Publications by authors named "Andrew C Bonner"

The threats of climate change to human well-being are well-documented and are growing in number and intensity. Despite the international community investing heavily in technological innovation and policy initiatives to solve the problem, emissions continue to rise. Experts are recognizing that eliminating emissions cannot be achieved without modifying the human behavior of which emissions are a function.

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Research has identified treatment-responsive and treatment-resistant subtypes of automatically maintained self-injurious behavior (ASIB) based on patterns of responding in the functional analysis (FA) reflecting its sensitivity to disruption by alternative reinforcement, and the presence of self-restraint. Rooker et al. (2019) unexpectedly observed reductions in treatment-resistant self-injury while participants performed an operant task.

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Some individuals diagnosed with intellectual and developmental disabilities engage in automatically reinforced self-injurious behavior (SIB). For these individuals, identifying effective treatments may be difficult due to the nature of the reinforcement contingency. The purpose of this study was to review the literature on the treatment of automatically reinforced SIB to determine commonalities in procedures that produced effective and ineffective treatment outcomes, as well as historical trends in the treatment of this class of SIB.

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Differential-reinforcement-of-low-rate (DRL) schedules are reinforcement contingencies designed to reduce response rates. A common variation of the DRL arrangement is known as full-session DRL ( f-DRL), in which a reinforcer is presented at the end of an interval if the response rate during that interval is below a predetermined criterion. Prior human operant research involving arbitrary mouse clicks has shown that the f-DRL is likely to reduce target responding to near zero rates.

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Hagopian, Rooker, and Zarcone (2015) evaluated a model for subtyping automatically reinforced self-injurious behavior (SIB) based on its sensitivity to changes in functional analysis conditions and the presence of self-restraint. The current study tested the generality of the model by applying it to all datasets of automatically reinforced SIB published from 1982 to 2015. We identified 49 datasets that included sufficient data to permit subtyping.

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