人们如何从感官证据中构建视觉记忆表征? 重新审视两个经典的选择模式
Maria M Robinson1, Isabella C DeStefano1, Edward Vul1
1University of California, San Diego, United States of America.
Journal of mathematical psychology
|July 3, 2024
概括
信号检测理论为决策提供了比软max模型更稳定和更可概括的预测,特别是在记忆任务中. 这表明信号检测参数可能反映真实的认知过程.
科学领域:
- 认知心理学 认知心理学
- 决策科学 决策科学 决策科学
- 神经科学是一个神经科学.
背景情况:
- 决策模型常常将信念和偏好简化为一个单一的潜在强度.
- 两个主要方法将这些优势与选择联系起来:信号检测理论 (SDT) 和软max函数.
- 当前的研究很少将这两种突出的选择模式联系起来,通常偏爱基于学科传统的选择模式.
研究的目的:
- 为了比较SDT和软max模型在表征潜在决策过程中的有效性.
- 调查哪个模型在不同数量的替代方案中提供了更好的预测,特别是在记忆任务中.
主要方法:
- 进行了视觉记忆实验,涉及多种替代方案的决策任务.
- 应用SDT和softmax模型来分析相同实验设计中的选择行为.
- 在不同数量的替代方案中检查模型参数的稳定性.
主要成果:
- 在同一实验中,软max参数 (lambda) 在不同数量的替代品中显著变化.
- 信号检测理论参数 (例如,灵敏度,d') 在不同数量的替代方案中保持稳定.
- 这种稳定性表明SDT参数可能对任务结构变化不变.
结论:
- 与软max函数相比,信号检测理论为决策提供了更强大和更可概括的预测.
- SDT参数的不变性表明它们可能代表了人类决策的基本方面,而不是仅仅是数学方便.
- 用SDT链接模型取代softmax可以提高跨不同任务结构的预测准确性,特别是在记忆研究中.
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