## 4 Results and Discussions
### Building a Generalized Linear Model to Assess the Difficulty of E-C and C-E Translation Tasks  
To analyze average pupil dilation (AVGpupilidilation) in English-to-Chinese (E-C) translation, we employed Generalized Linear Models (GLMs) to examine the effects of AU_type (attention unit type: ST, TT, parallel [reference]), GazeEventDuration (fixation duration), and participant (individual differences, with P01 as the reference). Given that pupil dilation was a continuous, positive-value response variable, we compared three different GLM families to identify the best-fitting model:  
1. Gaussian GLM (normal distribution): assumes that pupil dilation is normally distributed with constant variance. 2. Gamma GLM (Log-link Gamma distribution): accounts for the skewness of physiological measures like pupil dilation, which is strictly positive. 3. Inverse Gaussian GLM: models highly skewed data where variance increases quadratically with the mean. To assess whether the impact of gaze duration varies across AU types, we included an interaction term (AU_type \(\times\) GazeEventDuration). Model selection was based on the Akaike Information Criterion (AIC), which balances model fit and complexity. The results indicate that the Gaussian GLM achieved the lowest AIC (-11048), followed by the Gamma GLM (-11033) and Inverse Gaussian GLM (-10876). Given the comparable AIC values, we prioritize the use of the Gaussian model for interpretation due to its intuitive linear relationship between predictors and pupil dilation. The results, as presented in Table 4, suggest that translation processing type significantly impacts the cognitive load in English-to-Chinese translation. Specifically, ST processing leads to significantly lower pupil dilation than parallel processing (\(p\) < 0.001), while TT processing also reduces pupil dilation, though to a lesser extent (\(p\) = 0.033). These findings align with research indicating that text comprehension (ST) requires less cognitive effort than multitasking (parallel), whereas text production (TT) involves similar or slightly lower cognitive demands. Furthermore, gaze event duration influences cognitive load differently across processing conditions. Overall, longer fixations are associated with  
Figure 9: A timeline of attention units for the first 75 events for Participant 13: English–Chinese Translation. Parallel is displayed in Green, Source Text (ST) in Light Blue, and Target Text (TT) in Red. Each AU is labeled. a slight decrease in pupil dilation (\(p\) = 0.026), potentially indicating adaptation or cognitive efficiency. However, in TT processing, longer fixations significantly increase pupil dilation (\(p\) = 0.0026), suggesting that sustained fixations during text production impose higher cognitive demands. This finding is consistent with prior research showing that fixations during writing require prolonged cognitive processing, leading to increased cognitive loads. In contrast, gaze duration does not significantly affect pupil dilation in ST processing (\(p\) = 0.265), suggesting that cognitive effort remains stable regardless of fixation duration during text comprehension. Additionally, participant differences were highly significant across all models (\(p\) < 0.001), reflecting individual variability in cognitive processing strategies. Some participants, such as P10, exhibited higher pupil dilation, suggesting increased cognitive effort, whereas others, such as P18 and P12, showed reduced pupil dilation, indicating differences in cognitive load management. However, given the complexity of these variations and their secondary relevance to the study's primary objectives, we omit a detailed discussion of participant differences. To analyze the average pupil dilation (AVGpupildilation) in Chinese-to-English (C-E) translation, we employed a Generalized Linear Model (GLM) as displayed above, but without an interaction term between AU_type and GazeEventDuration.