Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can be identified in the dynamics of neurophysiological (EEG) processes at the single-trial level and be used to predict the presence of conflicts in a given moment. Applying deep learning, which was blind to cognitive theory, on single-trial EEG data allowed to predict the presence of conflict in ~95% of subjects ~33% above chance level. Neurophysiological features related to attentional and motor response selection processes in the occipital cortex and the superior frontal gyrus contributed most to prediction accuracy. Importantly, deep learning was able to identify predictive neurophysiological processes in single-trial neural dynamics. Hence, mathematical (artificial intelligence) approaches may be used to foster the validation and development of links between cognitive theory and neurophysiology of human behavior.

对于目标导向的行为，有效的动作控制是必不可少的。不同的理论已经强调了注意或响应选择子过程对于动作控制的重要性。然而，目前尚不清楚在单次试验的神经生理学（EEG）动力学过程中可以识别这些过程的程度，并可以用来预测给定时刻冲突的存在。在单项EEG数据上应用对认知理论无视的深度学习，可以预测〜95％的受试者中的冲突的存在，高于机会水平的33％。与枕叶和上额额回中的注意力和运动反应选择过程有关的神经生理学特征对预测准确性的影响最大。重要的，深度学习能够确定单次试验神经动力学中的预测性神经生理过程。因此，数学（人工智能）方法可以用于促进验证和发展人类行为的认知理论和神经生理学之间的联系。