Model selection analysis.

We performed a model selection analysis using both Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Both AIC and BIC consider how well a model fits the data while penalizing the number of free parameters. For both analyses, a lower score indicates a better model fit given the total number of free parameters. Experiments 1 & 2 were fit simultaneously and given a single combined score for both AIC and BIC. For Experiment 3, the Parkinson’s disease and age-matched control groups were fit separately and each group was given their own AIC and BIC scores. The table shows the sum of all AIC and BIC scores calculated for Experiments 1, 2, & 3. Both analyses would suggest Model 4 as the best-fit model across all experiments.