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The Intensity of Early Attentional Processing but Not Conflict Monitoring Determines the Size of Subliminal Response Conflicts

机译:早期注意处理的强度(而不是冲突监视)确定了阈下反应冲突的大小

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摘要

Response conflicts hamper goal-directed behavior and may be evoked by both consciously and subliminally (unconsciously) processed information. Yet, not much is known about the mechanisms and brain regions driving the size of subliminally induced conflicts. We hence combined a response conflict paradigm featuring subliminal primes and conscious flankers with in-depth neurophysiological (EEG) analyses, including source localization in a sample of N = 243 healthy subjects. Intra-individual differences in the size of subliminal conflicts were reflected both during early attentional stimulus processing (prime-associated N1 and target-associated P1 and N1 amplitudes) and conflict monitoring (N2 amplitudes). On the neuroanatomical level, this was reflected by activity modulations in the TPJ (BA39, BA40) and V2 (BA18), which are known to be involved in attentional stimulus processing and task set maintenance. In addition to a “standard” analysis of event-related potentials, we also conducted a purely data-driven machine learning approach using support vector machines (SVM) in order to identify neurophysiological features which do not only reflect the size of subliminal conflict, but actually allow to classify/predict it. This showed that only extremely early information processing (about 65 ms after the onset of the prime) was predictive of subliminal conflict size. Importantly, this predictive feature occurred before target information could even be processed and was reflected by activity in the left middle frontal gyrus (BA6) and insula (BA13). We conclude that differences in task set maintenance and potentially also in subliminal attentional processing of task-relevant features, but not conflict monitoring, determine the size of subliminally induced response conflicts.
机译:响应冲突阻碍了目标导向的行为,有意识和潜意识(潜意识)处理的信息都可能引起反应冲突。然而,关于驱动潜意识冲突规模的机制和大脑区域知之甚少。因此,我们结合了以潜意识下潜质和自觉侧翼为特征的反应冲突范式与深入的神经生理学(EEG)分析,包括对N = 243名健康受试者的样本中的源定位。在早期注意刺激处理(原始关联的N1和目标关联的P1和N​​1幅度)和冲突监控(N2幅度)期间,都反映了阈下冲突大小的个体内部差异。在神经解剖学水平上,这通过TPJ(BA39,BA40)和V2(BA18)中的活动调节反映出来,已知这些活动参与了注意刺激处理和任务集维护。除了对事件相关电位的“标准”分析之外,我们还使用支持向量机(SVM)进行了纯数据驱动的机器学习方法,以识别不仅反映下意识下冲突的大小,而且反映出下意识冲突大小的神经生理学特征。实际上允许对其进行分类/预测。这表明,只有极早的信息处理(初免发作后约65毫秒)才能预测下意识下的冲突大小。重要的是,这种预测特征在目标信息甚至无法处理之前就已经出现,并通过左中额回(BA6)和岛突(BA13)中的活动反映出来。我们得出的结论是,任务集维护的差异以及潜在的与任务相关的功能的潜意识注意处理(而不是冲突监视)的差异决定了潜意识诱发的响应冲突的大小。

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