身体知道更好:感官运动信号揭示了人脑中隐性和显式代理感之间的相互作用
Asaf Applebaum1, Ophir Netzer2, Yonatan Stern2
1Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.
Cognition
|October 25, 2024
概括
这项研究揭示了机器学习模型可以从手动中检测感觉运动冲突,超过主观的代理感 (SoA) 的判断. 这表明隐式处理机构信息比有意识的意识更准确.
科学领域:
- 认知神经科学 认知神经科学
- 发动机控制器的控制器
- 机器学习 机器学习
背景情况:
- 代理感 (SoA) 是主观的感觉控制自己的行为,受到隐含的感觉运动过程和明确的决策的影响.
- 目前测量SoA的方法经常将这些隐式和显式组件混为一谈,阻碍了对它们相互作用的理解.
- 隐含的感觉运动适应机制和明确的机构判断之间的关系仍然不清楚.
研究的目的:
- 调查隐含的感觉运动适应和明确的代理感 (SoA) 判断之间的关系.
- 为了确定是否可以从机器学习的手动力学推断传感运动冲突.
- 将基于机器学习的冲突检测的准确性与参与者的主观代理判断进行比较.
主要方法:
- 两个实验 (N=60) 使用虚拟手的范式,参与者执行运动.
- 在同步或暂时延迟的视觉反条件下收集了手部运动的动力学数据.
- 机器学习模型被用来从动力学数据中推断传感运动冲突.
主要成果:
- 传感运动冲突被准确地推断出从隐性运动动力学在试验逐试验的基础上.
- 机器学习模型在分类感官运动一致性方面取得了更高的准确性,而不是参与者的明确判断.
- 这些发现是强大的,并在第二次预先注册的实验中复制.
结论:
- 与代理感相关的隐式和显式处理有所不同.
- 大脑拥有关于感觉运动冲突的高保真信息.
- 意识代理推断可能无法充分利用所有可用的隐性感官运动冲突信息.
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