Fig. 4.

Illustration of the implemented multilevel model to estimate the degree of functional dimensionality, corresponding to step 4 in Fig. 2. The observed averaged dimensionality estimates per participant are assumed to be sampled from an underlying subject-specific t-distribution with mean ${\mu }_i$ and standard deviation ${\sigma }_i$. The standard deviation $\widehat{{\sigma }_i}$ of the participants' dimensionality estimates is assumed to be sampled from a normal distribution with mean ${\sigma }_i$ and a standard deviation of 1. The subject-specific t-distributions of ${\mu }_i$ are assumed to come from a population distribution with a normally distributed mean μ and variance σ. Subject-specific standard deviations ${\sigma }_i$ are assumed to come from a uniform distribution, ranging from 0 to max(${\sigma }_i$). At the top level, a uniform prior is implemented. Mean and variance of the normal distribution of population means μ are assumed to come from a uniform distribution ranging from 1 to $m-1$ and 0 to ${\sigma }_{max}$, respectively. Distributions were derived from https://github.com/rasmusab/distribution_diagrams.