The weighted evidence-and-visibility model explains choice and choice confidence in binary decision tasks. Is was mainly applied to perceptual decision tasks.
It is based on signal detection theory in that it assumes normal distributed evidence depending on the stimulus input (e.g. signal vs. noise) which is compared to a criterion to form binary decisions. To explain confidence judgments, the model assumes that task difficulty may be estimated in independently generating a second normally distributed variable, the visibility. Mean of visibility is always positive and depends also on the sensitivity parameter. To form confidence judgments, the decision evidence and visibility variable are combined in a weighted sum. The resulting confidence variable is then compared to a set of thresholds to form discrete confidence judgments.
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