A Hitchhiker's Guide towards Transactive Memory System Modeling in Small Group Interactions

Modeling Transactive Memory System (TMS) over time is an actual challenge of Human-Centered Computing. TMS is a group’s meta-knowledge indicating the attribute of “who knows what”. Conceiving and developing machines able to deal with TMS is a relevant step in the field of Hybrid Intelligence aiming at creating systems where human and artificial teammates cooperate in synergistic fashion. Recently, a TMS dataset has been proposed, where a number of audio and visual automated features and manual annotations are extracted taking inspiration from Social Sciences literature. Is it possible, on top of these, to model relationships between these engineered features and the TMS scores? In this work we first build and discuss a processing pipeline; then we propose four possible classifiers, two of which are artificial neural networks-based. We observe that the largest obstacle towards modeling the target relationships currently lies in the little data availability for training an automatic system. Our purpose, with this work, is to provide hints on how to avoid some common pitfalls to train these systems to learn TMS scores from audio/visual features.