## 4 Results
### Correlation between the Human Brain and Models
#### 4.2.5 A Brief Summary  
Our study conducted a detailed analysis of representational similarity across four AOIs: Overall, Frontal, Central, and Parietal. Each AOI corresponded to a specific set of EEG channels. We evaluated the similarity in brain and model reading activities under varying conditions, encompassing four extraction methods, three models, and three TOIs, as detailed in Table 12. Cells in the table marked with an asterisk (*) signify significant main or interaction effects from the repeated measures ANOVA, with 'ns' indicating no significant effects.  
Our findings demonstrated no significant interaction effects among extraction methods, model selection, and TOIs across all AOIs, suggesting that each factor independently influences RSA scores. This implied that each variable, such as extraction method, model selection, or TOI, uniquely contributed to the correlation between human brain activity and the models.  
Regarding TOIs, different intervals corresponded to distinct brain linguistic processing stages. For example, the N400 was typically linked with semantic violations or unexpected words, with the 250-500 ms TOI often used to explore this time window. This suggested that this particular TOI encompassed the semantic interpretation process. Results from channels above the Frontal lobe indicated higher RSA scores during the TOI, including the N400, compared to the overall duration. This suggested that the models captured semantic processing-related information to some extent, showing heightened sensitivity to semantic data over other types, like syntax or background knowledge.  
\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 & 0.072 & \(-\)0.001 & 5.22\(\times\)10\({}^{-5}\) \\  & P600 & \(-\)0.001 & 0.191 & \(-\)0.002 & 0 \\ \hline \multirow{2}{*}{N400 *} & Overall * & 0.001 & 0.072 & \(-\)5.22 \(\times\)10\({}^{-5}\) & 0.001 \\  & P600 & \(<\)0.001 & 1 & \(-\)0.001 & 0.001 \\ \hline \multirow{2}{*}{P600} & N400 & \(<\)0.001 & 1 & \(-\)0.001 & 0.001 \\  & Overall & 0.001 & 0.191 & 0 & 0.002 \\ \hline \hline \end{tabular}  
* Overall: 0–1500 ms; N400: 250-500 ms; P600: 500-1000 ms. TOIs marked with the symbol *\({}^{\infty}\) indicate a significant difference between them.  
\end{table}
Table 11: Post-hoc test for the main effects of the three TOIs on RSA scores (parietal).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Within-Subjects Effect** & **Overall (28)** & **Frontal (10)** & **Central (10)** & **Parietal (8)** \\ \hline Model & ns & * & ns & GPT-2 \(>\) BERT ** \\ Feature & ns & ns & ns & ns \\ TOI * & ns & \(TOI_{2}>TOI_{1}\) * & * & * \\ Model \(\times\) Feature & ns & ns & ns & ns \\ Model \(\times\) TOI & ns & ns & ns & ns \\ Feature \(\times\) TOI & ns & ns & ns & ns \\ Model \(\times\) Feature \(\times\) TOI & ns & ns & ns & ns \\ \hline \hline \end{tabular}  
* As in the table, the model selection is based on the model selection.  
Regarding the channels above the Parietal lobe, RSA scores for GPT-2 were notably higher than those for BERT. This could imply that GPT-2's processing strategies resonated more with the linguistic processing patterns of this specific brain region. From a model perspective, unlike BERT's bidirectional masked language model approach, GPT-2's unidirectional autoregressive model might have more closely mirrored participants' sequential text reading pattern. They could predict the next word in a text but could not see it in advance, akin to GPT-2's processing style.