语义上相关的单词的自由回忆揭示了相似的结构结构
Jeffrey C Zemla1, Nancy Linehan2, Lynn J Lohnas2
1Department of Psychology, Syracuse University, 765 Irving Ave, Syracuse, NY, 13244, USA. jczemla@syr.edu.
Memory & cognition
|February 3, 2026
概括
研究人员开发了一种新方法,直接从自由回忆数据中估计语义相似性,揭示了可预测的记忆回忆模式,并优于现有模型.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
背景情况:
- 语义关联会影响自由回忆中的项目聚类.
- 以前的方法依赖于外部语义相似性估计,独立于参与者数据.
- 像潜伏语义分析 (LSA) 和word2vec这样的分布式语义模型估计语义相似性.
研究的目的:
- 开发和验证一种用于直接从自由回忆数据中估计对对语义相似性的新方法.
- 研究时间和语义关联在回忆顺序之间的相互作用.
- 从自由回忆数据构建语义网络,并与现有模型进行比较.
主要方法:
- 参与者研究了随机呈现或被屏蔽的分类词汇列表.
- 使用既有和新的分析来评估回忆集群.
- 开发了一种新模型,从自由回忆数据中估计对相似性和语义网络.
- 该模型的预测与word2vec进行了比较,并使用PEERS数据集进行了验证.
主要成果:
- 语义关联可预测地影响回忆顺序,与时间关联相互作用.
- 模型估计的语义网络与类别结构和word2vec.com紧密相匹配.
- 这种新型模型比word2vec更准确地预测自由回忆集群,控制时间相似性.
- 对PEERS数据集的验证证实了该模型的稳定性.
结论:
- 从内存回忆数据直接估计语义相似性是可行的.
- 开发的方法提供了一种新的方法来研究情节和语义记忆.
- 这种方法提供了对时间和语义因素在记忆检索中的相互作用的见解.
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