Topic: degrees of freedom

Yidun Wan constructs Hamiltonians that connect parent and condensed string-net models to describe bosonic anyon condensation in topological phases. The framework classifies possible condensations and identifies the degrees of freedom of the resulting models. Its microscopic categorical description refines the usual modular-tensor-category perspective and gives algorithms for calculating the categorical quantities through physical model data.

In order to survive, animals constantly face decisions between competing interpretations for noisy and sparse sensory data. In statistics, multiple formal frameworks exist to tackle this problem, known as Model Selection (MS). However, despite the abundance of theoretical methods, the way in which MS is performed by the brain is poorly understood. Previous work showed that humans have a bias towards simpler models or interpretations of sensory data [1,2,3]. Human model selection strategies have been quantitatively described using a linear combination of the models Maximum Log-Likelihood ($L$)