## 4 Discussion  
### General Interpretation of the Results  
The findings of this review align with and extend prior evidence on the efficacy of eye-tracking metrics and computational techniques for understanding reading behaviour, cognitive workload, and attention. A key theme identified is the growing reliance on machine learning models to classify and interpret eye metrics such as fixations, saccades, and blinks. Studies using hybrid models, like CNN-LSTM combinations, consistently demonstrated superior accuracy compared to traditional methods, with reported accuracies as high as 94.2% for blink detection and outperforming for classification of eye movement types (e.g., fixations, saccades) across multiple datasets [15, 16, 17, 18]. These findings corroborate earlier research that emphasises the importance of model architecture and dataset quality in achieving the reliable classifications of eye movements. The review also underscores the utility of synthetic eye-tracking data in addressing data scarcity, with frameworks such as SP-EyeGAN showing significant promise in pre-training machine learning models [9, 25]. This innovation aligns with broader trends in data augmentation across machine learning domains and offers a scalable solution for enhancing performance in eye-tracking applications, especially in reading. Regarding reading comprehension, eye-tracking metrics provide valuable insights into cognitive processes. Fixation duration, saccade length, and regression patterns were commonly linked to reading efficiency and comprehension. For instance, multiple studies demonstrated that refutation texts improve comprehension by encouraging rereading [27], while machine learning models successfully classified reading behaviours (e.g., speed reading, in-depth reading). However, reliance on controlled experimental conditions limits the generalisability of these findings to real-world reading contexts. In the context of attention, eye-tracking metrics such as fixation count, duration, and saccadic velocity were strongly linked to visual attention and mental workload. For instance, decreasing blink rates were consistently associated with increased focus on specific targets. Frequent saccades relates to constant mind wandering and diminishing attention span. Machine learning models, particularly SVMs, achieved classification accuracies of approximately 79% using eye metrics alone, which increased to 88% when biosignals such as heart rate were included [13]. However, many studies relied on context-specific datasets, which limits the generalisability of these findings to reading tasks. The review also examined the impact of typography on attention and comprehension. Key findings highlight that intelligent typesetting, such as bolding and italicising task-relevant text, facilitates regression patterns that reduce cognitive load and improve the integration of new and prior information [10]. Conversely, fully capitalised text disrupts reading fluency by increasing fixation counts and saccades. A main finding includes that placing emphasis on important words in a sentence results in better memory. Additionally, optimal text spacing was shown to enhance reading efficiency, with diminishing returns observed when spacing exceeded a critical threshold [48]. Lastly, this review draws attention to the challenges of adapting mobile eye-tracking systems for reading tasks. While advancements in lightweight models and hardware have improved accessibility, studies point to limitations in accuracy and the influence of external factors, such as lighting and head movement [13]. These findings suggest a need for a further refinement of mobile systems to expand their applicability in diverse environments. In summary, this systematic review contributes to the growing body of evidence, demonstrating the interplay between eye-tracking metrics, machine learning methods, and reading behaviour.