This article tackles two challenges of the empirical evaluation of interaction techniques that rely on user memory, such as hotkeys, here coined Recall-based interaction techniques (RBITs): (1) the lack of guidance to design the associated study protocols, and (2) the difficulty of comparing evaluations performed with different protocols. To address these challenges, we propose a model-based evaluation of RBITs. This approach relies on a computational model of human memory to (1) predict the informativeness of a particular protocol through the variance of the estimated parameters (Fisher Information) (2) compare RBITs recall performance based on the inferred parameters rather than behavioral statistics, which has the advantage of being independent of the study protocol. We also release a Python library implementing our approach to aid researchers in producing more robust and meaningful comparisons of RBITs.