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利用亚马逊土耳其机器人平台评估人类操作性行为的消退、恢复和复发。

Evaluating Extinction, Renewal, and Resurgence of Operant Behavior in Humans with Amazon Mechanical Turk.

作者信息

Ritchey Carolyn M, Kuroda Toshikazu, Rung Jillian M, Podlesnik Christopher A

机构信息

Department of Psychological Sciences, Auburn University.

Huckle Co., Ltd., Aichi, Japan.

出版信息

Learn Motiv. 2021 May;74. doi: 10.1016/j.lmot.2021.101728. Epub 2021 May 13.

Abstract

Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace providing researchers with the opportunity to collect behavioral data from remote participants at a low cost. Recent research demonstrated reliable extinction effects, as well as renewal and resurgence of button pressing with MTurk participants. To further examine the generality of these findings, we replicated and extended these methods across six experiments arranging reinforcement and extinction of a target button press. In contrast to previous findings, we did not observe as reliable of decreases in button pressing during extinction (1) after training with VR or VI schedules of reinforcement, (2) in the presence or absence of context changes, or (3) with an added response cost for button pressing. However, we found that that a 1-point response cost for all button presses facilitated extinction to a greater extent than the absence of response cost. Nevertheless, we observed ABA renewal of button pressing when changing background contexts across phases and resurgence when extinguishing presses on an alternative button. Our findings suggest that MTurk could be a viable platform from which to ask and address questions about extinction and relapse processes, but further procedural refinements will be necessary to improve the replicability of control by experimental contingencies.

摘要

亚马逊土耳其机器人(MTurk)是一个众包市场,为研究人员提供了以低成本从远程参与者那里收集行为数据的机会。最近的研究表明,MTurk参与者在按键行为上存在可靠的消退效应,以及恢复和复发现象。为了进一步检验这些发现的普遍性,我们在六个实验中重复并扩展了这些方法,安排了目标按键行为的强化和消退。与之前的发现不同,我们没有观察到在以下情况下消退期间按键行为可靠地减少:(1)在使用可变比率(VR)或可变间隔(VI)强化程序进行训练后;(2)存在或不存在情境变化时;(3)对按键行为增加反应代价时。然而,我们发现,对所有按键行为设置1分的反应代价比不设置反应代价更能促进消退。尽管如此,当在不同阶段改变背景情境时,我们观察到了按键行为的ABA式恢复,并且在对另一个按键行为进行消退时观察到了复发。我们的研究结果表明,MTurk可能是一个可行的平台,可用于提出和解决关于消退和复发过程的问题,但需要进一步完善程序,以提高实验条件控制的可重复性。

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