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Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability

机译:脑信号变异性差异影响认知灵活性和认知稳定性。

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摘要

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories.>SIGNIFICANCE STATEMENT Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential effects of variability for different behavioral situations requiring either cognitive flexibility or stability. However, this hypothesis has so far not been put to an empirical test. In this study, we assessed cognitive flexibility and cognitive stability, and, besides a generally positive effect of neural variability on accuracy measures, we show that neural variability in a prefrontal brain area at the inferior frontal junction is differentially associated with performance: higher levels of variability are beneficial for the effectiveness of task switching (cognitive flexibility) but detrimental for the efficiency of distractor inhibition (cognitive stability).
机译:最近的研究得出了一个引人入胜的结论,即在健康的成年人中,神经元过程中较高的变异性对于认知功能是有益的。从工作记忆的神经计算理论也可以推断出神经元加工过程中的可变性的有益影响,尽管这仅适用于需要认知灵活性的任务。但是,认知稳定性,即面对无关紧要的干扰物维持任务目标的能力,在脑信号变异性较高的情况下会受到影响。为了直接检验这一预测,我们在健康人的样本中研究了认知灵活性(即任务切换)和认知稳定性(即干扰物抑制)过程中的行为和脑信号变异性,并开发了有效且易于实施的分析事件相关的fMRI任务范例中评估BOLD信号变异性的方法。结果表明,神经准确性对任务绩效具有总体积极影响,如准确性测量所评估。但是,左下额叶连接区中较高级别的BOLD信号可变性会导致任务切换期间的错误率成本降低,从而有助于认知灵活性。相反,同一区域的可变性会对认知稳定性产生不利影响,如分心抑制过程中可变性对响应时间成本的负面影响所示。这种模式在行为层面上得到了反映,较高的行为变异性预示着更好的任务切换,但干扰分散抑制性能更差。我们的数据通过显示取决于任务上下文的脑信号可变性的不同影响,从而扩展了先前关于脑信号可变性的结果,与计算理论的预测相符。>意义声明最近的神经科学研究表明,人脑信号是本质上可变的,建议这种可变性可以提高性能。前额神经网络的计算模型预测了需要认知灵活性或稳定性的不同行为情况下变异性的不同影响。但是,到目前为止,尚未对该假设进行实证检验。在这项研究中,我们评估了认知灵活性和认知稳定性,并且除了神经变异性对准确性测评的总体积极影响外,我们还显示了额叶下额交界处前额脑区域的神经变异性与表现有相关性:可变性对于任务切换的有效性(认知灵活性)是有益的,但是对分心抑制的效率(认知稳定性)是有害的。

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