Random forest classification results:A regularized random forest (Scikit-learn implementation). The implementation utilized the cross-entropy as an objective function with 500 decision trees. This number of decision trees was used as part of the regularization in order to avoid overfitting and also to approximate a simpler model which would enable comparison with the results from the logistic regression. Tree-based models are composed of nodes with each representing a level of depth in the model resulting from binary decision nodes where each node compares one feature value of the samples to a threshold. The maximum depth of each decision tree classifier was set to 1 so that only 1 best feature would be eventually used to make the decision. This means that during the training, with bootstrapped samples and features, the individual decision tree classifier ranks the importance of the feature by dropping one of the features and estimate a new decoding score after the dropping. The more loss in the decoding score compared to the original score, the more important the given feature was. In other words, we aimed to estimate the best feature among all features used (i.g confidence, awareness, and correctness). The majority rule was then applied to estimate the feature importance across all decision tree classifiers.Note the sum of feature importance of all features add up to 1 in the implementation of Scikit-learn: ( https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn . ensemble.RandomForestClassifier.feature_importances_ ), and so this will be the case across all trials back. Hence, the main effect of the time window in the ANOVA is not meaningful because the average feature importance at each time window would be exactly the same (i.e. 0.33) and consequential it is not reported.

AbstractMetacognition can be deployed retrospectively -to reflect on the correctness of our behavior- or prospectively -to make predictions of success in one’s future behavior or make decisions about strategies to solve future problems. We investigated the factors that determine prospective decision making. Human participants performed a visual discrimination task followed by ratings of visibility and response confidence. Prior to each trial, participants made prospective judgments. In Experiment 1, they rated their belief of future success. In Experiment 2, they rated their decision to adopt a focused attention state. 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 behavioral 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.