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偏好、对变革的抵触以及累积决策模型。

Preference, resistance to change, and the cumulative decision model.

作者信息

Grace Randolph C

机构信息

University of Canterbury.

出版信息

J Exp Anal Behav. 2018 Jan;109(1):33-47. doi: 10.1002/jeab.305. Epub 2018 Jan 4.

Abstract

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant-duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change.

摘要

根据行为动量理论(内文 & 格雷斯,2000a),并发链中的偏好和多重时间表中的变化抗性是代表强化历史的共同结构的独立测量指标。在此,我回顾了1997年至2002年间内文、格雷斯及其同事进行的关于偏好和变化抗性的原始研究,其中强化变量是通过参数方式进行操纵的,以及最近的研究。格雷斯及其同事为并发链提出的累积决策模型被证明能够很好地解释偏好和变化抗性,并且能够预测在固定时长成分下观察到的对强化率和强度的敏感性增加。累积决策模型对偏好和变化抗性数据拟合的残差呈正相关,支持了行为动量理论的预测。尽管仍有一些问题存在,但累积决策模型所假设的学习过程,即结果与代表当前情境中平均结果值的标准进行比较,可能为获得变化抗性差异提供一个合理的模型。

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