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 resource to be executed. We use both mathematical and numerical analyses of shared representations in neural network architectures to articulate the 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 resources by different tasks, reflects the optimization of a fundamental tradeoff 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 tradeoff between control-dependent processing versus automaticity, and relates to a number of other fundamental principles and phenomena concerning cognitive function, and computation more generally.

A fundamental question in memory research is how different forms of memory interact. Previous research has shown that people rely on working memory (WM) in short-term recognition tasks; a common view is that episodic memory (EM) only influences performance on these tasks when WM maintenance is disrupted. However, retrieval of memories from EM has been widely observed during brief periods of quiescence, raising the possibility that EM retrievals during maintenance—critically, before a response can be prepared—might affect short-term recognition memory performance even in the absence of distraction. We hypothesized that this influence would be mediated by the lingering presence of reactivated EM content in WM. We obtained support for this hypothesis in three experiments, showing that delay-period EM reactivation introduces incidentally associated information (context) into WM, and that these retrieved associations negatively impact subsequent recognition, leading to substitution errors (Experiment 1) and slowing of accurate responses (Experiment 2). FMRI pattern analysis showed that slowing is mediated by the content of EM reinstatement (Experiment 3). These results expose a previously hidden influence of EM on WM, raising new questions about the adaptive nature of their interaction.Electronic supplementary materialThe online version of this article (10.3758/s13415-018-00674-z) contains supplementary material, which is available to authorized users.

A fundamental question in memory research is how different forms of memory interact. Previous research has shown that when working memory (WM) is overloaded or maintenance is interrupted in short-term memory tasks, humans and animals can rely on episodic memory (EM) to support performance. Furthermore, episodic memory reactivation appears also to occur on its own (i.e., irrespective of demand), even during the short delays typically used in WM experiments. Based on these observations, we hypothesized that EM reinstatements would affect WM, even in the absence of any interference. Using novel behavioral and neural signatures of the effect of EM on WM, we show that EM introduces additional information into WM by reinstating incidental associations (context) present during initial encoding. The first two experiments establish that the influence of encoding context is evident both in errors (Experiment 1) and in slowing of responses (Experiment 2). Experiment 3 shows that fMRI evidence of EM reinstatement during the delay predicts response times on each trial. Modeling WM search using a Drift-Diffusion Model (DDM), we show that fits improve when trial drift rate varies with fMRI evidence for reinstatement during that trial's delay period. These results expose a previously hidden interaction between WM maintenance and EM replay, and raise new questions about the adaptive nature of the interplay between these mechanisms.