  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, participants (n=203) watched 8 short emotionally engaging
autobiographical stories while simultaneously providing moment-by-moment
ratings of the intensity of their affective experience. In dataset 3,
participants (n=60) with distinct social preferences made 76 decisions about
how much money to return in a hidden multiplier trust game. Across all
experimental contexts, CF was able to accurately recover missing data and
importantly outperformed mean imputation, particularly in contexts with greater
individual variability. This approach will enable new avenues for affective
science research by allowing researchers to acquire high dimensional ratings
from emotional experiences with minimal disruption to the emotion-generation
process.
