This is an interesting paper combining machine learning and psychology, and brings interesting insights about what can be learned from naturalistic, egocentric, real-world datasets, and how the learned representations can be used on downstream tasks. It’s well-written and clearly presented, and likely of interest to the general NeurIPS audience. Reviewers 3 and 4 initially had concerns about the model’s performance, and suggestions about using other datasets. After discussion and the rebuttal, they were able to be convinced that these run counter to the main motivation of the study. They subsequently raised their scores and all reviewers - and myself - are in agreement to accept.