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：人工智能革命最令人瞩目的成就之一是开发了能够生成有意义的文本、并在无需额外训练的情况下以简单英语响应指令的大语言模型。我们在此展示，语言模型可作为研究人类对有意义材料记忆的科学工具。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在不同长度叙事中的再认与回忆数据。研究发现，回忆与再认表现均随叙事长度线性递增。此外，为探究叙事理解在记忆中的作用，我们使用打乱顺序的故事版本重复了上述实验。结果发现，尽管回忆表现显著下降，再认能力基本未受影响。值得注意的是，在打乱条件下，回忆似乎遵循原始叙事顺序而非呈现顺序，这表明记忆中发生了基于语境的叙事重建。