利用协同过滤从稀疏评分中恢复个体情绪状态

IF 2.1 Q2 PSYCHOLOGY
Eshin Jolly, Max Farrens, Nathan Greenstein, Hedwig Eisenbarth, Marianne C. Reddan, Eric Andrews, Tor D. Wager, Luke J. Chang
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引用次数: 0

摘要

情绪研究的一个基本挑战是在不干扰情绪生成过程的情况下,以高粒度和时间精度测量感觉状态。在这里,我们介绍并验证了一种新的方法,其中稀疏地对响应进行采样,并使用一种称为协作过滤(CF)的计算技术来恢复丢失的数据。这种方法利用了个人经历中的结构化协变,并在开源Python工具箱Neighbors中提供。我们通过仅使用原始数据的一小部分来恢复密集的个人评级,在三种不同的实验环境中验证了我们的方法。在数据集1中,参与者(n=316)分别对6种不同离散情绪的112幅情绪图像进行了评分。在数据集2中,参与者(n=203)观看了8个简短的情感引人入胜的自传体故事,同时提供了他们情感体验强度的逐时评分。在数据集3中,具有不同社会偏好的参与者(n=60)在一个隐藏的乘数信任游戏中做出了76个关于回报多少钱的决定。在所有实验环境中,CF都能够准确地恢复缺失的数据,并且在很大程度上优于平均值和多变量插补,尤其是在个体变异性较大的环境中。这种方法将为情感科学研究开辟新的途径,使研究人员能够从情感体验中获得高维度的评分,而对情感生成过程的干扰最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering

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.

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