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Deep neural networks (DNNs) have become remarkably successful in dataprediction, and have even been used to predict future actions based on limitedinput. This raises the question: do these systems actually "understand" theevent similar to humans? Here, we address this issue using videos taken from anaccident situation in a driving simulation. In this situation, drivers had tochoose between crashing into a suddenly-appeared obstacle or steering their caroff a previously indicated cliff. We compared how well humans and a DNNpredicted this decision as a function of time before the event. The DNNoutperformed humans for early time-points, but had an equal performance forlater time-points. Interestingly, spatio-temporal image manipulations andGrad-CAM visualizations uncovered some expected behavior, but also highlightedpotential differences in temporal processing for the DNN.

Predicting decision-making in the future: Human versus Machine