One of the most impressive achievements of the AI revolution is the development of large language models that can generate meaningful text and respond to instructions in plain English with no additional training necessary. Here we show that language models can be used as a scientific instrument for studying human memory for meaningful material. We developed a pipeline for designing large scale memory experiments and analyzing the obtained results. We performed online memory experiments with a large number of participants and collected recognition and recall data for narratives of different lengths. We found that both recall and recognition performance scale linearly with narrative length. Furthermore, in order to investigate the role of narrative comprehension in memory, we repeated these experiments using scrambled versions of the presented stories. We found that even though recall performance declined significantly, recognition remained largely unaffected. Interestingly, recalls in this condition seem to follow the original narrative order rather than the scrambled presentation, pointing to a contextual reconstruction of the story in memory.

翻译：人工智能革命最引人瞩目的成就之一，是开发出能够生成有意义的文本、并无需额外训练即可根据自然语言指令进行回应的大语言模型。本研究表明，语言模型可作为研究人类对有意义的材料进行记忆的科学工具。我们开发了一套用于设计大规模记忆实验并分析所得结果的流程。我们通过在线记忆实验，收集了大量参与者对不同长度叙述性文本的再认与回忆数据。研究发现，回忆与再认成绩均随叙述长度呈线性增长。此外，为探究叙述理解在记忆中的作用，我们采用打乱版本的故事重复了上述实验。结果显示，尽管回忆成绩显著下降，但再认能力基本未受影响。值得注意的是，在打乱条件下，回忆内容似乎遵循原始叙述顺序而非打乱后的呈现顺序，这表明记忆中存在对故事语境的重新建构过程。