The paper presents a Bayesian model framework for estimating individual perceptual uncertainty from continuous tracking data, taking into account motor variability, action cost, and possible misestimation of the generative dynamics. While the contribution is mostly technical, the analyses are well done and clearly explained. The paper provides therefore a didactic resource for students wishing to implement similar models on continuous action data.
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