## 4 Results
### Correlation between the Human Brain and Models
#### 4.2.2 Ten Channels Located above the Frontal Lobe (Frontal)  
A total of 10 previously selected 28 EEG channels are located above the frontal lobe. The descriptive statistical results of the RSA scores for the brain and model based on these 10 EEG channels are presented in Table 10. Figure 9 depicts the distribution of representational similarity between human brain activity and language models at three different TOIs for various models and fact word extraction methods using boxplots. The rhombus symbols represent outliers, indicating data points that differ significantly from other observations. The error bars represent the 95% _CI_. At the 0-1500 ms TOI, the highest RSA score of 0.00435 was observed between the human brain and the GloVE model, using the TF-IDF method for fact word extraction. At the 250-500 ms TOI, the highest RSA score reached 0.00361 for the human brain and the GloVE model, achieved with the Dependency fact word extraction method. For the 500-1000 ms TOI, employing the Entity method for fact word extraction yielded the highest RSA score of 0.00401 with the GloVe model. Figure 8: Distribution of RSA scores across all channels. The results for the main and interaction effects from the repeated measures ANOVA are presented in Table 6. For the 10 channels above the frontal lobe, the main effect results were as follows: For the 3 models, the sphericity assumption was not met (\(p\) = 0.022). After applying the Greenhouse-Geisser correction, the result was \(F(1.36)\) = 5.301, \(p\) = 0.025, indicating a significant main effect of the selected three model categories on the RSA scores. The sphericity assumption was met for the four extraction methods (\(p\) = 0.865), resulting in \(F(3)\) = 0.851, \(p\) = 0.475, indicating that the main effect of the selected fact-word extraction methods on the RSA scores was not significant. For the TOI, the sphericity assumption was met (\(p\) = 0.647), resulting in \(F(2)\) = 3.706, \(p\) = 0.038, indicating a significant main effect of the selected TOIs on the RSA scores. Interaction results were as follows: For the interaction between models and extraction methods, the sphericity assumption was met (\(p\) = 0.073) with \(F(6)\) = 2.139, \(p\) = 0.058, indicating no significant interaction. For the interaction between models and TOI, the sphericity assumption was not met (\(p\) = 0.039), and after applying the Greenhouse-Geisser correction, the result was \(F(2.418)\) = 0.651, \(p\) = 0.556, indicating no significant interaction. The sphericity assumption was met for the interaction between the extraction method and TOI (\(p\) = 0.507), resulting in \(F(6)\) = 1.519, \(p\) = 0.183, indicating no significant interaction. The sphericity assumption was not met for the interaction among the three factors (\(p\) < 0.001). After applying the Greenhouse-Geisser correction, the result was \(F(5.054)\) = 1.081, \(p\) = 0.379, indicating no significant interaction. Given the significant main effects of the three models and three TOIs on the RSA scores, post-hoc tests were conducted on these two factors. The pairwise comparison results after the Bonferroni correction were presented in Tables 7 and 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model 1**} & \multirow{2}{*}{**Model 2**} & \multirow{2}{*}{**Difference of \(M\) (1–2)**} & \multirow{2}{*}{\(p\)**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{5-6}  & & & & **Lower** & **Upper** \\ \hline \multirow{2}{*}{GloVe} & BERT & 0.001 & 0.050 & \(-\)1.23\(\times 10^{-6}\) & 0.002 \\  & GPT-2 & \(<\)0.001 & 0.384 & 0 & 0.001 \\ \hline \multirow{2}{*}{BERT} & GloVe & \(-\)0.001 & 0.050 & \(-\)0.002 & 1.23 \(\times 10^{-6}\) \\  & GPT-2 & \(-\)0.001 & 0.226 & \(-\)0.002 & 0 \\ \hline \multirow{2}{*}{GPT-2} & BERT & 0.001 & 0.226 & 0 & 0.002 \\  & GloVe & \(<\)0.001 & 0.384 & \(-\)0.001 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Post-hoc test for the main effects of the three models on RSA scores (frontal).