PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation
This addresses the challenge of reducing workload and errors in affective computing for researchers and practitioners, though it is incremental as it builds on existing preference-learning and peak-end rule concepts.
The paper tackled the problem of time-consuming and error-prone self-annotation for affective states by introducing PREFAB, a low-budget method that targets affective inflection regions, which outperformed baselines in modeling affective inflections and improved annotator confidence without degrading quality.
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.