Topic: goodness of fit

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 ($L$)