Quantifying motivation with effort-based decision-making paradigms in health and disease

Motivation can be characterized as a series of cost-benefit valuations, in which we weigh the amount of effort we are willing to expend (the cost of an action) in return for particular rewards (its benefits). Human motivation has traditionally been measured with self-report and questionnaire-based tools, but an inherent limitation of these methods is that they are unable to provide a mechanistic explanation of the processes underlying motivated behavior. A major goal of current research is to quantify motivation objectively with effort-based decision-making paradigms, by drawing on a rich literature from nonhuman animals. Here, we review this approach by considering the development of these paradigms in the laboratory setting over the last three decades, and their more recent translation to understanding choice behavior in humans. A strength of this effort-based approach to motivation is that it is capable of capturing the wide range of individual differences, and offers the potential to dissect motivation into its component elements, thus providing the basis for more accurate taxonomic classifications. Clinically, modeling approaches might provide greater sensitivity and specificity to diagnosing disorders of motivation, for example, in being able to detect subclinical disorders of motivation, or distinguish a disorder of motivation from related but separate syndromes, such as depression. Despite the great potential in applying effort-based paradigms to index human motivation, we discuss several caveats to interpreting current and future studies, and the challenges in translating these approaches to the clinical setting.