记忆的嵌入式计算框架:在真实和虚假回忆预测中表示的关键作用
Dominic Guitard1, Jean Saint-Aubin2, J Nick Reid3
1School of Psychology, Cardiff University, Tower Building, 70 Park Place, Cardiff, CF10 3AT, UK. guitardd@cardiff.ac.uk.
Psychonomic bulletin & review
|April 11, 2025
概括
人类的记忆是不完美的,导致错误的回忆. 嵌入式计算内存框架 (eCFM) 现在通过整合语义和情节性内存来建模这一点,改善了对串行召回精度的预测.
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.
- 心理语言学 心理语言学
背景情况:
- 人类的记忆是重建性的,容易出现错误,尤其是对未经研究的信息的错误回忆.
- 目前存在的串行召回计算模型在准确预测特定项目的虚假召回方面面临挑战.
- 了解虚假回忆的机制对于开发全面的记忆模型至关重要.
研究的目的:
- 解决当前模型在预测串行召回任务中的错误召回方面的局限性.
- 引入和验证嵌入式计算内存框架 (eCFM) 用于模拟人类记忆.
- 将语义和情节记忆帐户集成到一个计算框架中,使用丰富的词汇.
主要方法:
- 开发了嵌入式计算内存框架 (eCFM),集成语义和情节性内存组件.
- 整合了一个全面的词典,捕捉了字形,语音和语义词的关系.
- 模拟了eCFM与六项涉及年轻成年人的实验数据,词和非词列表.
主要成果:
- 该eCFM成功地解释了真实串行召回的模式,包括准确性,内部错误和遗漏.
- 该模型准确地捕获了虚假回忆现象,例如外向错误和关键诱惑效应.
- 模型模拟与实验数据对齐,在语义,语音和拼写相关性各异的列表中.
结论:
- 该eCFM提供了一个强大的连续召回计算帐户,成功模拟了准确的召回和错误的召回.
- 整合语义知识 (词典) 与情节性记忆过程对于精确的记忆建模至关重要.
- 这个框架弥合了计算差距,通过统一语义和情节记忆来推进人类记忆的整体模型.
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