# The Invariant Ground Truth of Affect

arXiv: https://arxiv.org/abs/2210.07630 · HTML: https://mlsanity.com/paper/2210.07630/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [One-Class Convolutional Neural Network](https://mlsanity.com/paper/1901.08688/) (2019)
- [The Arousal video Game AnnotatIoN (AGAIN) Dataset](https://mlsanity.com/paper/2104.02643/) (2021)
- [Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality](https://mlsanity.com/paper/2103.06541/) (2021)
- [Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data](https://mlsanity.com/paper/1907.06671/) (2019)
- [The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games](https://mlsanity.com/paper/2101.10706/) (2021)
- [From Pixels to Affect: A Study on Games and Player Experience](https://mlsanity.com/paper/1907.02288/) (2019)
- [PAGAN: Video Affect Annotation Made Easy](https://mlsanity.com/paper/1907.01008/) (2019)
- [Data-Driven Background Subtraction Algorithm for in-Camera Acceleration in Thermal Imagery](https://mlsanity.com/paper/1608.00229/) (2016)

## Built on by (most cited first, 6 of 6)

- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [Across-Game Engagement Modelling via Few-Shot Learning](https://mlsanity.com/paper/2409.13002/) (2024)
- [Privileged Contrastive Pretraining for Multimodal Affect Modelling](https://mlsanity.com/paper/2508.03729/) (2025)
- [LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics](https://mlsanity.com/paper/2604.07193/) (2026)
- [Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction](https://mlsanity.com/paper/2604.07198/) (2026)
- [Resource-Constrained Affect Modelling via Variance Regularisation Pruning](https://mlsanity.com/paper/2605.27479/) (2026)

## Abstract (arXiv)

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 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.