  In this paper, we present a R script named Beak, built to simulate rates of
behavior interacting with schedules of reinforcement. Using Beak, we've
simulated data that allows an assessment of different reinforcement feedback
functions (RFF). This was made with unparalleled precision, since simulations
provide huge samples of data and, more importantly, simulated behavior isn't
changed by the reinforcement it produces. Therefore, we can vary it
systematically. We've compared different RFF for RI schedules, using as
criteria: meaning, precision, parsimony and generality. Our results indicate
that the best feedback function for the RI schedule was published by Baum
(1981). We also propose that the model used by Killeen (1975) is a viable
feedback function for the RDRL schedule. We argue that Beak paves the way for
greater understanding of schedules of reinforcement, addressing still open
questions about quantitative features of schedules. Also, they could guide
future experiments that use schedules as theoretical and methodological tools.
