Ramdani, E. Sagui, B. Schmid, O. Castagna, K. Davranche, F. Vidal, T. Hasbroucq
{"title":"Action monitoring fails when motor execution is too fast: no time for correction","authors":"Ramdani, E. Sagui, B. Schmid, O. Castagna, K. Davranche, F. Vidal, T. Hasbroucq","doi":"10.31234/osf.io/5fd49","DOIUrl":null,"url":null,"abstract":"A recent information processing model of two-choice RT situations (Servant et al., 2015), suggests that conditions which reduce the duration of peripheral motor processes, should also reduce the efficiency of the action monitoring system, because letting no time enough for correction of partial errors (i.e. subthreshold transient muscle activity of the agonists of the incorrect response preceding the correct response). A physiological situation, namely sustained physical exercise, has repeatedly been reported to reduce the duration of response execution. Therefore, in order to test the prediction of the model, we compared action monitoring efficiency between a sustained exercise (59.42% of MAP) and a control (15 W) condition in the same subjects while they were performing a Simon task. Electromyographic (EMG) recordings of muscles implicated in the response allowed to measure premotor time (time interval between the stimulus and the onset of the EMG burst) and motor time (MT, time interval between the onset of the EMG burst and the mechanical response, which gives access to response execution processes). Electromyogram further permitted to unmask partial errors. Correction ratio was calculated by dividing the number of partial errors by the number of incorrect activations (partial errors + errors). As expected, exercise decreased MT. In addition, exercise reduced the correction ratio. Furthermore, there was a positive inter-subject correlation between these two dependent variables. In line with Servant et al.'s model (2015), we propose that the drop in the efficiency of cognitive control was due to insufficient MT available for action monitoring to operate when incorrect activations were produced.","PeriodicalId":87318,"journal":{"name":"Journal of systems and integrative neuroscience","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of systems and integrative neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31234/osf.io/5fd49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A recent information processing model of two-choice RT situations (Servant et al., 2015), suggests that conditions which reduce the duration of peripheral motor processes, should also reduce the efficiency of the action monitoring system, because letting no time enough for correction of partial errors (i.e. subthreshold transient muscle activity of the agonists of the incorrect response preceding the correct response). A physiological situation, namely sustained physical exercise, has repeatedly been reported to reduce the duration of response execution. Therefore, in order to test the prediction of the model, we compared action monitoring efficiency between a sustained exercise (59.42% of MAP) and a control (15 W) condition in the same subjects while they were performing a Simon task. Electromyographic (EMG) recordings of muscles implicated in the response allowed to measure premotor time (time interval between the stimulus and the onset of the EMG burst) and motor time (MT, time interval between the onset of the EMG burst and the mechanical response, which gives access to response execution processes). Electromyogram further permitted to unmask partial errors. Correction ratio was calculated by dividing the number of partial errors by the number of incorrect activations (partial errors + errors). As expected, exercise decreased MT. In addition, exercise reduced the correction ratio. Furthermore, there was a positive inter-subject correlation between these two dependent variables. In line with Servant et al.'s model (2015), we propose that the drop in the efficiency of cognitive control was due to insufficient MT available for action monitoring to operate when incorrect activations were produced.
最近一项关于两种选择RT情境的信息处理模型(Servant et al., 2015)表明,减少外周运动过程持续时间的条件,也应该降低动作监测系统的效率,因为没有足够的时间来纠正部分错误(即在正确反应之前,错误反应的激动剂的阈下短暂肌肉活动)。一种生理状况,即持续的体育锻炼,已经多次被报道可以减少反应执行的持续时间。因此,为了验证模型的预测,我们比较了同一受试者在执行Simon任务时持续运动(59.42%的MAP)和对照(15 W)条件下的动作监测效率。肌电图(EMG)记录与反应有关的肌肉,可以测量运动前时间(刺激和肌电爆发之间的时间间隔)和运动时间(MT,肌电爆发和机械反应之间的时间间隔,可以获得反应执行过程)。肌电图进一步揭示了部分错误。修正率的计算方法是部分错误数除以错误激活数(部分错误数+错误数)。正如预期的那样,运动降低了MT。此外,运动降低了校正率。此外,这两个因变量之间存在正的主体间相关。根据Servant等人的模型(2015),我们提出认知控制效率的下降是由于在产生错误激活时,可用于动作监控的MT不足。