Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI
It addresses understanding human decision-making in joint-action for psychology and AI, but is incremental in applying existing methods to a specific domain.
This study used machine learning and explainable AI to model human decision-making in a dyadic herding task, finding that trained models could predict target selections of experts and novices before conscious intent, with experts more influenced by co-herder information.
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.