Chung Dongil, Yun Kyongsik, Jeong Jaeseung
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, South Korea and Virginia Tech Carilion Research Institute, Roanoke, VA 24016, USA.
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, South Korea and.
Soc Cogn Affect Neurosci. 2015 Sep;10(9):1210-8. doi: 10.1093/scan/nsv006. Epub 2015 Feb 16.
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.
合作与搭便车是人类社会决策中最常观察到的行为。在社会互动中，战略决策过程的影响在迭代合作决策中一直有报道。然而，新信息呈现后立即出现的神经活动，即策略学习可能开始的时间，尚未以高时间分辨率进行研究。在此，我们实施了一个迭代的二元公共物品博弈，模拟合作/搭便车行为。我们使用支持向量机和独特的组合性能度量应用多特征模式分析方法，并从当前轮结果呈现时的单次试验、事件相关频谱扰动中识别出预测参与者在下一轮合作或搭便车决策的神经特征。我们发现，通过10折交叉验证，中央顶叶和颞叶区域的神经振荡显示出最高的预测能力；这些预测了参与者的下一个决策，而这些决策与他们自己之前选择时的神经反应无关。我们认为，所选特征的空间分布和时频信息代表了下一轮搭便车或合作的隐性动机，并与关于先前结果的信息并行分别处理。
