Machine learning predicts human prospective decision making

Metacognition can be deployed retrospectively (i.e. to reflect on the correctness of our recent behaviour) or prospectively (i.e. to make predictions of success in one9s future behaviour or make decisions about strategies to solve future problems). We sought to investigate the factors that determine this sort of prospective decision making in humans. Human participants performed a visual discrimination task followed by ratings of stimulus visibility and response confidence. Prior to each discrimination trial participants made prospective judgments concerning the upcoming task. In Experiment 1, they rated their belief of future success. In Experiment 2, they rated their decision to adopt a focussed attentional state. Both types of prospective decisions were related to behavioural performance in different ways. Prospective beliefs of success were associated with no performance changes while prospective decisions to engage attention were followed by better self-evaluation of the correctness of behavioural responses. Using standard machine learning classifiers we found that the current prospective decision could be predicted from information concerning task-correctness, stimulus visibility and response confidence from previous trials. In both Experiments, awareness and confidence were more diagnostic of the prospective decision than task correctness. Notably, classifiers trained with prospective beliefs of success in Experiment 1 predicted decisions to engage in Experiment 2 and vice-versa. These results indicate that the formation of these seemingly different prospective decisions share a common, dynamic representational structure.