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A closer look at cognitive control: differences in resource allocation during updating, inhibition and switching as revealed by pupillometry

机译:仔细研究认知控制:通过瞳孔测定法揭示的更新,抑制和切换过程中资源分配的差异

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The present study investigated resource allocation, as measured by pupil dilation, in tasks measuring updating (2-Back task), inhibition (Stroop task) and switching (Number Switch task). Because each cognitive control component has unique characteristics, differences in patterns of resource allocation were expected. Pupil and behavioral data from 35 participants were analyzed. In the 2-Back task (requiring correct matching of current stimulus identity at trial p with the stimulus two trials back, p ?2) we found that better performance (low total of errors made in the task) was positively correlated to the mean pupil dilation during correctly responding to targets. In the Stroop task, pupil dilation on incongruent trials was higher than those on congruent trials. Incongruent vs. congruent trial pupil dilation differences were positively related to reaction time differences between incongruent and congruent trials. Furthermore, on congruent Stroop trials, pupil dilation was negatively related to reaction times, presumably because more effort allocation paid off in terms of faster responses. In addition, pupil dilation on correctly-responded-to congruent trials predicted a weaker Stroop interference effect in terms of errors, probably because pupil dilation on congruent trials were diagnostic of task motivation, resulting in better performance. In the Number Switch task we found higher pupil dilation in switch as compared to non-switch trials. On the Number Switch task, pupil dilation was not related to performance. We also explored error-related pupil dilation in all tasks. The results provide new insights in the diversity of the cognitive control components in terms of resource allocation as a function of individual differences, task difficulty and error processing.
机译:本研究调查了通过瞳孔扩张测量的资源分配,这些资源用于测量更新(2-Back任务),抑制(Stroop任务)和切换(Number Switch任务)的任务。因为每个认知控制组件都具有独特的特征,所以预期资源分配方式会有所不同。分析了来自35名参与者的学生和行为数据。在2-Back任务(要求试验p中当前刺激身份正确匹配,且刺激2次试验,p≤2正确匹配)中,我们发现更好的性能(任务中的总错误率低)与平均瞳孔呈正相关在正确响应目标时进行扩张。在Stroop任务中,不一致试验的瞳孔扩张程度高于一致试验的瞳孔扩张程度。不一致和一致试验的瞳孔扩张差异与不一致和一致试验之间的反应时间差异呈正相关。此外,在一致的Stroop试验中,瞳孔扩张与反应时间呈负相关,大概是因为更多的精力分配以更快的反应获得了回报。此外,对正确响应的全等试验的瞳孔扩张预测就误差而言较弱的Stroop干扰效应,这可能是由于对全等试验的瞳孔扩张对任务动机的诊断,从而导致更好的表现。在数字转换任务中,我们发现与非转换试验相比,转换中的瞳孔扩张更高。在“数字转换”任务上,瞳孔扩张与成绩无关。我们还探索了在所有任务中与错误相关的瞳孔扩张。研究结果提供了关于认知控制组件多样性的新见解,这些认知控制组件是作为个体差异,任务难度和错误处理的函数的资源分配。

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