Association for Behavior Analysis International

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50th Annual Convention; Philadelphia, PA; 2024

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Symposium #442
Resurgence and Operant Variability Between Species
Monday, May 27, 2024
10:00 AM–11:50 AM
Convention Center, 200 Level, 204 C
Area: EAB/DDA; Domain: Basic Research
Chair: Kenneth D. Madrigal (Universidad de Sonora)
Discussant: Jennifer J. McComas (University of Minnesota)
Abstract:

Changes in contingencies of reinforcement results on recurring and variable responding. Treatment relapse has been assessed using a resurgence procedure in basic and applied settings. The first two studies will discuss strategies for mitigating treatment relapse in humans and rats. In the first study, Schieltz et al. evaluated the use of response latency as an incremental measure of long-term maintenance. In their study, initial demonstrations of long-term maintenance are prescribed by longer latencies to challenging behavior in comparison to shorter latencies to independent manding during treatment. Madrigal et al. assessed resurgence under conditions of extinction and decreasing frequencies of reinforcement, reporting an increase in resurgence as contingencies of reinforcement degraded. In a third study, Benavides et al., compared the levels of operant variability generated by a Lag n criterion with variability generated by VI and VR schedules with rats. Lastly, Gutierrez et al., manipulated the value of the Lag using a within session adjustable procedure with rats. Results on both the latter studies suggest that previous procedures in the study of operant variability have limited the maximum level of variability observed so far. These studies contribute valuable insights into understanding and addressing treatment relapse and operant variability.

Instruction Level: Intermediate
Keyword(s): Humans, Operant variability, Rats, Resurgence
 
Use of Response Latency as a Measure of Long-Term Maintenance of Treatment Effects
KELLY M. SCHIELTZ (University of Iowa), Chloe M. Lewis (University of Iowa), Christopher Roy Child (The University of Iowa), Matthew O'Brien (The University of Iowa), Joel Eric Ringdahl (University of Georgia)
Abstract: Long-term maintenance of treatment effects is determined when challenging behavior no longer recurs and alternative or adaptive behavior persists under treatment disruptions (Nevin & Wacker, 2013). To meet both of these requirements, long-term maintenance may not occur for many months (up to 18 months), as shown by Wacker et al. (2011) with young children with developmental disabilities who engaged in socially maintained challenging behavior. Clinically, the delays in accomplishing these requirements could be discouraging, whereas demonstrating incremental progress toward these goals may promote both practitioners and caregivers motivation in continuing to implement treatment programs. Thus, the purpose of this study was to evaluate the use of response latency as an incremental measure of long-term maintenance. Across baseline (functional analysis), treatment (functional communication training), and treatment disruption (extinction) phases, latency to first response for both challenging behavior and independent manding was retrospectively evaluated across participants enrolled in a research project focused on identifying mitigating variables for treatment relapse. We defined longer latencies to challenging behavior in comparison to shorter latencies to independent manding during treatment disruption as initial demonstrations of long-term maintenance. Results across participants and clinical implications of our findings will be discussed.
 

Frequencies of Reinforcement Transitions and Resurgence of Operant Responding in Rats

KENNETH D. MADRIGAL (Universidad de Sonora), Cinthia Hernandez (Universidad de Guadalajara (CEIC)), Julissa Gracia (Universidad de Sonora), L. Rebeca Mateos Morfin (Universidad de Guadalajara), Carlos J. Flores Aguirre (Universidad de Guadalajara)
Abstract:

Resurgence is defined as the recurrence of a previously reduce response when contingencies of reinforcement of an alternative response are worsened. Recently, Yensen et al. (2022) reported resurgence by worsening the contingencies of reinforcement for alternative responding. This worsening of conditions was arranged by transitioning from a VI to a FI schedule, and by decreasing the frequency of reinforcement. This current study builds upon Yensen et al.'s work, aiming to replicate their findings and account for unconsidered variables. Rats were exposed to a within-session resurgence procedure. In the first experiment, target and alternative responses were trained under VI30s and VI50s schedules, respectively, while in the second experiment, equal frequencies of reinforcement were arranged for both responses. Consistently and repeatedly, resurgence was observed in both experiments when alternative responding was placed under extinction conditions. However, as reported by Yensen et al., resurgence was only observed under the FI100s schedule after the target and alternative responses were trained with unequal reinforcement frequencies. Results are discussed by identifying some of the possible conditions under which resurgence can be observed.

 

Comparing Induced and Operant Variability Using Lag and Intermittent Reinforcement Procedures

RODRIGO BENAVIDES (National Autonomous University of Mexico), Brissa Gutiérrez (Universidad Nacional Autonóma de México), Rogelio Escobar (Universidad Nacional Autónoma de México)
Abstract:

There are two main sources of behavioral variability. One is its direct reinforcement, for example, through a Lag n procedure. In these procedures, a sequence of responses is reinforced only if it is different from n number of previous trials. The other is by induction through intermittent reinforcement. Given that both procedures are relatively different, there is no direct comparison between the variability generated by them. The purpose of these experiments was to compare the levels of variability generated by a Lag n criterion with variability generated by variable interval and variable ratio schedules with rats. A two-component multiple schedule was used, with one component in which a sequence of 4 responses distributed in 4 or 2 levers was reinforced with a Lag 5 criterion. The delivery of reinforcers in the second component was coupled to the performance in the first component, but with a criterion based on the intermittent reinforcement schedules. Higher levels of variability were found during the VI than during Lag, while higher variability was found during Lag than during VR. The results are partially congruent with both induced and operant variability literature. Possible limitations and future avenues of the procedure are discussed.

 
Adjustable Lag n Procedure to Reinforce Variability in Rats
BRISSA GUTIÉRREZ (Universidad Nacional Autonóma de México), Rodrigo Benavides (National Autonomous University of Mexico), Rogelio Escobar (Universidad Nacional Autónoma de México)
Abstract: Lag n is a procedure that consists of reinforcing a sequence of responses only if it is different from n number of previous trials. The highest Lag value reported in the literature is Lag 50, with pigeon subjects. At that value, a decrease in variability is observed. However, it is possible that the total number of possible sequences implemented in the procedures limited the maximum level of Lag criteria and variability. The purpose of the present experiment was to manipulate the value of the Lag using an within session adjustable procedure with rats. Each session consisted of 5 blocks of 10 trials each. Each trial consisted of the occurrence of 8 responses distributed in 4 levers generating 65, 526 different possible sequences. Lag could increase, decrease, or be maintained depending on the percentage of reinforced trials during each block. The results show that the level of variability increases as the Lag value increases, while the percentage of reinforced trials increases to an asymptotic point. Values greater than Lag 2000 were reached for almost all subjects. The results suggest that previous procedures in the study of operant variability have limited the maximum level of variability observed so far.
 

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