在复杂的行动目录和其神经表征中更新预测
Rosari Naveena Selvan1, Minghao Cheng2, Sophie Siestrup3
1Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany; Department for Computational Neuroscience, Third Institute of Physics - Biophysics, University of Göttingen, Göttingen, Germany.
NeuroImage
|June 13, 2024
概括
观察复杂的动作需要将它们分成部分. 在动作边界的脑活动,特别是在预测违规时,揭示了我们如何更新这些序列的内部模型.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 发动机控制器的控制器
背景情况:
- 人类行为被认为是连续的,但是在离散的段落中处理的.
- 行动边界对于预测处理和更新内部模型至关重要.
- 了解行动细分的神经机制是认知神经科学的关键.
研究的目的:
- 在复杂,概率结构化的动作序列的边界调查神经过程.
- 在预测违规和成功的动作序列检索期间检查大脑活动.
- 识别参与预测即将发生的行动和检索行动目录的大脑区域.
主要方法:
- 使用功能磁共振成像 (fMRI) 来研究行动观察期间的大脑活动.
- 采用复杂的动作序列与预定义的概率结构,由触摸 (T) 和释放 (U) 事件定义.
- 利用计算机视觉来检测动作边界,并呈现修改后的序列来违反预测.
主要成果:
- 违反行动预测会增加行动观测网络和前部岛屿的激活.
- 动作释放 (U) 事件显示了补充运动区域,条纹体和舌环的激活,表明了剧本检索.
- 行动触摸 (T) 事件招募了中线和海马区域,反映了对下一个行动段的预测.
结论:
- 在动作边界的神经活动支持复杂的动作序列的预测处理.
- 大脑区域,如补充运动区,条纹体,胰岛和海马,在动作细分和预测中发挥着不同的作用.
- 这些发现增强了对大脑过程的理解,这些过程是观察到复杂动作的基础预测.
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