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

J Exper Analysis Behavior

Browning

Nall

2019

Self Cite

Resurgence as Choice (RaC) is a quantitative theory suggesting that an increase in an extinguished target behavior with subsequent extinction of an alternative behavior (i.e., resurgence) is governed by the same processes as choice more generally. We present data from an experiment with rats examining a range of treatment durations with alternative reinforcement plus extinction and demonstrate that increases in treatment duration produce small but reliable decreases in resurgence. Although RaC predicted the relation between target responding and treatment duration, the model failed in other respects. First, contrary to predictions, the present experiment also replicated previous findings that exposure to cycling on/off alternative reinforcement reduces resurgence. Second, RaC did a poor job simultaneously accounting for target and alternative behaviors across conditions. We present a revised model incorporating a role for more local signaling effects of reinforcer deliveries or their absence on response allocation. Such signaling effects are suggested to impact response allocation above and beyond the values of the target and alternative behaviors as longer-term repositories of experience. The new model provides an excellent account of the data and can be viewed as an integration of RaC and a quantitative approximation of some aspects of Context Theory.

Section: Ghosts Of Models Past and Futurementioning

confidence: 83%

Resurgence as Choice in Context: Treatment duration and on/off alternative reinforcement

J Exper Analysis Behavior

Browning

Nall

2019

Self Cite

“…For the Rich and Lean components, respectively, the r and Δr parameters were set to 120 and 30 (the programmed number of reinforcers delivered per hour in these components during baseline) for all fits. To limit extreme parameter variation under such conditions, b was fixed at 0.5, as is typical in the literature (Nevin, 2002;Nevin et al, 2017;Nevin & Shahan, 2011), for all model fits. Specifically, previous exposure to extinction contingencies may facilitate discrimination of contingency termination, which would result in faster suppression of behavior and increased values of c across tests.…”

Section: Resultsmentioning

confidence: 99%

“…If researchers were to study extinction or extinction-related phenomena (e.g., relapse following extinction; see Bouton, Winterbauer, & Todd, 2012;Craig et al, 2014;Nevin et al, 2017;Sweeney & Shahan, 2013), it is possible that repeated extinction testing would contaminate assessment of relevant outcomes across successive tests. Pigeons are long-lived animals that commonly are used in experimental analyses of behavior, and they often are used in several experiments during their lifetime.…”

Section: Discussionmentioning

confidence: 99%

Behavioral momentum and resistance to extinction across repeated extinction tests

Craig

Sweeney

J Exper Analysis Behavior

2019

Self Cite

The present experiments assessed whether resistance to extinction of pigeons' key pecking decreased across repeated extinction tests. An additional impetus for this research was to determine how the quantitative framework provided by behavioral momentum theory might be used to describe any such changes across tests. Pigeons pecked keys in two-component multiple schedules (one component associated with a higher reinforcer rate and the other with a lower rate) in which baseline and extinction conditions alternated. In Experiment 1, baseline and extinction conditions alternated every session, and, in Experiment 2, these conditions lasted for 10 and 7 sessions, respectively. Resistance to extinction decreased across successive extinction conditions in both experiments. Fits of the behavioralmomentum based model of extinction to the data returned uncertain results in Experiment 1 but implicated both generalization decrement and response-reinforcer contingency termination as the possible mechanisms responsible for behavior change in Experiment 2. Thus, these data suggest that experimental manipulations that affect discrimination of changes in reinforcement contingencies may influence resistance to extinction by modulating the disruptive impacts of removing reinforcers from the experimental context and of suspending response-reinforcer contingencies.

“…Given existing reviews and tutorials describing the quantitative details of BMT and its extension to resurgence for applied behavior analysts (e.g., Greer, Fisher, Romani et al, 2016; Nevin & Shahan, ), we will not reiterate those details here. Rather, we will simply summarize the theoretical account and the difficulties it has encountered (detailed accounts of the quantitative difficulties can be found in Craig & Shahan, ; Nevin et al, ; Shahan & Craig, ).…”

Section: Behavioral Momentum Theory Of Resurgencementioning

confidence: 99%

“…Despite these and other promising findings resulting from applying BMT to the development of more durable treatments for problem behavior, some of the basic research inspired by the theory has challenged its core underlying assumptions and predictions of the equations (e.g., Craig & Shahan, ; Nevin et al, ; Sweeney & Shahan, ). First, the BMT equation suggests that differential reinforcement (and alternative reinforcement more broadly) in Phase 2 must always disrupt target responding.…”

Section: Behavioral Momentum Theory Of Resurgencementioning

confidence: 99%

Resurgence as Choice: Implications for promoting durable behavior change

Greer

J of App Behav Analysis

2019

Self Cite

Resurgence is an increase in a previously suppressed behavior resulting from a worsening in reinforcement conditions for current behavior. Resurgence is often observed following successful treatment of problem behavior with differential reinforcement when reinforcement for an alternative behavior is subsequently omitted or reduced. The efficacy of differential reinforcement has long been conceptualized in terms of quantitative models of choice between concurrent operants (i.e., the matching law). Here, we provide an overview of a novel quantitative model of resurgence called Resurgence as Choice (RaC), which suggests that resurgence results from these same basic choice processes. We review the failures of the only other quantitative model of resurgence (i.e., Behavioral Momentum Theory) and discuss its shortcomings with respect to the limited range of circumstances about which it makes predictions in applied settings. Finally, we describe how RaC overcomes these shortcomings and discuss implications of the model for promoting durable behavior change.