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Predicting user confidence during visual decision making

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Figure 1: Two examples tasks from the WASI test suite. For each task, userswere asked to select which of the five options (1-5) should be placed at the ques-tion mark in the stimuli, such that the rows and columns of this had matchingcorrespondence
Figure 2:Boxplots of confidence values, and stacked bar charts of correct(green)/incorrect (red) responses reported grouped by user (top) or by task(bottom), Green/Blue colouring on boxplots indicates which items are subse-quently used for training/testing in the manually curated data splits.
Figure 3: Scatter plots of relationship between proportion of correct responsesand task id (left), and time taken (right)
Figure 4: Architectures for ensemble predictors.Blue lines show process oftraining items but “sees” all of the features for each training or test item.Hybrid - each model is trained from amodel induction and red lines show process of predicting value for a new testitem.(a): Conventional - each model is trained from a different subset of (b): disjoint subset of features but ‘’sees” theentire training set.
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