Probabilistic modeling of magnitude perception

In the project, human perception of magnitudes, such as distance or duration, will be analyzed experimentally and simulated using probabilistic modeling. The modeling will be based on Bayesian inference, which provides a framework for integrating prior knowledge, sensory information, and decision-making processes. The project will specifically deal with the role of likelihood functions in assigning and learning sensory uncertainty and with the role of context in choosing the appropriate generative model underlying the perceptual estimation process.