Johannes Bill

The paper investigates causal inference during closed-loop navigation by examining how humans disambiguate self- and object-motion, showing misattribution patterns and their dependence on motion conditions and target location.

This paper proposes an online hierarchical Bayesian inference model to explain human motion perception and demonstrates its ability to qualitatively and quantitatively explain various motion stimuli.

This study shows human motion perception aligns with Bayesian structural inference in psychophysics experiments, matching performance and error patterns.

The paper investigates how humans use hierarchical motion structure in visual perception through controlled stimuli and experiments.