Effort Reinforces Learning

Humans routinely learn the value of actions by updating their expectations based on past outcomes - a process driven by reward prediction errors (RPEs). Importantly, however, implementing a course of action also requires the investment of effort. Recent work has revealed a close link between the neural signals involved in effort exertion and those underpinning reward-based learning, but the behavioral relationship between these two functions remains unclear. Across two experiments, we tested healthy male and female human participants (N = 140) on a reinforcement learning task in which they registered their responses by applying physical force to a pair of hand-held dynamometers. We examined the effect of effort on learning by systematically manipulating the amount of force required to register a response during the task. Our key finding, replicated across both experiments, was that greater effort increased learning rates following positive outcomes and decreased them following negative outcomes, which corresponded to a differential effect of effort in boosting positive RPEs and blunting negative RPEs. Interestingly, this effect was most pronounced in individuals who were more averse to effort in the first place, raising the possibility that the investment of effort may have an adaptive effect on learning in those less motivated to exert it. By integrating principles of reinforcement learning with neuroeconomic approaches to value-based decision-making, we show that the very act of investing effort modulates one's capacity to learn, and demonstrate how these functions may operate within a common computational framework.SIGNIFICANCE STATEMENT Recent work suggests that learning and effort may share common neurophysiological substrates. This raises the possibility that the very act of investing effort influences learning. Here, we tested whether effort modulates teaching signals in a reinforcement learning paradigm. Our results showed that effort resulted in more efficient learning from positive outcomes and less efficient learning from negative outcomes. Interestingly, this effect varied across individuals, and was more pronounced in those who were more averse to investing effort in the first place. These data highlight the importance of motivational factors in a common framework of reward-based learning, which integrates the computational principles of reinforcement learning with those of value-based decision-making.