Modeling VI and VDRL feedback functions: searching normative rules through computational simulation
This work addresses open questions in behavioral psychology about quantitative features of reinforcement schedules, potentially guiding future experiments, but it is incremental as it builds on existing models.
The researchers tackled the problem of identifying the best reinforcement feedback functions for RI and RDRL schedules by developing a computational simulation tool called Beak, which allowed precise comparisons based on criteria like meaning and precision, and found that Baum's (1981) function was best for RI schedules while Killeen's (1975) model was viable for RDRL schedules.
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.