Compensatory Consumption dataset (ex1)

The dataset contains behavioral data, raw EEG recordings, analysis code, and machine-learning files collected in a frustration-induction and compensatory consumer decision-making experiment. It is intended to support research on compensatory consumption, value-based decision-making under frustration, and the cognitive and neural mechanisms underlying consumer choice.Dataset Contents:Behavioral Data:Trial-level measures of purchase choice, purchase rate, and response time for within-domain and cross-domain compensatory products under frustration and control conditions.Neural Data (EEG):Raw EEG recordings collected during the frustration-induction task and the consumer decision-making task.

The dataset includes event markers, channel information, and corresponding experimental labels for all trials.Analysis Code:Code used for behavioral analysis, EEG preprocessing, ERP analysis, time-frequency analysis, and related statistical testing.Machine-Learning Prediction Files:Model training and validation results are provided for multiple classifiers, including SVR, LDA, Logistic Regression, RF, KNN, NB, and GBM, along with optimized hyperparameters and performance metrics (e.g., accuracy, AUC, and F1 score).