Data coming from different sensors often capture information related to common latent factors.

Computational cognitive models are a fundamental tool in behavioral neuroscience.

Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity.

TreeV AE hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data.

Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception.

Therefore, it's important to study whether we can and also how to