Toward a Rational and Mechanistic Account of Mental Effort

Abstract

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.

Empirical evidence for resource-rational anchoring and adjustment

“…By contrast, when time was costly then our participants were faster and less accurate, their adjustments appeared to be smaller, and their anchoring bias was larger. This is consistent with the rational allocation of mental e ort (Shenhav et al, 2017) and our hypothesis that the number of adjustments is chosen to achieve an optimal speed-accuracy tradeo . However, since our experiment used only two levels of time cost and error cost, it remains to be investigated whether the number of adjustments changes gradually with its costs and benefits-as predicted by the resource-rational model-or whether people can only choose between a fast versus a slow mode of numerical estimation as postulated by dual-systems theories (Evans, 2008;Kahneman, 2011).…”

Rational metareasoning and the plasticity of cognitive control

“…where ω is the opportunity cost per unit time, t is the duration of the controlled process, and cost(c) is the intrinsic cost of exerting the control signal c. While the first term captures that goal-directed control processes, such as planning, can take significantly longer than automatic processes, such as habits, the second term captures that due to interference between overlapping pathways the cost of a control signal increases with its intensity [13] even when control intensity accelerates the decision process [11,50,51]. In many real-world scenarios and some experiments, the opportunity cost is time-varying.…”

“…In broad accord with the predictions of the EVC theory, previous research has found that control specification is context-sensitive [8,9] and modulated by reward across multiple domains [10,11], such as attention, response inhibition, interference control, and task switching. While previous theories account for that fact that people's performance in these task is sensitive to reward [7,[12][13][14], it remains unclear how these dependencies arise from people's experience. Recently, it has been proposed that the underlying mechanism is associative learning [15,16].…”

Empathy is hard work: People choose to avoid empathy because of its cognitive costs

“…As such, observing that increasing the time to empathize only increases effort avoidance suggests that effort per se plays a critical role. Future work might more systematically explore how demands for working memory and cognitive control (Shenhav et al, 2017) manifest in empathic contexts, shaping how people decide whether to empathize. For example, studies might test the association between empathic choices in the Empathy Selection Task and classical measures of cognitive control, to examine how domain-general effort avoidance preferences in a non-empathic task correspond to selecting empathic effort.…”