整合文字形式表示与全球相似性计算在识别记忆中
1University of Melbourne, Melbourne, Australia. adamosth@gmail.com.
Psychonomic bulletin & review
|November 16, 2023
概括
这项研究将单词感知与记忆模型整合在一起,发现视觉单词形式和含义都会影响识别错误. 拼写和语义因素在长期记忆检索中同样重要.
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:
- 识别记忆检索通常依赖于全球相似性计算.
- 之前的模型忽略了感知词表示,尽管观察到对感知上相似词的错误识别.
研究的目的:
- 将阅读文学中的正写表示集成到识别记忆的全球相似性模型中.
- 调查正写和语义信息在记忆检索和错误识别中的相对贡献.
主要方法:
- 使用绝对 (slot代码,重叠) 和相对 (大图) 字母位置模型.
- 利用线性弹道积累器 (LBA) 模型将表示与响应数据和反应时间 (RT) 联系起来.
- 整合了word2vec的语义表示,以与正写模型进行比较.
主要成果:
- 相对位置模型更好地解释了四个数据集中的三个数据集的识别数据.
- 参数估计表明初始字母显著影响文字处理.
- 发现正写和语义表示对于项目间的相似性和错误识别几乎同样重要.
结论:
- 拼写相似性在识别记忆中起着至关重要的作用,挑战了优先考虑语义表示的理论.
- 虽然考虑到错误报警的变化,但该模型需要进一步开发以捕捉命中率的变化,这表明需要扩展表示.
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