How the Brain Decides When to Work and When to Rest: Dissociation of Implicit-Reactive from Explicit-Predictive Computational Processes

Abstract

A pervasive case of cost-benefit problem is how to allocate effort over time, i.e. deciding when to work and when to rest. An economic decision perspective would suggest that duration of effort is determined beforehand, depending on expected costs and benefits. However, the literature on exercise performance emphasizes that decisions are made on the fly, depending on physiological variables. Here, we propose and validate a general model of effort allocation that integrates these two views. In this model, a single variable, termed cost evidence, accumulates during effort and dissipates during rest, triggering effort cessation and resumption when reaching bounds. We assumed that such a basic mechanism could explain implicit adaptation, whereas the latent parameters (slopes and bounds) could be amenable to explicit anticipation. A series of behavioral experiments manipulating effort duration and difficulty was conducted in a total of 121 healthy humans to dissociate implicit-reactive from explicit-predictive computations. Results show 1) that effort and rest durations are adapted on the fly to variations in cost-evidence level, 2) that the cost-evidence fluctuations driving the behavior do not match explicit ratings of exhaustion, and 3) that actual difficulty impacts effort duration whereas expected difficulty impacts rest duration. Taken together, our findings suggest that cost evidence is implicitly monitored online, with an accumulation rate proportional to actual task difficulty. In contrast, cost-evidence bounds and dissipation rate might be adjusted in anticipation, depending on explicit task difficulty.

Author Summary

Imagine that ahead of you is a long time of work: when will you take a break? This sort of issue – how to allocate effort over time – has been addressed by distinct theoretical fields, with different emphasis on reactive and predictive processes. An intuitive view is that you start working, stop when you are tired, and start again when fatigue goes away. Biologically, this means that decisions are taken when some physiological variable reaches a given bound on the risk of homeostatic failure. In a more economic perspective, fatigue translates into effort cost, which must be anticipated and compared to expected benefit before engaging an action. We proposed a computational model that bridges these perspectives from sport physiology and decision theory. Decisions are made in reaction to bounds being reached by an implicit cost variable that accumulates during effort, at a rate proportional to task difficulty, and dissipates during rest. However, some latent parameters (bounds and dissipation rate) are adjusted in anticipation, depending on explicit costs and benefits. This model was supported by behavioral data obtained using a paradigm where participants squeeze a handgrip to win a monetary payoff proportional to effort duration.