## 3 Results
### Eye Movement Predictive Models for Reading Comprehension by Reading Conditions and Text Types  
First, within the context of fictional reading under the R condition, a predictive model concerning literal comprehension exhibited a statistically significant explanation of 21% of the variance (\(F(1,\,146)\) = 40.66, \(p\) < 0.001), with no inclusion of eye movement parameters. Only age significantly and positively influenced the variable (\(\beta\) = 0.47, \(t(146)\) = 6.39, \(p\) < 0.001). Regarding inferential comprehension, a predictive model significantly accounted for 37% of the variance (\(F(2,\,145)\) = 43.70, \(p\) < 0.001). The model encompassed age (\(\beta\) = 0.50, \(t(145)\) = 7.13, \(p\) < 0.001) and TFD (\(\beta\) = \(-\)0.22, \(t(145)\) = \(-\)3.21, \(p\) = 0.002) as predictors. Both factors were statistically significant, with age displaying a positive and TFD exerting a negative effect on the inferential comprehension scores. Second, under the R condition during non-fictional reading, a predictive model for literal comprehension significantly explained 37% of the variance (\(F(3,\,131)\) = 27.31, \(p\) < 0.001). In this model, age (\(\beta\) = 0.53, \(t(131)\) = 7.32, \(p\) < 0.001) and AFD (\(\beta\) = \(-\)0.19, \(t(131)\) = \(-\)2.63, \(p\) = 0.010) emerged as significant predictors, while scanpath length (\(\beta\) = 0.12, \(t(131)\) = 1.73, \(p\) = 0.086) did not reach statistical significance. Age and scanpath length demonstrated positive associations with literal comprehension, whereas AFD showed a negative impact  
Figure 3: Scanpath length in each reading condition when reading fictional and non-fictional text. *\(p\) < 0.05; **\(p\) < 0.01; ***\(p\) < 0.001.  
on the variable. A model predicting inferential comprehension accounted for 47% of the variance (\(F(2,132)=59.56\), \(p<0.001\)), wherein age and TFD appeared as significant factors. Age (\(\beta=0.61\), \(t(132)=9.50\), \(p<0.001\)) exhibited a positive influence on the inferential comprehension scores, while the impact of TFD (\(\beta=-0.21\), \(t(132)=-3.24\), \(p=0.002\)) on inferential comprehension was negative. Next, within the AR condition, while reading fictional text, a predictive model that includes age and TFD significantly accounted for 22% of the variance in literal comprehension (\(F(2,136)=20.96\), \(p<0.001\)). Both age (\(\beta=0.46\), \(t(136)=6.13\), \(p<0.001\)) and TFD (\(\beta=0.21\), \(t(136)=2.73\), \(p=0.007\)) had a significant positive impact on literal comprehension. For inferential comprehension, on the other hand, a predictive model yielded a significant explanation of 25% of the variance (\(F(3,135)=16.37\), \(p<0.001\)). The variables encompassed in the model were age (\(\beta=0.39\), \(t(135)=5.08\), \(p<0.001\)), scanpath length (\(\beta=0.15\), \(t(135)=1.81\), \(p=0.073\)), and AFD (\(\beta=-0.14\), \(t(135)=-1.76\), \(p=0.081\)). Age exhibited a significant positive influence on the inferential comprehension scores. Although scanpath length was also positively associated with inferential comprehension, this relationship did not reach statistical significance. AFD negatively affected inferential comprehension, but this effect did not attain statistical significance. Finally, considering literal comprehension under the AR condition while engaging with non-fictional text, a predictive model significantly explained 45% of the variance, with all three eye movement parameters involved (\(F(4,142)=30.75\), \(p<0.001\)). Age (\(\beta=0.55\), \(t(142)=7.62\), \(p<0.001\)) and TFD (\(\beta=0.14\), \(t(142)=2.06\), \(p=0.041\)) significantly and positively affected literal comprehension. Scanpath length (\(\beta=0.11\), \(t(142)=1.42\), \(p=0.159\)) also displayed a positive impact on literal comprehension, but it was not statistically significant. AFD (\(\beta=-0.13\), \(t(142)=-1.72\), \(p=0.088\)) had a non-significant negative impact on the literal comprehension scores.