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惩罚和动态选择:对直接抑制模型的评估
Rafaela M Fontes1, Timothy A Shahan1
1Utah State University, Logan, UT, USA.
Journal of the experimental analysis of behavior
|January 31, 2025
概括
惩罚率显著影响行为,将选择转向不那么受到惩罚的选择. 这些发现挑战了现有的模型,并揭示了惩罚.
科学领域:
- 行为心理学 行为心理学
- 动物行为研究 动物行为研究
- 操作者调节条件
背景情况:
- 了解惩罚如何影响决策对于行为修改至关重要.
- 现有的模型,比如直接抑制模型,可以预测惩罚对响应分配的影响.
研究的目的:
- 在选择程序中调查不同惩罚率对响应分配的影响.
- 测试来自修改的直接抑制模型的预测.
- 分析惩罚如何与强化计划相互作用.
主要方法:
- 采用了选择程序,在会话中快速变化的相对强化率.
- 采用了两个实验,在强化计划上叠加平等和不平等的惩罚率.
- 应用了通用匹配定律来分析响应分配数据.
主要成果:
- 惩罚增加了对更丰富的强化计划的偏好,无论绝对惩罚率如何.
- 响应分配偏离了惩罚率较高的时间表,这表明对频繁惩罚的偏见.
- 直接抑制模型的预测受到实验结果的挑战.
结论:
- 惩罚率,而不仅仅是它的存在,对响应分配进行了关键调节.
- 由于相对的惩罚频率,行为偏见可能会出现,挑战更简单的抑制模型.
- 一般化匹配法为理解惩罚如何影响选择行为中的敏感性和偏见提供了一个框架.
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