UIUC Quant Brownbag

Understanding how people judge their own knowledge and abilities is crucial for everything from education to medical diagnosis. Yet we lack precise models that can predict how confident someone will be in their decisions. Here, we introduce a novel quantitative model that allows us, uniquely, to make predictions about confidence judgments from accuracy alone and to transfer these predictions fully across tasks. We focus on a suite of visual working memory tasks, though our modelling approach applies to a wide range of perception and memory phenomena. Following recent proposals, our approach postulates that generalizable models of metacognition must consider not just a discrepancy between metacognitive processes and behaviour, but also people’s latent representations of the environment and its structure. We demonstrate that this model successfully predicts metacognitive judgments in the typical situation where confidence tracks memory performance, and that it goes beyond previous work by making high precision predictions of entire distributions of memory errors as a function of confidence. Moreover, we show that the model predicts, rather than fits, a range of dissociations between memory performance and confidence caused by careful choice of the probes used to test memory and by variations in how people represent the structure of the stimulus space. Our framework offers a straightforward explanation of metacognition, allows prediction in a novel way, integrates recent advancements in computational modelling of metacognition with artificial agents and humans, and sheds new light on how judgments of confidence are constrained by memory organization.