# PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation

arXiv: https://arxiv.org/abs/2601.13904 · HTML: https://mlsanity.com/paper/2601.13904/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 76 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [On The Effect of Auxiliary Tasks on Representation Dynamics](https://mlsanity.com/paper/2102.13089/) (2021)
- [Improving Multi-Task Generalization via Regularizing Spurious Correlation](https://mlsanity.com/paper/2205.09797/) (2022)
- [On the Consistency of Ordinal Regression Methods](https://mlsanity.com/paper/1408.2327/) (2014)
- [A dataset of continuous affect annotations and physiological signals for emotion analysis](https://mlsanity.com/paper/1812.02782/) (2018)
- [Functional Regularization for Representation Learning: A Unified Theoretical Perspective](https://mlsanity.com/paper/2008.02447/) (2020)
- [The Arousal video Game AnnotatIoN (AGAIN) Dataset](https://mlsanity.com/paper/2104.02643/) (2021)
- [The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games](https://mlsanity.com/paper/2101.10706/) (2021)
- [PAGAN: Video Affect Annotation Made Easy](https://mlsanity.com/paper/1907.01008/) (2019)
- [RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks](https://mlsanity.com/paper/2204.06901/) (2022)
- [Jointly Aligning and Predicting Continuous Emotion Annotations](https://mlsanity.com/paper/1907.03050/) (2019)
- [A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors](https://mlsanity.com/paper/2305.08752/) (2023)
- [I Feel I Feel You: A Theory of Mind Experiment in Games](https://mlsanity.com/paper/2001.08656/) (2020)

## Abstract (arXiv)

Self-annotation is the gold standard for collecting affective state labels in affective computing. Existing methods typically rely on full annotation, requiring users to continuously label affective states across entire sessions. While this process yields fine-grained data, it is time-consuming, cognitively demanding, and prone to fatigue and errors. To address these issues, we present PREFAB, a low-budget retrospective self-annotation method that targets affective inflection regions rather than full annotation. Grounded in the peak-end rule and ordinal representations of emotion, PREFAB employs a preference-learning model to detect relative affective changes, directing annotators to label only selected segments while interpolating the remainder of the stimulus. We further introduce a preview mechanism that provides brief contextual cues to assist annotation. We evaluate PREFAB through a technical performance study and a 25-participant user study. Results show that PREFAB outperforms baselines in modeling affective inflections while mitigating workload (and conditionally mitigating temporal burden). Importantly PREFAB improves annotator confidence without degrading annotation quality.