Abstract
Cost-benefit analysis is a key determinant of decision-making, yet little is known about the underlying neural circuit mechanisms—perhaps because investigating this abstract concept using laboratory animals is challenging without quantitative behavioral readouts and theoretical frameworks. Here, we developed a novel behavioral paradigm to measure optimal cost-benefit switching decisions in mice. On each trial of the task, a mouse faces two options to collect reward: one lever provides a small volume of reward that requires a fixed number of presses (fixed ratio, FR); the other lever confers a large volume, but the required number of presses increases after each collection (progressive ratio, PR). The mouse initially prefers the PR with larger reward, but as the session progresses its preference changes to the FR because of increasing cost of effort (e.g. lever presses) on the PR. This preference switch was quantified by the indifference point at which the values of both choices became equivalent. We aimed to quantify a parametric shift in switching decisions by systematically varying effort cost and reward benefit in a two-dimensional parameter space. This parametric manipulation successfully influenced the switching decisions, therefore shifting the indifference points accordingly with the relative value of the larger reward. Our data-driven estimation of the indifference points was further validated by a theoretical framework based on the optimization principle. Taken together, our behavioral paradigm with a theoretical framework provides a quantitative platform to investigate the function and dysfunction of neural circuits underlying cost-benefit assessment.