Stanford University, United States of America.
University of Pennsylvania, United States of America.
Cognition. 2023 Oct;239:105497. doi: 10.1016/j.cognition.2023.105497. Epub 2023 Jul 11.
We examine why some words are more memorable than others by using predictive machine learning models applied to word recognition and recall datasets. Our approach provides more accurate out-of-sample predictions for recognition and recall than previous psychological models, and outperforms human participants in new studies of memorability prediction. Our approach's predictive power stems from its ability to capture the semantic determinants of memorability in a data-driven manner. We identify which semantic categories are important for memorability and show that, unlike features such as word frequency that influence recognition and recall differently, the memorability of semantic categories is consistent across recognition and recall. Our paper sheds light on the complex psychological drivers of memorability, and in doing so illustrates the power of machine learning methods for psychological theory development.
我们通过使用应用于单词识别和召回数据集的预测机器学习模型，来研究为什么有些单词比其他单词更易被记住。与之前的心理学模型相比，我们的方法为识别和召回提供了更准确的样本外预测，并且在记忆预测的新研究中优于人类参与者。我们的方法的预测能力源于其以数据驱动的方式捕捉易记性的语义决定因素的能力。我们确定了哪些语义类别对易记性很重要，并表明，与影响识别和召回的词频等特征不同，语义类别的易记性在识别和召回中是一致的。本文揭示了易记性的复杂心理驱动因素，并展示了机器学习方法在心理理论发展中的强大功能。
