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, 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 and multivariate 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.

Emotional arousal pattern (EMAP): A new database for modeling momentary subjective and psychophysiological responding to affective stimuli

“…Therefore, online ratings during the experience allow researchers to model the momentary and continuous changes in emotional experience (Poß et al, 2020). However, as only one experiential aspect can realistically be evaluated by a participant at a given time (Jolly et al, 2022), ratings are restricted to one dependent variable. While emotion categorization is a prevalent way to describe emotional states—that is, by valence—there is longstanding evidence that many cognitive processes vary depending on emotional arousal (e.g., Vogt et al, 2008).…”