Using natural language processing to understand individual differences: Integrating quantitative and qualitative approaches to memory and perception

Much research in memory and perception focuses on quantitative outcome variables. Despite the utility of such metrics, they are often insufficient for understanding individual differences in how people encode and retrieve items. To address this gap, we developed a natural language processing approach to analyze narrative responses to questions about the strategies people used in similarity rating and recognition judgment tasks for a novel set of auditory timbre stimuli. We applied topic modeling to 779 responses to three questions about: how people judged similarity between sounds; how people recognized previously heard sounds; and how people formed impressions of the sounds they heard. 20 topics characterized the similarity responses, 16 characterized the recognition judgements, and 30 characterized people’s impressions. Principal components analysis identified latent themes within each topic set. Individual differences in topic prominence were related to recognition memory accuracy and attention to dimensions in similarity ratings. These techniques represent a general methodology for triangulating quantitative, qualitative, and computational methods in memory research.