Similar history biases for distinct prospective decisions of self-performance

Abstract

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.

Measuring metacognitive performance: type 1 performance dependence and test-retest reliability

“…2020 ; Konishi et al. 2020 ; Mei et al. 2020 ; Ordin and Polyanskaya 2020 ) or in which relationships between two domains (e.g.…”

Confidence guides priority between forthcoming tasks

“…For instance, people have been shown to exhibit a similar amount of idiosyncratic over/underconfidence across domains 44 . Also, prospective reports influence subsequent retrospective estimates 43 , 46 . However, retrospective judgments seem to be more accurate, probably because trial-specific cues can be used 47 .…”

Using serial dependence to predict confidence across observers and cognitive domains

“…The present approach using pattern classification with out-of-sample generalization thereby provides evidence that confidence generation can be reliably quantified and predicted across new samples and experimental contexts. The use of machine learning can prove very useful towards developing predictive models of confidence across different populations and experimental contexts (see (Fleming et al 2016;Mei et al 2020), for a similar approach to predict prospective beliefs of self-performance). It is difficult to make conclusions regarding the level of ROC prediction scores obtained here without a prior context of similar studies using different measures of predictive performance.…”