Motivation is key for performance in domains such as work, sport, and learning. Research has established that motivation and the willingness to invest effort generally increase as a function of reward. However, this view struggles to explain some empirical observations-for example, in the domain of sport, athletes sometimes appear to lose motivation when playing against weak opponents-this despite objective rewards being high. This and similar evidence highlight the role of subjective value in motivation and effort allocation. To capture this, here, we advance a novel theory and computational model where motivation and effort allocation arise from reference-based evaluation processes. Our proposal argues that motivation (and the ensuing willingness to exert effort) stems from subjective value, which in turns depends on one's standards about performance and on the confidence about these standards. In a series of simulations, we show that the model explains puzzling motivational dynamics and associated feelings. Crucially, the model identifies realistic standards (i.e., those matching one's own actual ability) as those more beneficial for motivation and performance. On this basis, the model establishes a normative solution to the problem of optimal allocation of effort, analogous to the optimal allocation of neural and computational resources as in efficient coding.
Keywords: Effort; Motivation; Reference-based model; Subjective value.
© 2022. The Author(s).