## 3 Materials and Methods
### Metrics Selection and Data Analysis
#### 3.5.2 Analysis of Correlation between Human Brain and Models  
This study employed RSA to assess the similarity between model and human brain activity. The core concept of RSA is that when representational systems (e.g., neural network models or the human brain) receive many inputs, they measure the activity patterns generated by each input. By calculating the response similarity for each possible pair of inputs, one can construct a representational similarity matrix, which encapsulates the internal structure of the representational system. In this research, with 1220 tokens in the text, the analysis of the representational similarity between the model and human brain activity was divided into 3 steps, as depicted in Figure 3.  
The first step was to compute the representational similarity matrix for the deep language model. Initially, the cosine similarity between the word vectors of the 1220 tokens in the article was calculated, as described in Equation (6), where \(v(t_{i})\) represented the word vector of the i-th token. These similarities were arranged into a matrix, with the calculation process illustrated in Figure 3a-c. This similarity matrix's upper and lower triangles were mirror images of each other, with its diagonal representing the similarity of a word to itself. \[\textit{Cosine Similarity}(t_{i},t_{j})=\frac{v(t_{i})\cdot v(t_{j})}{\|v(t_{i })\|_{2}\times\|v(t_{j})\|_{2}} \tag{6}\]  
The second step involves computing the representational similarity matrix for the EEG signals. Similarly, the Pearson correlation coefficients between the EEG vectors corresponding to the 1220 tokens in the article are calculated. In Equation (7), \(e(t_{i,k})\) represented the EEG signal of the i-th token at the k-th sampling point, and \(\overline{e}(t_{i})\) denoted the average EEG signal of the i-th token, averaged across 375 sampling points. These similarities were then arranged into a matrix in the same order as in the first step, with the calculation process illustrated in Figure 3d-f.  
\[\textit{Pearson's corr}(e_{i},e_{j})=\frac{\sum_{k=1}^{375}(e(t_{i,k})-\overline{e}( t_{i}))(e(t_{j,k})-\overline{e}(t_{j}))}{\sqrt{\sum_{k=1}^{375}(e(t_{i,k})- \overline{e}(t_{i}))^{2}}\sqrt{\sum_{k=1}^{375}(e(t_{j,k})-\overline{e}(t_{j} ))^{2}}} \tag{7}\]  
Figure 3: Steps for calculating representational similarity. (**a**) Compute the cosine similarity of word vectors from deep language models. (**b**) Arrange the cosine similarities of the word vectors into a matrix. (**c**) An example of the computed word vector similarity matrix. The deeper the blue, the lower the similarity; the deeper the red, the higher the similarity. (**d**) Compute the Pearson correlation coefficient for EEG vectors. (**e**) Arrange the Pearson correlation coefficients of the EEG vectors into a matrix. (**f**) An example of the computed EEG vector similarity matrix. Compare it with the corresponding word vector similarity matrix using the Spearman rank correlation coefficient. The third step involved computing the Spearman rank correlation coefficient between the two matrices, as described in Equation (8), where \(d_{i}^{2}\) represents the squared difference in ranks for each element between the two matrices, and n is the number of elements in the matrix. This correlation coefficient reflects the representational similarity between the model and human brain activities [30]. \[\textit{Spearman Rank Correlation}=1-\frac{6\sum d_{i}^{2}}{n(n^{2}-1)} \tag{8}\]  
In this study, we selected \"GloVE\" [36], \"BERT\" [37], and \"GPT-2\" [38] models to compare with human brain activity related to language comprehension. \"GloVE\" is a word embedding method based on global word frequency statistics and is considered a relatively traditional model. Both \"BERT\" and \"GPT-2\" are models based on the Transformer architecture, and they excel in handling long-distance dependencies and capturing complex structures within sentences.