## 3 Results and Discussion
### Role of Defectiveness in Shaping Visual Attention  
In order to test how the level of defectiveness caught the eye of the participants, the first fixation duration (FFD) values were analyzed using factorial ANOVA, where the factor area (with the level of severity going from 0 (control) to 6 (severely defected)) and the variety (three levels) were tested. Variety was tested to assess if there would be any effect of the color of the apple (red, yellow, or green). The results of the factorial ANOVA (Table 2) showed immediately that variety had no effect on the FFD values, but the level of defects emerged as one of the critical determinants in our analysis. There was a significant difference in the mean FFD (\(p\) < 0.01), indicating that there are at least two levels of defects where the differences are significant. The analysis revealed that the defectiveness of the apple significantly influenced all metrics, as the same pattern was achieved after completing the factorial ANOVA for the FC and FD as well, suggesting that participants' attention was strongly drawn to defects. Table 3 captures these findings. Under the pattern of the data, the authors mean that variety had no significant effect; only the area factor did. In the next step, a Tukey HSD test was completed on the area factor to assess which areas differed significantly. The Tukey HSD test results suggest that there are significant differences between the mean differences of the FFD in most pairs of areas. The lowest FFD values were observed for the control, while the highest were observed for levels 1 and 3. Interestingly, defect level 6 did not require more time for first fixation, which is understandable, as the defect at that level is big; therefore, it is easy to catch. The differences in gazing behaviors between participants from China and Hungary hint at the intriguing role of cultural factors in visual attention, which aligns with the research in [30] on cross-cultural factors influencing consumers' quality perceptions [32]. While the apple variety did not significantly influence the gazing patterns, the prominence of the apple's defectiveness as a determinant is undeniable. This aligns with the research \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** &
\begin{tabular}{c} **Sum of** \\ **Squares** \\ \end{tabular} & **df** & **Mean Square** & **F** & **Sig.** \\ \hline Corrected & 10.716 * & 20 & 0.536 & 5.059 & \textless{}0.01 \\ Model & 249.750 & 1 & 249.750 & 2358.001 & \textless{}0.01 \\ Variety & 0.053 & 2 & 0.026 & 0.249 & 0.780 \\ Area & 8.501 & 6 & 1.417 & 13.376 & \textless{}0.01 \\ Variety* area & 2.106 & 12 & 0.175 & 1.657 & 0.070 \\ Error & 180.587 & 1705 & 0.106 & & \\ Total & 439.442 & 1726 & & & \\ Corrected Total & 191.303 & 1725 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Factorial ANOVA of first fixation duration with factors area (7 levels: 0 (control) to 6 (severely defected)) and variety* (3 levels: GDG, GDY, and Idared). The symbol “a” represents statistically significant results (\(p\) < 0.01). by [13; 21; 32], which highlight the role of visual attention in individuals with dyslexia, indicating that dyslexics have been found to be worse than good readers at cueing visual attention and visual search. Furthermore, a study emphasized the concept of selective attention being guided to the most active location in the priority map, which resonates with the idea that participants' attention was drawn to specific areas of defectiveness in the apples [33]. Additionally, the research provides insights into gaze abnormalities and attention, particularly in individuals with autism, suggesting a fundamental problem with selecting socially relevant versus irrelevant information for attention, which could be relevant in understanding the influence of defectiveness on visual attention [34].