## 4 Results
### Correlation between the Human Brain and Models
#### 4.2.3 Ten Channels Located above the Central Region (Central)  
A total of 10 previously selected 28 EEG channels were located above the central region. The descriptive statistics for the RSA scores of the brain and the model on these 10 EEG channels were shown in Table 10. Figure 10 depicted 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 represented the 95% _CI_. At the 0-1500 ms TOI, the highest RSA score of 0.00340 was observed between the human brain and the BERT model, using the Pos method for fact word extraction. At the 250-500 ms TOI, the highest RSA score reached 0.00346 for the human brain and the GPT-2 model, achieved with the Pos fact word extraction method. For the 500-1000 ms TOI, employing the TF-IDF method for fact word extraction yielded the highest RSA score of 0.00318 with the GloVe model. The results for the main and interaction effects from the repeated measures ANOVA were presented in Table 11. For the 10 channels located above the central region, the results for the main effects analysis were as follows: For the 3 models, the sphericity assumption is violated. The results for the main effects of the three TOIs on RSA scores (from the left) are shown in Table 11.  
Figure 10: Distribution of RSA scores for channels located above the central region. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**TOI 1**} & \multirow{2}{*}{**TOI 2**} & \multirow{2}{*}{**Difference of \(M\) (1–2)**} & \multirow{2}{*}{\(p\)**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{5-6}  & & & & **Lower** & **Upper** \\ \hline \multirow{2}{*}{Overall *} & N400 * & \(-\)0.001 & 0.041 & \(-\)0.002 & \(-\)3.00 \(\times 10^{-5}\) \\  & P600 & \textless{}0.001 & 0.772 & \(-\)0.001 & 0.001 \\ \hline \multirow{2}{*}{N400 *} & Overall * & 0.001 & 0.041 & 3.00\(\times 10^{-5}\) & 0.002 \\  & P600 & \textless{}0.001 & 0.448 & 0 & 0.001 \\ \hline \multirow{2}{*}{P600} & N400 & \textless{}0.001 & 0.448 & \(-\)0.001 & 0 \\  & Overall & \textless{}0.001 & 0.772 & \(-\)0.001 & 0.001 \\ \hline \hline \end{tabular}  
* Overall: 0–1500 ms; N400: 250–500 ms; P600: 500–1000 ms. TOIs marked with the symbol \({}^{\prime\prime\prime}\) indicate a significant difference between them.  
\end{table} 
Table 8: Post-hoc test for the main effects of the three TOIs on RSA scores (fromtal). tion was met (\(p\) = 0.321), resulting in an ANOVA outcome of \(F(2)\) = 0.048, \(p\) = 0.953, indicating that the main effect of the chosen model categories on RSA scores was not significant. For the four extraction methods, the sphericity assumption was not met (\(p\) = 0.014), so the Greenhouse-Geisser correction was applied, resulting in a corrected ANOVA of \(F(1.709)\) = 0.166, \(p\) = 0.816, indicating that the main effect of the chosen fact word extraction methods on RSA scores was not significant. For the TOIs, the sphericity assumption was not met (\(p\) = 0.031), leading to a corrected ANOVA result of \(F(1.389)\) = 4.140, \(p\) = 0.046, suggesting a significant main effect of the chosen TOIs on RSA scores. The interaction between the model and extraction method met the sphericity assumption (\(p\) = 0.073), with an ANOVA result of \(F(6)\) = 0.68, \(p\) = 0.773, indicating no significant interaction. For the interaction between the model and TOI, the sphericity assumption was not met (\(p\) = 0.003), so the Greenhouse-Geisser correction was applied, yielding a corrected ANOVA of \(F(1.948)\) = 1.223, \(p\) = 0.310, indicating no significant interaction. The sphericity assumption was violated for the interaction between the extraction method and TOI (\(p\) = 0.005), leading to a corrected ANOVA result of \(F(3.588)\) = 0.395, \(p\) = 0.791.