Mouse movement trajectories in online surveys has been shown to reflect question difficulty during online surveys. We explore the use of deep neural network embeddings to summarize these trajectories, using a ResNet-based architecture applied to time-normalized cursor paths. Clustering and UMAP visualization of these embeddings on a subset of the data reveal a combination of large, dense clusters and smaller, distinct subgroups, suggesting diverse movement patterns among respondents. These preliminary findings indicate that neural embeddings can capture meaningful structure in survey interaction behavior, providing a foundation for further investigation into individual differences and adaptive survey design.
inproceedings BWH+26
BibTeXKey: BWH+26