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Pseudo-Label Augmentation for Affect Sensing in Small Collaborative Groups

Sep 2026 · Companion Publication of the 28th International Conference on Multimodal Interaction · 1 citation · 46 references
Computer Science

Abstract

Physiological affect sensing in naturalistic group interaction is typically limited more by label density than by sensor density: wearable devices produce thousands of time windows, while self-report labels are collected only a few times per session. This paper asks how to mine more supervisory signal from sparsely labelled group sessions. More broadly, we treat this as a step toward collective-state sensing: better individual affect estimates within a team help reveal the affective structure emerging at group level. Using GroupAffect-4, a four-person collaborative dataset with wearable physiology, eye tracking, Big Five personality, and post-task VAD labels, we study a progression of label-mining strategies: no augmentation, Gaussian Process (GP) pseudo-labelling, personality-aware trust weighting, and joint personality-plus-confidence weighting under a shared target-construction pipeline. We further ask whether individual predispositions (here, Big Five personality) together with group-level context improve which pseudo-labels should be trusted. They help, but not in the originally hypothesised graded way: the compressed BFI cosine-similarity range (0.91–0.99) renders weight ordering negligible (perm p = 0.39), so personality similarity acts as a same-team membership filter rather than a calibrated trust dial. In the known-team setting, pseudo-label augmentation improves over the labelled-only baseline, with the clearest benefit coming from within-team filtering rather than from fine-grained personality ranking. With smoothing, A1–A3 are tied to rounding on Valence and Arousal, while A3 has the highest Dominance score among the augmented SVM variants; the practical choice is therefore dimension-dependent. Cross-subject LOSO transfer remains encouraging for such a small and sparsely labelled dataset, especially on Arousal, whereas strict session-isolated LOGO removes the augmentation benefit. At the current scale of only 10 groups, LOGO should be read as a conservative lower bound on unseen-group transfer rather than as a definitive ceiling, but the present evidence shows that personality weighting mainly operates as a within-team mechanism.

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