更改

[[文件:NIS+ boids.png|居左|700x700像素|鸟群中的因果涌现]]

[[文件:NIS+ boids.png|居左|700x700像素|鸟群中的因果涌现]]

The brain experiment uses fMRI data collected from two experiments involving 830 human subjects. In the first group, subjects performed a visual task by watching a short movie clip, during which recordings were made. In the second group of experiments, they were asked to be in a resting state and the recording was completed. To manage the high original dimension, the authors reduced the original 14000-dimensional data to 100 dimensions using the [[Schaefer atlas]] method, with each dimension corresponding to a brain region. After that, the authors learned these data through NIS+ and extracted the dynamics at six different macroscopic scales. Figure a shows the multi-step prediction error results at different scales. Figure b shows the comparison of EI of NIS and NIS+ methods on different macroscopic dimensions in the resting state and the visual task of watching movies. The authors found that in the visual task, causal emergence is most significant when the macroscopic state dimension is q = 1. Through attribution analysis, it is found that the visual area plays the largest role (Figure c), which is consistent with the real scene. Figure d shows different perspective views of brain region attribution. In the resting state, one macroscopic dimension is not enough to predict the microscopic time series data. The dimension with the largest causal emergence is between 3 and 7 dimensions.

The brain experiment uses fMRI data collected from two experiments involving 830 human subjects. In the first group, subjects performed a visual task by watching a short movie clip, during which recordings were made. In the second group of experiments, they were asked to be in a resting state and the recording was completed. Since the original dimension is relatively high, the authors reduced the original 14000-dimensional data to 100 dimensions using the [[Schaefer atlas]] method, with each dimension corresponding to a brain region. After that, the authors learned these data through NIS+ and extracted the dynamics at six different macroscopic scales. Figure a shows the multi-step prediction error results at different scales. Figure b shows the comparison of EI of NIS and NIS+ methods on different macroscopic dimensions in the resting state and the visual task of watching movies. The authors found that in the visual task, causal emergence is most significant when the macroscopic state dimension is q = 1. Through attribution analysis, it is found that the visual area plays the largest role (Figure c), which is consistent with the real scene. Figure d shows different perspective views of brain region attribution. In the resting state, one macroscopic dimension is not enough to predict the microscopic time series data. The dimension with the largest causal emergence is between 3 and 7 dimensions.

[[文件:NIS+ 脑数据.png|居左|700x700像素|脑神经系统中的因果涌现]]

[[文件:NIS+ 脑数据.png|居左|700x700像素|脑神经系统中的因果涌现]]