Behavioral and economic theories have long maintained that actions are chosen so as to minimize demands for exertion or work, a principle sometimes referred to as the "law of less work." The data supporting this idea pertain almost entirely to demands for physical effort. However, the same minimization principle has often been assumed also to apply to cognitive demand. We set out to evaluate the validity of this assumption. In six behavioral experiments, participants chose freely between courses of action associated with different levels of demand for controlled information processing. Together, the results of these experiments revealed a bias in favor of the less demanding course of action. The bias was obtained across a range of choice settings and demand manipulations, and was not wholly attributable to strategic avoidance of errors, minimization of time on task, or maximization of the rate of goal achievement. Remarkably, the effect also did not depend on awareness of the demand manipulation. Consistent with a motivational account, avoidance of demand displayed sensitivity to task incentives and co-varied with individual differences in the efficacy of executive control. The findings reported, together with convergent neuroscientific evidence, lend support to the idea that anticipated cognitive demand plays a significant role in behavioral decisionmaking.

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.

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.

Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system's task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.

Daily life frequently offers a choice between activities that are profitable but mentally demanding (cognitive labor) and activities that are undemanding but also unproductive (cognitive leisure). Although such decisions are often implicit, they help determine academic performance, career trajectories, and even health outcomes. Previous research has shed light both on the executive control functions that ultimately define cognitive labor and a ‘default mode’ of brain function that accompanies cognitive leisure. However, little is known about how labor/leisure decisions are actually made. Here, we identify a central principle guiding such decisions. Results from three economic-choice experiments indicate that the motivation underlying cognitive labor/leisure decision-making is to strike an optimal balance between income and leisure, as given by a joint utility function. The results reported establish a new connection between microeconomics and research on executive function. They also suggest a new interpretation of so-called ego-depletion effects, and a potential new approach to such phenomena as mind-wandering and self-control failure.