## 2 Materials and Methods
### Results  
The 'pwr package' in R was used for a power analysis to make sure that the sample size obtained sufficient power ([PERSON], 1992; [PERSON], 1992). Given that every participant underwent six rounds of testing and study, we assumed a large effect size (i.e., \(\eta_{\text{p}}{}^{2}=0.14\)([PERSON], 1992)). The achieved power was 0.992, with \(\alpha=0.05\). With subjects and items acting as random intercept effects, we used logistic mixed effects regressions to analyze accuracy data and linear mixed effects regressions to analyze reaction times ([PERSON], 1992; [PERSON], 1992). For the RT analysis, we only took into consideration trials that had accurate responses, excluding cases with RTs more than three median absolute deviations above or below the median RT calculated individually for each subject condition (7.5%). The results are shown in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Effects** & **AAIC** & **LLR \(\chi^{2}\) (1)** & \(p\) Value** \\ \hline \multirow{6}{*}{Proportion correct} & round & \(-36.4\) & \(38.404\) & \(<\)0.001 \\  & order condition & \(-12.7\) & \(14.671\) & \(<\)0.001 \\ \cline{1-1}  & complex condition & \(-7.6\) & \(9.624\) & \(<\)0.05 \\ \cline{1-1}  & four types & \(162.4\) & \(168.379\) & \(<\)0.001 \\ \cline{1-1}  & complexity \({}^{*}\) order & \(-11\) & \(13.027\) & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of logistic mixed-effects regression analysis on accuracy. Figure 4: Example round consisting of a study and test phase for each of the four stimulus types. There were six rounds total of the four types of stimulus lists. Figure 5 shows the performance on the cued-recall tests as a function of four stimulus types of cues for each round. Regardless of stimulus types, subjects performed better in later rounds, becoming more accurate, \(\Delta\text{AIC}=-36.4\), LLR \(\chi^{2}\) (1) = 38.404, \(p<0.001\); and faster, \(\Delta\text{AIC}=-225.2\), LLR \(\chi^{2}\) (1) = 265.19, \(p<0.001\). Because we are not interested in the role of practice of the pairs (round), further analyses did not include the round factor and focused on the interaction between complexity and order. There was a significant interaction between complexity and order on accuracy, \(\Delta\text{AIC}=-12.7\), LLR \(\chi^{2}\) (1) = 14.671, \(p<0.001\); and response time, \(\Delta\text{AIC}=-106.9\), LLR \(\chi^{2}\) (1) = 108.89, \(p<0.001\). Consistent with prior studies' results, subjects showed worse performance in the complex condition than in the simple condition; subjects became less accurate, \(\Delta\text{AIC}=-7.6\), LLR \(\chi^{2}\) (1) = 9.624, \(p=0.002\); and slower, \(\Delta\text{AIC}=-1463.4\), LLR \(\chi^{2}\)(1) = 1465.34, \(p<0.001\). Specially, that performance for subjects differed significantly on the type of bi-symbol pair combinations, with subjects recalling the words to complex and disordered bi-symbol pairs (type) less accurately, \(\Delta\text{AIC}=162.4\), LLR \(\chi^{2}\) (1) = 168.379, \(p<0.001\); and more slowly, \(\Delta\text{AIC}=-1580.8\), LLR \(\chi^{2}\) (1) = 1584.76, \(p<0.001\). Importantly, subjects performed more accurately in the ordered condition than in the disordered condition in all cases, \(\Delta\text{AIC}=-11\), LLR \(\chi^{2}\) (1) = 13.027, \(p<0.001\); and faster, \(\Delta\text{AIC}=-248.1\), LLR \(\chi^{2}\) (1) = 250.11, \(p<0.001\), such that visual order did affect subjects' memory processing. Figure 6 shows the cued-recall performance as a function of complexity (x-axis) and order (separate lines for the two levels).