## 5 Discussion and Conclusions  
Two main issues were studied in this experiment. The first was related to how global and local features influence eye-movement patterns under similarity perception, and the second was related to the relationship between similarity choice and eye-movement patterns. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**FTCs**} & \multicolumn{2}{c}{**Scene 1**} & \multicolumn{2}{c}{**Scene 2**} & \multicolumn{2}{c}{**Scene 3**} & \multicolumn{2}{c}{**Scene 4**} \\ \cline{2-9}  & **FTCs\_S,A** & **FTCs\_S,B** & **FTCs\_S,A** & **FTCs\_S,B** & **FTCs\_S,A** & **FTCs\_S,B** & **FTCs\_S,A** & **FTCs\_S,B** \\ \hline Average (Sele\_A) & 8.410 & 5.710 & 6.830 & 4.830 & 7.917 & 5.333 & 9.117 & 6.294 \\ Std (Sele\_A) & 4.542 & 4.341 & 3.380 & 2.791 & 3.309 & 2.884 & 3.140 & 4.089 \\ t-test (Sele\_A) & t(16) = 4.883 & t(11) = 2.966 & t(23) = 6.075 & t(16) = 3.289 \\ \(p\)-value (Sele\_A) & \(<\)0.001 & 0.013 & \(<\)0.001 & 0.005 & \\ Cohen’s d (Sele\_A) & 0.607 & 0.645 & 0.834 & 0.777 & \\ Average (Sele\_B) & 4.460 & 6.230 & 4.110 & 5.560 & 7.667 & 8.500 & 7.231 & 9.1538 \\ Std (Sele\_B) & 2.602 & 2.555 & 1.605 & 2.455 & 5.279 & 5.540 & 3.940 & 5.550 \\ t-test (Sele\_B) & t(12) = \(-3.320\) & t(17) = \(-2.247\) & t(5) = \(-3.464\) & t(12) = \(-2.241\) \\ \(p\)-value (Sele\_B) & 0.006 & 0.038 & 0.018 & 0.045 & \\ Cohen’s d (Sele\_B) & 0.686 & 0.699 & 0.378 & 0.398 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical analysis of FTCs for four scenes in both tests. Figure 6: Examples of fixation-transfer counts (FTCs) for four scenes. (**a**) Scene 1: FTCs in the red circle on the right were greater than those in the blue circle on the left; the final similarity choice was Texture B. (**b**) Scene 2: FTCs in the red circle on the right were greater than those in the blue circle on the left; the final similarity choice was Texture B. (**c**) Scene 3: FTCs in the blue circle on the right were less than those in the red circle on the left; the final similarity choice was Texture A. (**d**) Scene 4: FTCs in the blue circle on the right were less than those in the red circle on the left; the final similarity choice was Texture A. Regarding the first issue, TFD and FPV in each ROI were calculated to analyze eye-movement patterns. Generally, a high TFD value was attributed to the attractiveness of an ROI [41]. For Scenes 1-4, the TFD in Texture B was longer than that in Texture A. This reflected that Texture B got more attention. However, some studies had different views. Long fixation duration on an ROI indicates that the ROI is too complex to understand [42; 43]. In this research, textures in each scene were synthesized with the PGPC texture model. The complexities of grains and skeletons that were used in the synthesis were at the same level of complexity. Therefore, the only difference of complexity for Textures A and B was shown in directionality (for Scenes 1 and 2) and density (for Scenes 3 and 4). [PERSON] et al. [36] demonstrated that horizontal displays are easier to process. This also proves our finding that the TFD in Texture A was shorter than the TFD in Texture B. Previous research [24] demonstrated that similarity has a different effect on processing global and local features. In the present study, fixations in Textures A and B showed different distributions under similarity perception. Textures A and S had a similarity of global features, and the fixation points in Texture A were dispersed. Textures B and S had a similarity of local features, and the fixation points in Texture B were relatively concentrated.