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Exploring the impact of coherence (through the presence versus absence of feedback) and levels of derivation on persistent rule-following.
Learning & Behavior ( IF 1.8 ) Pub Date : 2020-07-15 , DOI: 10.3758/s13420-020-00438-1
Colin Harte 1 , Dermot Barnes-Holmes 1, 2 , Yvonne Barnes-Holmes 1 , Ciara McEnteggart 1
Affiliation  

Recent developments in relational frame theory (RFT) have outlined a number of key variables of potential importance when analyzing the dynamics involved in derived relational responding. Recent research has begun to explore the impact of a number of these variables on persistent rule-following, namely, levels of derivation and coherence. However, no research to date has systematically examined the impact of coherence on persistent rule-following at varying levels of derivation. Across two experiments, the impact of coherence (manipulated through the systematic use of performance feedback) was explored on persistent rule-following when derivation was relatively low (Exp. 1) and high (Exp. 2). A training protocol based on the implicit relational assessment procedure (IRAP) was used to establish novel combinatorially entailed relations that manipulated the feedback provided on the untrained, derived relations (A-C) for five blocks of trials in Experiment 1 and one block of trials in Experiment 2. One of these relations was then inserted into the rule for responding on a subsequent contingency-switching match-to-sample task to assess rule persistence. While no significant differences were found in Experiment 1, the provision or non-provision of feedback had a significant differential impact on rule persistence in Experiment 2. These differences, and the subtle complexities that appear to be involved in persistent rule-following in the face of reversed reinforcement contingencies, are discussed.



中文翻译:

探索连贯性(通过反馈的存在与否)和推导水平对持续遵循规则的影响。

关系框架理论 (RFT) 的最新发展概述了在分析派生关系响应中涉及的动态时具有潜在重要性的许多关键变量。最近的研究已经开始探索许多这些变量对持续遵循规则的影响,即推导和连贯性的水平。然而,迄今为止,还没有研究系统地研究一致性对不同推导水平上持续遵守规则的影响。在两个实验中,当推导相对较低(实验 1)和较高(实验 2)时,一致性(通过系统使用性能反馈来操纵)对持续遵循规则的影响进行了探索。使用基于隐式关系评估程序 (IRAP) 的训练协议来建立新的组合蕴涵关系,该关系操纵在实验 1 中的五个试验块和实验中的一个试验块的未经训练的衍生关系 (AC) 上提供的反馈2. 然后将这些关系之一插入到规则中,以响应后续的随机切换匹配到样本任务以评估规则持久性。虽然在实验 1 中没有发现显着差异,但提供或不提供反馈对实验 2 中的规则持久性有显着的不同影响。的反向强化意外事件,进行了讨论。实验 1 中的五个试验块和实验 2 中的一个试验块的派生关系 (AC)。然后将这些关系之一插入规则中,用于响应后续的随机切换匹配到样本任务以评估规则持久性. 虽然在实验 1 中没有发现显着差异,但提供或不提供反馈对实验 2 中的规则持久性有显着的不同影响。的反向强化意外事件,进行了讨论。实验 1 中的五个试验块和实验 2 中的一个试验块的派生关系 (AC)。然后将这些关系之一插入规则中,用于响应后续的随机切换匹配到样本任务以评估规则持久性. 虽然在实验 1 中没有发现显着差异,但提供或不提供反馈对实验 2 中的规则持久性有显着的不同影响。的反向强化意外事件,进行了讨论。然后将这些关系之一插入到规则中,用于响应后续的突发事件切换匹配样本任务,以评估规则持久性。虽然在实验 1 中没有发现显着差异,但提供或不提供反馈对实验 2 中的规则持久性有显着的不同影响。的反向强化意外事件,进行了讨论。然后将这些关系之一插入到规则中,用于响应后续的突发事件切换匹配样本任务,以评估规则持久性。虽然在实验 1 中没有发现显着差异,但提供或不提供反馈对实验 2 中的规则持久性有显着的不同影响。的反向强化意外事件,进行了讨论。

更新日期:2020-07-16
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