在MINERVA2中识别真假:整合模糊跟踪理论和计算内存建模
Minyu Chang1, Brendan T Johns2, Charles J Brainerd3
1Department of Psychology, Trinity University.
Psychological review
|February 27, 2025
概括
在MINERVA2模型成功模拟Deese/Roediger/McDermott (DRM) 虚假识别效应,通过结合模糊-跟踪理论假设,增强虚假记忆的计算模型.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
- 记忆研究 记忆研究
背景情况:
- 在此之前,MINERVA2模型已经在模拟基本的Deese/Roediger/McDermott (DRM) 虚假识别方面展现出潜力.
- 扩展计算模型来解释细微的记忆现象对于理解认知过程至关重要.
研究的目的:
- 通过整合模糊痕迹理论的关键假设来增强MINERVA2模型.
- 证明模型在模拟更广泛的DRM幻觉效应和相关记忆判断方面的能力.
主要方法:
- 结合了使用不同的矢量表示 (分布式语义为要点,全息字形为字形) 的文字-核心区别.
- 通过将项目存储质量与前一项的语义相似性联系起来,整合了基本层次的概念.
- 对DRM幻觉研究的经验数据进行了增强MINERVA2模型的测试,包括记住/知道和源判断.
主要成果:
- 改进的MINERVA2模型准确地模拟了DRM幻觉范式中的各种经验效应.
- 该模型成功考虑了记住/知道和源监控判断,证明了它的解释能力.
- 纳入核心层次结构解决了多层次记忆模型中的存储独立性问题.
结论:
- MINERVA2,加上模糊跟踪理论原则,提供了一个强大的计算框架来建模DRM幻觉.
- 这项研究加强了计算建模方法与虚假记忆的心理学理论之间的联系.
- 这些发现支持MINERVA2作为一种可扩展的工具,用于调查情节性记忆和虚假识别现象.
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