Effort-Based Reward Valuation: EEG data

Dataset related to the paper entitled “Tracking the Temporal Dynamics of Post-Effort Reward Valuation: The Role of Individual Differences in Physical Effort Valuation”. This dataset includes: 1) Effort-Doors task (PsychoPy script)“EffortDoors_PsychoPy_Task.zip” contains the PsychoPy script and the images used in the Effort-Doors task. 2) Scalp topography plots “RewP Gain minus Loss for Low Effort.tif”: scalp topography of the ΔRewP in the low-effort condition. “RewP Gain minus Loss for High Effort.tif”: scalp topography of the ΔRewP in the high-effort condition. “RewP Gain minus Loss.tif”: scalp topography of the ΔRewP collapsed across effort conditions. 3) R script for data management and analysis “Effort_Reward_Final.R” contains the code used for the analysis of EEG outcomes and self-reported data. “Effort_Reward_Behavioural_Execution_Press_rate.R” contains the code used for processing the Effort-Doors behavioural data and conducting the supplementary trial-wise behavioural check based on execution press rate. 4) EEG data, self-reported and behavioural data “Mean_Amplitudes_with_Odd_Evens_and_Data_for_ERP_Plots.xlsx” includes two subtables: Table 1 contains mean ERP amplitudes for each participant across electrodes and experimental conditions (Gain/Loss × HE/LE × Odds/Evens). Table 2 contains time-resolved ERP data across conditions, used for waveform plotting. “RewP_Data.xlsx” includes two subtables: Table 1 contains ERP data and self-reported data. Table 2 provides explanations for abbreviated names of some self-reported variables “Effort_Reward_behavioural_raw_data.csv” contains the raw behavioural data from the Effort-Doors task. These data were used for the supplementary trial-wise behavioural check examining whether the high-effort versus low-effort distinction was behaviourally expressed and whether this distinction changed across trial progression.