通过Stroop干扰效应解码选择性注意和认知控制加工:一项事件相关脑电图衍生研究

IF 0.5 Q4 PSYCHIATRY
Razieh Kamali-Ardekani, Alireza Tavakkoli Neishabouri, Mojtaba Rabiei, Mohammadreza Alizadeh, Ali Yoonessi, Lida Shafaghi, M. Hadjighassem
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引用次数: 0

摘要

背景:认知控制和由此产生的选择性注意的过程构成了神经认知功能连续体的共同根源。通过各种范式评估了对与任务无关的信息和不想要的属性的有效抑制。不同形式的Stroop任务可以为检测这种抑制和选择性注意的状态提供一个平台。与注意力控制相关的脑电图(EEG)信号的计算建模可以补充这一学科的研究。方法:在记录脑电图的同时,进行了96项三条件彩色单词Stroop任务的试验。所有受试者(9名参与者)都是右撇子(20-25岁),其中一半是男性。三个条件信号时期被重新定义为两个条件:(1)区分不一致时期(DIe),这是从其等效的一致时期中减去的不一致时期;(2)中性时期,其中提取刺激后150-300ms和350-500ms的间隔。然后对预处理的数据进行分析,并将整个EEG历元视为在不同条件下进行比较的变量。一种可接受的拟合支持向量机(SVM)算法对数据进行分类。结果:对于每个个体,在两个时间间隔(150-300和350-500毫秒)内对DIe和中性时期进行比较。SVM分类方法在个体内提供了可接受的精确度,在150-300毫秒的区间为59-65%,在350-500毫秒的区间则为65-70%。关于频域评估,这两个区间的Δ频带在两种条件之间没有显著差异。结论:支持向量机模型在事件相关历元晚期(350-500ms)分类中表现较好。因此,选择性注意相关特征在这个时间间隔中更为显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decoding Selective Attention and Cognitive Control Processing Through Stroop Interference Effect: An Event-Related Electroencephalography-Derived Study
Background: The process of cognitive control and resultant selective attention construct the shared root of a continuum of neurocognitive functions. Efficient inhibition of task-irrelevant information and unwanted attributes has been evaluated through various paradigms. Stroop tasks in different forms could provide a platform for detecting the state of this type of inhibition and selective attention. Computational modeling of electroencephalography (EEG) signals associated with attentional control could complement the investigations of this discipline. Methods: Ninety-six trials of a three-condition Color-Word Stroop task were performed while recording EEG. All subjects (9 participants) were right-handed (20 - 25 years), and half were male. Three-condition signal epochs were redefined as two conditions: (1) Differentiated incongruent epochs (DIe), which are incongruent epochs that their equivalent congruent epochs are subtracted from and (2) Neutral epochs, in which intervals of 150 - 300 ms and 350 - 500 ms post-stimulus were extracted. Preprocessed data were then analyzed, and the whole EEG epoch was considered the variable to be compared between conditions. An acceptably fitted support vector machine (SVM) algorithm classified the data. Results: For each individual, the comparison was made regarding DIe and neutral epochs for two intervals (150 - 300 and 350 - 500 ms). The SVM classification method provided acceptable accuracies at 59 - 65% for the 150 - 300 ms interval and 65 - 70% for the 350 - 500 ms interval within individuals. Regarding frequency domain assessments, the Delta frequency band for these two intervals showed no significant difference between the two conditions. Conclusions: The SVM models performed better for the late event-related epoch (350 - 500 ms) classification. Hence, selective attention-related features were more significant in this temporal interval.
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来源期刊
CiteScore
1.20
自引率
10.00%
发文量
70
期刊介绍: The Iranian Journal of Psychiatry and Behavioral Sciences (IJPBS) is an international quarterly peer-reviewed journal which is aimed at promoting communication among researchers worldwide and welcomes contributions from authors in all areas of psychiatry, psychology, and behavioral sciences. The journal publishes original contributions that have not previously been submitted for publication elsewhere. Manuscripts are received with the understanding that they are submitted solely to the IJPBS. Upon submission, they become the property of the Publisher and that the data in the manuscript have been reviewed by all authors, who agree to the analysis of the data and the conclusions reached in the manuscript. The Publisher reserves copyright and renewal on all published material and such material may not be reproduced without the written permission of the Publisher. Statements in articles are the responsibility of the authors.
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