## 4 Results and Discussions
### Retrospective Self-Reflections  
In this study, participants' introspective accounts were gathered using cue-based Retrospective Think-Aloud (RTA) protocols. They were asked to articulate how they distributed cognitive resources between source text (ST) and target text (TT) processing in both translation directions--that is, to compare the effort invested in ST versus TT during English-to-Chinese (E-C) and Chinese-to-English (C-E) tasks. Comparisons between these subjective reflections and the objective data were conducted at a macro level. Participants' self-reflections are summarized below in Tables 6 and 7: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Allocation of Cognitive Resources to ST and TT**} & \multicolumn{3}{c}{**Allocation of Cognitive Resources to ST and TT**} \\ \cline{2-7}  & **S1–3** & **S4–6** & **S7–9** & **S1–3** & **S4–6** & **S7–9** \\ \hline P01 & 60/40 & 50/50 & 50/50 & P12 & 60/40 & 70/30 & 80/20 \\ \hline P02 & 40/60 & 40/60 & 40/60 & P13 & 60/40 & 60/40 & 65/35 \\ \hline P03 & 50/50 & 60/40 & 60/40 & P14 & 50/50 & 60/40 & 60/40 \\ \hline P04 & 50/50 & 50/50 & 30/70 & P15 & 50/50 & 50/50 & 50/50 \\ \hline P05 & 30/70 & 40/60 & 50/50 & P16 & 50/50 & 50/50 & 50/50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Self-reflection: E-C translation. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Predictor** & **Estimate** & _p-Value_ & **Interpretation** \\ \hline Intercept & 3.092 & \textless{}0.001 *** & Baseline pupil dilation (parallel processing, reference participant). \\ \hline AU\_typeST & \(-\)0.0077 & \textless{}0.285 *** & Not significant: ST processing does not significantly differ from parallel processing. \\ \hline AU\_typeTT & \(-\)0.0475 & \textless{}0.001 *** & TT processing significantly reduces pupil dilation compared to parallel processing. \\ \hline GazeEventDuration & 0.0000076 & \textless{}0.001 *** & Longer gaze durations are significantly associated with increased pupil dilation. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Interpretation of the Gaussian model for Chinese-to-English translation, with a corresponding estimate and _p_-value with Signif. codes: *** 0.001. In the case of C-E translation, participants' retrospective reflections obtained, through RTA, closely aligned with the eye-tracking and keystroke logging results. Most participants recognized that producing content in their second language required more time overall, a greater number of attention units (AUs), and longer durations per AU. However, when reflecting on their E-C translation processes, many participants believed that they devoted more cognitive effort to understanding the English source text than to producing the Chinese target text. Of the 22 participants, only 4 (P02, P04, P05, and P18) reported allocating greater cognitive resources to TT production than to ST comprehension at any point. A small number (P11, P15, P16, and P22) perceived their efforts to be evenly divided between the two processing types. The majority, however, appeared unaware of the dominant cognitive load associated with TT production. Even among those who acknowledged investing more effort in TT production, their estimates were significantly lower than what the empirical data suggested -- typically ranging between 30% and 40% of total cognitive effort. For the majority of participants, the main difficulty encountered during L2-to-L1 translation stemmed from comprehending the English source text. Their strong confidence in producing text in their native language -- rooted in familiarity and habitual use -- appears to be a key factor contributing to the notable gap between their subjective assessments and the objective findings obtained through eye-tracking and keystroke logging. As participant P19 observed, translating from Chinese into English requires careful consideration of linguistic and cultural appropriateness. In contrast, during English-to-Chinese tasks,