对人类记忆进行大规模研究,以获得有意义的叙事
Antonios Georgiou1,2, Tankut Can3, Mikhail Katkov1,2
1School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey 08540, USA.
Learning & memory (Cold Spring Harbor, N.Y.)
|February 21, 2025
概括
大型语言模型 (LLM) 允许使用自然主义叙述进行大规模的人类记忆研究. LLM有助于设计刺激并分析回忆/识别数据,揭示具有叙事长度的记忆尺度.
科学领域:
- 认知心理学 认知心理学
- 计算语言学 计算语言学
- 神经科学是一个神经科学.
背景情况:
- 对人类记忆的大规模研究至关重要,但对于诸如叙事之类的自然主义刺激来说具有挑战性.
- 以前的方法需要大量的手工劳动来设计刺激和数据分析.
- 大型语言模型 (LLM) 为自动化这些过程提供了一个潜在的解决方案.
研究的目的:
- 开发和验证一个LLM驱动的管道,用于设计和分析大规模叙事记忆实验.
- 调查叙事长度如何影响回忆和识别记忆.
- 探索叙事理解和记忆中的上下文重建的作用.
主要方法:
- 开发了一个LLM管道,用于生成叙事刺激和分析记忆数据.
- 与众多参与者进行了在线回忆和识别记忆实验.
- 利用编码叙事来评估理解对记忆的影响.
- 使用LLM文本嵌入来将语义相似性与回忆概率相关联.
主要成果:
- 回忆和识别表现与叙事长度线性缩放.
- 较长的叙述导致总结而不是精确的细节回忆.
- 在加密叙述中,回忆显著下降,而识别保持稳定.
- 编码叙事的记忆回忆遵循了原来的故事顺序,表明了上下文重建.
- 基于LLM的语义相似性强烈预测了回忆概率.
结论:
- 士课程显著提升了人类记忆研究的规模和范围.
- 叙事理解在回忆中起着至关重要的作用,但不是识别.
- LLM为记忆研究提供了新的工具,并为人工智能提供了心理知情的基准.
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