  This study uses supervised machine learning (SML) and explainable artificial
intelligence (AI) to model, predict and understand human decision-making during
skillful joint-action. Long short-term memory networks were trained to predict
the target selection decisions of expert and novice actors completing a dyadic
herding task. Results revealed that the trained models were expertise specific
and could not only accurately predict the target selection decisions of expert
and novice herders but could do so at timescales that preceded an actor's
conscious intent. To understand what differentiated the target selection
decisions of expert and novice actors, we then employed the explainable-AI
technique, SHapley Additive exPlanation, to identify the importance of
informational features (variables) on model predictions. This analysis revealed
that experts were more influenced by information about the state of their
co-herders compared to novices. The utility of employing SML and explainable-AI
techniques for investigating human decision-making is discussed.
