Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering.

ABSTRACT: A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in which responses are sparsely sampled and the missing data are recovered using a computational technique known as collaborative filtering (CF). This approach leverages structured covariation across individual experiences and is available in Neighbors, an open-source Python toolbox. We validate our approach across three different experimental contexts by recovering dense individual ratings using only a small subset of the original data. In dataset 1, participants (n=316) separately rated 112 emotional images on 6 different discrete emotions. In dataset 2, pa

SUBMITTER: Jolly E 

PROVIDER: S-EPMC9743951 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature