Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is predominately attributed to the affect labels which inadvertently include biases inherent to the subjective nature of emotion and its labeling. The response to such limitations is usually augmenting the dataset with more annotations per data point; however, this is not possible when we are interested in self-reports via first-person annotation. Moreover, outlier detection methods based on inter-annotator agreement only consider the annotations themselves and ignore the context and the corresponding affect manifestation. This paper reframes the ways one may obtain a reliable ground truth of affect by transferring aspects of causation theory to affective computing. In particular, we assume that the ground truth of affect can be found in the causal relationships between elicitation, manifestation and annotation that remain \emph{invariant} across tasks and participants. To test our assumption we employ causation inspired methods for detecting outliers in affective corpora and building affect models that are robust across participants and tasks. We validate our methodology within the domain of digital games, with experimental results showing that it can successfully detect outliers and boost the accuracy of affect models. To the best of our knowledge, this study presents the first attempt to integrate causation tools in affective computing, making a crucial and decisive step towards general affect modeling.
翻译:积极计算试图揭示影响诱导、影响表现和影响说明之间的未知关系。但是,影响的基本真相主要归结于影响标签,这些标签无意中包含情感及其标签主观性质固有的偏见。对此类限制的反应通常是以每个数据点的更多说明来增加数据集;然而,当我们有兴趣通过第一人注解进行自我报告时,这是不可能的。此外,基于通知者协议的异端检测方法只考虑说明本身,忽视上下文和相应的影响表现。本文重新定义了通过将因果关系理论的某些方面转移至感动计算来获取可靠的影响地面真相的方法。特别是,我们假设,在对各种任务和参与者的诱导、表现和注释之间的因果关系中可以找到影响地面真相。为了检验我们的假设,我们使用基于因果关系的启发性方法来检测感知外体的外体,以及建立对参与者和任务具有强力的模型。我们验证了我们在数字游戏领域采用的方法,通过将因果关系理论理论转移到感动性计算。我们假设地面的真相可以发现出在任务和任务中进行最精确性的研究。