel processing (i.e., multitasking) associated with task-dedicated representations. The theory helps frame a formally rigorous, normative approach to the trade-off between control-dependent processing versus automaticity, and how this relates to a number of other fundamental principles and phenomena concerning cognitive function, and computation more generally.