## 4 Results
### Correlation between the Human Brain and Models
#### 4.2.1 All 28 Channels (Overall)  
The descriptive statistical results of the RSA scores for the human brain and model across all channels were presented in Table 11. Figure 8 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 represent the 95% confidence interval (_CI_). At the 0-1500 ms TOI, the highest RSA score of 0.00302 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 0.00283 for the human brain and the GloVE 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 0.00309 with the BERT model.  
Before conducting a repeated measure ANOVA on the RSA scores of the 28 channels, we first performed Mauchly's Sphericity Test. We reported the results of the sphericity test for the main effects and interactions that met the assumption of sphericity. For effects that violated this assumption, we applied the Greenhouse-Geisser correction. On these 28 channels, the main effects of 'Model', 'Feature', and 'TOI', as well as the interaction effect between 'Model' and 'Feature', met the assumption of sphericity. Thus, we conducted a repeated measures ANOVA using the original degrees of freedom. For other three-way interactions that did not meet the assumption, we used the Greenhouse-Geisser correction. The results of the main and interaction effects are shown in Table 15.  
For the 28 channels, the main effect results were as follows: For the 3 models, the sphericity assumption was met (\(p\) = 0.782), with an ANOVA result of \(F(2)\) = 0.334, \(p\) = 0.719, 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 met (\(p\) = 0.98) with an ANOVA result of \(F(3)\) = 0.701, \(p\) = 0.557, suggesting that the main effect of the chosen fact word extraction methods on RSA scores was not significant. For the TOIs, the sphericity assumption was met (\(p\) = 0.155) with an ANOVA result of \(F(2)\) = 0.334, \(p\) = 0.719, indicating that the main effect of the chosen periods of interest on RSA scores was not significant.  
The results for the interactions were as follows: For the interaction between the model and extraction method, the sphericity assumption was met (\(p\) = 0.970) with an ANOVA result of \(F(6)\) = 0.68, \(p\) = 0.666, suggesting that the interaction between the two factors was not significant. For the interaction between the models and TOIs, the sphericity assumption was not met (\(p\) = 0.046), so the Greenhouse-Geisser correction was applied, resulting in \(F(2.338)\) = 0.224, \(p\) = 0.833, indicating a non-significant interaction. For the interaction between the extraction method and TOI, the sphericity assumption was not met (\(p\) = 0.044), so the Greenhouse-Geisser correction was applied, resulting in \(F(3.112)\) = 0.353, \(p\) = 0.794, indicating a non-significant interaction. For the three-way interaction, the sphericity assumption was not met (\(p\) < 0.001). After applying the Greenhouse-Geisser correction, the result was \(F(4.336)\) = 2.253, \(p\) = 0.070, suggesting that the interaction among the three factors was insignificant.