A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task (DST) to a format that allows measuring concurrent high-density electroencephalography (EEG). We used multivariate pattern analysis (MVPA) to decode conflict-related electrophysiological markers associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not have been obtained with classic analytical strategies, open novel avenues of research.

中文翻译：

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脑电图数据的多变量模式分析技术研究侧翼干扰效应

认知神经科学的一个核心挑战是了解构成根据内部目标控制我们行为的能力的神经机制。侧翼任务需要对由触发不相容动作倾向的干扰物包围的刺激做出反应，通常用于衡量这种冲突。尽管在这些情况下产生的干扰已被广泛研究，但多变量分析技术可以为潜在的神经机制提供新的启示。目前的研究是将嵌入在需求选择任务 (DST) 中的干扰 Flanker 范式调整为允许测量并发高密度脑电图 (EEG) 的格式的初步近似。我们使用多变量模式分析 (MVPA) 以时频解析方式解码与一致或不一致目标事件相关的冲突相关电生理标记。我们的结果复制了使用其他分析方法获得的结果，并提供了有关潜在机制动态的新信息，这些机制显示出重新实例化的迹象。我们的发现，其中一些是经典分析策略无法获得的，开辟了新的研究途径。