Response strength in extreme multiple schedules

Resistance to change, of behavior and of theory

The persistence of operant behavior when disrupted tends to be positively related to how often reinforcers were delivered in the past. Behavioral momentum theory describes this finding as the outcome of Pavlovian processes. That is, the relation between discriminative stimuli and reinforcers that were delivered in their presence strengthens behavior, thereby making it more likely to persist. If only the story were that simple. A growing number of findings challenge the basic tenets of behavioral momentum theory. Some even call into question whether Pavlovian relations contribute to persistence in the first place. In this paper, I will review behavioral momentum theory and some of the data that have been problematic for the theory. I will argue that despite these very real challenges, the theory provides important utility not only to basic analyses of response persistence but also to clinical interventions directed at long-term reductions in problem behavior. It, for example, has set the stage for the development of alternative conceptual analyses of resistance to change, two of which will be highlighted for readers. Moreover, behavioral momentum theory may tell us something important about the reasons it continues to have an influence on the field, despite the challenging data that deter it.

An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation

A reliance on null hypothesis significance testing (NHST) and misinterpretations of its results are thought to contribute to the replication crisis while impeding the development of a cumulative science. One solution is a data-analytic approach called Information-Theoretic (I-T) Model Selection, which builds upon Maximum Likelihood estimates. In the I-T approach, the scientist examines a set of candidate models and determines for each one the probability that it is the closer to the truth than all others in the set. Although the theoretical development is subtle, the implementation of I-T analysis is straightforward. Models are sorted according to the probability that they are the best in light of the data collected. It encourages the examination of multiple models, something investigators desire and that NHST discourages. This article is structured to address two objectives. The first is to illustrate the application of I-T data analysis to data from a virtual experiment. A noisy delay-discounting data set is generated and seven quantitative models are examined. In the illustration, it is demonstrated that it is not necessary to know the "truth" is to identify the one that is closest to it and that the most likely models conform to the model that generated the data. Second, we examine claims made by advocates of the I-T approach using Monte Carlo simulations in which 10,000 different data sets are generated and analyzed. The simulations showed that 1) the probabilities associated with each model returned by the single virtual experiment approximated those that resulted from the simulations, 2) models that were deemed close to the truth produced the most precise parameter estimates, and 3) adding a single replicate sharpens the ability to identify the most probable model.

Quantitative models of persistence and relapse from the perspective of behavioral momentum theory: Fits and misfits

We review quantitative accounts of behavioral momentum theory (BMT), its application to clinical treatment, and its extension to post-intervention relapse of target behavior. We suggest that its extension can account for relapse using reinstatement and renewal models, but that its application to resurgence is flawed both conceptually and in its failure to account for recent data. We propose that the enhanced persistence of target behavior engendered by alternative reinforcers is limited to their concurrent availability within a distinctive stimulus context. However, a failure to find effects of stimulus-correlated reinforcer rates in a Pavlovian-to-Instrumental Transfer (PIT) paradigm challenges even a straightforward Pavlovian account of alternative reinforcer effects. BMT has been valuable in understanding basic research findings and in guiding clinical applications and accounting for their data, but alternatives are needed that can account more effectively for resurgence while encompassing basic data on resistance to change as well as other forms of relapse.

Modeling the effects of sensory reinforcers on behavioral persistence with alternative reinforcement

Problem behavior often has sensory consequences that cannot be separated from the target response, even if external, social reinforcers are removed during treatment. Because sensory reinforcers that accompany socially mediated problem behavior may contribute to persistence and relapse, research must develop analog sensory reinforcers that can be experimentally manipulated. In this research, we devised analogs to sensory reinforcers in order to control for their presence and determine how sensory reinforcers may impact treatment efficacy. Experiments 1 and 2 compared the efficacy of differential reinforcement of alternative behavior (DRA) versus noncontingent reinforcement (NCR) with and without analog sensory reinforcers in a multiple schedule. Experiment 1 measured the persistence of key pecking in pigeons, whereas Experiment 2 measured the persistence of touchscreen responses in children with intellectual and developmental disabilities. Across both experiments, the presence of analog sensory reinforcers increased the levels, persistence, and variability of responding relative to when analog sensory reinforcers were absent. Also in both experiments, target responding was less persistent under conditions of DRA compared to NCR regardless of the presence or absence of analog sensory reinforcers.