Decoding covert motivations of free riding and cooperation from multi-feature pattern analysis of EEG signals

Cooperation and free riding are among the most frequently observed behaviors in human social decision-making. In social interactions, the effects of strategic decision processes have been consistently reported in iterative cooperation decisions. However, the neural activity immediately after new information is presented, the time at which strategy learning potentially starts has not yet been investigated with high temporal resolution. Here, we implemented an iterative, binary public goods game that simulates cooperation/free riding behavior. We applied the multi-feature pattern analysis method by using a support vector machine and the unique combinatorial performance measure, and identified neural features from the single-trial, event-related spectral perturbation at the result-presentation of the current round that predict participants' decisions to cooperate or free ride in the subsequent round. We found that neural oscillations in centroparietal and temporal regions showed the highest predictive power through 10-fold cross-validation; these predicted the participants' next decisions, which were independent of the neural responses during their own preceding choices. We suggest that the spatial distribution and time-frequency information of the selected features represent covert motivations to free ride or cooperate in the next round and are separately processed in parallel with information regarding the preceding results.