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

TL;DR

PREFAB is a low-cost, preference-based retrospective self-annotation method for affective computing that reduces user workload and improves confidence while accurately capturing affective inflections.

Contribution

It introduces a novel preference-learning approach with a preview mechanism for efficient affect annotation, focusing on affective inflections rather than full session labeling.

Findings

PREFAB outperforms baseline methods in modeling affective inflections.

It reduces annotator workload and temporal burden.

It enhances annotator confidence without sacrificing annotation quality.

Abstract

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.