# Overview Here we share data and code used in the pre-print *"Modulation of motor vigour by expectation of reward probability trial-by-trial is preserved in healthy ageing and Parkinson's disease patients"*. # Data - In *"raw JSON files Study1 and Study2"* are saved the raw `.JSON` files used in the first two studies. This folder includes the `.JSON` files for the *Study1* (37 healthy young [HYA], 20 patients diagnosed with Parkinson's Disease [PD] and 37 age-matched older adults [HOA]) and for the *Study2* (39 HYA divided into two groups [Q8_T and Q8_F] depending on their answers to a post-performance questionnaire). - Preprocessed data ready to be used for Bayesian Linear Mixed Models (code provided below) are stored in the *"data brms"* folder. This includes `.csv` files on performance tempo and reaction times for Study1, 2 and 3. - The authors thank former student of the MSc course in Computational Cognitive Neuroscience at Goldsmiths, Osama Shah, for programming the online task for this study. This was part of his MSc project. # Code - We share the code `BLMM_brms.R` used to perform Bayesian Linear Mixed models on our data. This code allows to assess how predictions about the strength of the action-reward contingencies modulate motor performance. It also allows to investigate between-group differences in the sensitivity of motor performance to the strength of these expectations. - We also share the Matlab codes used to fit the trial-wise performance in our task with the Hierarchical Gaussian Filter (HGF). This includes a main code (*"run_HGF"*), the code for the perceptual only model (*"run_HGF_perceptual"*) and the code for the perceptual + response model ("*run_HGF_perceptual_response*").