An Investigation of Methods Toward Mitigating Resurgence and Renewal in A Human Operant Model

Abstract

Interventions related to differential reinforcement (DR) are among the most frequently used within the field of applied behavior analysis (ABA). However, certain conditions can inhibit the level at which effects of these interventions maintain upon treatment challenges or generalize across contexts. This can result in the relapse, or recurrence of the destructive behavior the interventions aim to decrease. The purpose of this basic study is to identify effective methods for attenuating both the resurgence and renewal of a previously taught response put on extinction and for promoting the maintenance and generalization of alternative responses with university students using a computer program. Using three experiments, effects of delivering lag reinforcement for multiple alternative responses, reinforcement in multiple contexts, and both in combination, were evaluated on the relapse of a previously extinguished operant response and on the maintenance and generalization of alternative responses. Across five different data analyses (three statistical and two visual), lag reinforcement in multiple contexts during DRA was the most effective in attenuating resurgence of the extinguished response and promoting maintenance of alternative responding. There were no significant differences in levels of renewal and generalization across the three experiments. Future directions toward applied research related to resurgence and renewal are discussed.