Modeling VI and VDRL feedback functions: Searching normative rules through computational simulation.

We present the mathematical description of feedback functions of variable interval and variable differential reinforcement of low rates as functions of schedule size only. These results were obtained using an R script named Beak, which was built to simulate rates of behavior interacting with simple schedules of reinforcement. Using Beak, we have simulated data that allow an assessment of different reinforcement feedback functions. This was made with unparalleled precision, as simulations provide huge samples of data and, more importantly, simulated behavior is not changed by the reinforcement it produces. Therefore, we can vary response rates systematically. We've compared different reinforcement feedback functions for random interval schedules, using the following criteria: meaning, precision, parsimony, and generality. Our results indicate that the best feedback function for the random interval schedule was published by Baum (1981). We also propose that the model used by Killeen (1975) is a viable feedback function for the random differential reinforcement of low rates schedule. We argue that Beak paves the way for greater understanding of schedules of reinforcement, addressing still open questions about quantitative features of simple schedules. Also, Beak could guide future experiments that use schedules as theoretical and methodological tools.

Rachlin's extended self: Influences on a Brazilian research group.

We pay tribute to Rachlin's work stating that researching and writing for posterity is an act of self-control and altruism. We show how Rachlin's work influenced a series of seminars at the University of São Paulo (Brazil) based on his book from 1989, Judgment, Decision, and Choice. This influence is illustrated through two empirical exercises conducted during our seminars, where students were actively involved in data collection and analysis. The first exercise is about judgment of randomness involving coin tossing. The second is a replication of a procedure by Jones and Rachlin (2006) about social discounting of monetary quantities. We use these empirical examples to highlight some of Rachlin's major contributions to the science of behavior and their implications to our seminars and to ourselves as researchers.

On the "Strength" of Behavior.

The place of the concept of response strength in a natural science of behavior has been the subject of much debate. This article reconsiders the concept of response strength for reasons linked to the foundations of a natural science of behavior. The notion of response strength is implicit in many radical behaviorists' work. Palmer (2009) makes it explicit by applying the response strength concept to three levels: (1) overt behavior, (2) covert behavior, and (3) latent or potential behavior. We argue that the concept of response strength is superfluous in general, and an explication of the notion of giving causal status to nonobservable events like latent behavior or response strength is harmful to a scientific endeavor. Interpreting EEG recordings as indicators of changes in response strength runs the risk of reducing behavior to underlying mechanisms, regardless of whether such suggestions are accompanied by behavioral observations. Many radical behaviorists understand behavior as a discrete unit, inviting conceptual mistakes reflected in the notion of response strength. A molar view is suggested as an alternative that accounts for the temporally extended nature of behavior and avoids the perils of a response-strength based approach.