In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits. This review explores recent advances in computational modeling and empirical research aimed at addressing these questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how we manage those limited cognitive resources. We conclude by identifying remaining challenges for theoretical accounts of mental effort as well as possible applications of the available findings to understanding the causes of and potential solutions for apparent failures to exert the mental effort required of us.

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

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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. The role of capacity constraints in human cognitionOne of the most remarkable features of human cognition is the ability to rapidly adapt behavior in a changing world. This is often attributed to the capacity for cognitive control: the ability to flexibly direct behavior in pursuit of a goal (Box 1). Cognitive control is engaged by all of the higher mental faculties that distinguish humans from other species, including reasoning, problem solving, planning, and the use of symbolic language [1]. Yet, humans are strikingly limited in how many control-demanding tasks (see Glossary) they can perform simultaneously (e.g., reading a document while listening to a friend) or how intensely they can focus on a single task (e.g., parsing a mathematical equation in a noisy environment). The significance of these limitations is not only apparent in daily life. They are also a fundamental premise of general theories of human cognition (e.g. [2-7],). These theories posit that the exertion of cognitive control is associated with a cost, and that humans consider this cost when making decisions about how to allocate control [7][8][9]. The notion of a cost and concomitant constraints on control, can help integrate a wide range of empirical findings concerning the allocation of mental effort [10][11][12][13], the selection between cognitive heuristics [5], planning [14,15], or cognitive impairments in depression [16]. Yet, none of these theories provides an explanation for why control-dependent processing would be subject to these limitations in the first place.Here, we review two fundamental, computational dilemmas that arise in neural systems and suggest that these provide a rational account of constraints on cognitive control. First, we review empirical and computational evidence suggesting a trade-off between the rapid acquisition of novel tasks (learning efficacy), that is promoted by sharing representations across tasks, on the one hand; and multitasking capability (processing efficiency) that is achieved by separating representations and dedicating them to individual tasks, on the other hand. The work reviewed suggests that limitations in the ability to simultaneously execute multiple tasks may...

The shutdown of schools in response to the rapid spread of COVID-19 poses risks to the education of young children, including a widening education gap. In the present article, we investigate how school closures in 2020 influenced the performance of German students in a curriculum-based online learning software for mathematics. We analyzed data from more than 2,500 K-12 students who computed over 124,000 mathematical problem sets before and during the shutdown, and found that students’ performance increased during the shutdown of schools in 2020 relative to the year before. Our analyses also revealed that low-achieving students showed greater improvements in performance than high-achieving students, suggesting a narrowing gap in performance between low- and high-achieving students. We conclude that online learning environments may be effective in preventing educational losses associated with current and future shutdowns of schools.