## 4 Results and Discussion  
The analysis of the Benton Visual Retention Test (BVRT) results conducted on a group of early school-age children provided valuable insights into their cognitive abilities and potential learning difficulties, particularly related to dyslexia. The study involved 145 children, each subjected to a series of trials aimed at assessing their ability to reproduce geometric patterns from memory. Based on the obtained results, appropriate risk levels of visual perception disorders were classified, as indicated by the range of errors made (see Table 4). Participants were classified based on the number of errors made and the overall accuracy in reproducing the patterns. The total number of errors made by the study participants consists of various types of inaccuracies, indicating the complexity of cognitive processes related to visual perception, visual memory, and visual constructional abilities. A statistical summary of the number of correct and incorrect reproductions is presented in Table 7.  
According to the task assumptions, the correctness of pattern execution and its reproduction from memory were evaluated. Errors indicating spatial function disturbances \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Quantity** \\ \hline Number of correct mappings & 746 \\ Number of incorrect mappings & 704 \\ Average number of correct mappings & 5.14 \\ Average number of misrepresentations & 4.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of errors. were mainly related to distortions, omissions, and displacements. A detailed breakdown of errors is presented in Table 8.  
Analyzing the results obtained from the conducted eye-tracking studies, differences between fixations during memorization and reproduction of figures can be observed. As shown in Figure 7, trajectories during observation and reproduction differ slightly. This can be particularly observed in the attention plots during memorization and reproduction of shapes. The Figure 12 illustrates the convergent learning process for the adopted network architecture. As can be seen, this process is highly unstable due to numerous similarities among individual attention trajectories. Additionally, one must consider the arbitrary manner in which the expert assigns scores in the BVRT test, which generates a locally biased information leakage effect. For the network used, a fit test of the trained network model was carried out using a linear regression model. The following results, shown in Table 9, were obtained for the designed network during testing mode (train/test ratio: 70%/30%). In Figure 13, a confusion matrix is also presented, which facilitates the final assessment of the determination level of the obtained model. In the test set containing 43 records, 42 records were identified correctly with one incorrect diagnosis, yielding an overall prediction accuracy of the LSTM network of 97.7%, which should be considered a high indicator. Figure 12: Training efficiency for the assumed net structure model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Types of Errors in Subjects** & **Quantity** \\ \hline Skip & 190 \\ Distortion & 212 \\ Perseverations & 61 \\ Rotation & 58 \\ Translation & 139 \\ Errors of relative magnitude & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of error types. Based on this, it can be concluded that the obtained coefficient of determination \(R^{2}\) indicates the high effectiveness of the proposed method of attentional analysis for diagnosing dyslexia. Early diagnosis of dyslexia is essential for maximizing the potential of individuals with dyslexia and for creating a more inclusive, educated, and economically stable society. By identifying and addressing dyslexia early, we can ensure that all individuals have the opportunity to succeed and contribute positively to their communities. Early diagnosis of dyslexia is especially essential for providing children with the support they need to succeed academically, emotionally, and socially.