Assistant Professor, Marketing
- Customer Experience Design (CxDesign)
MKTG 358 (Win)
- Independent Studies (4)
Semantic determinants of memorability.
2023; 239: 105497
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.
View details for DOI 10.1016/j.cognition.2023.105497
View details for PubMedID 37442022