Examining the Influence of PSO Detection on Eye-Tracking During Reading

Post-saccadic oscillations (PSOs) are commonly observed in eye movement data but are often neglected, resulting in misclassifications of eye movement events and inaccurate interpretations. This study utilizes an event detection model based on CNN architecture that classifies eye movement data into three categories of events: fixations, saccades, and PSOs. The model is used to classify eye movement data collected during text reading. The results of the model are then compared with the commonly used classic SR Research software algorithm, which classifies eye movement data into fixations and saccades only. We compare the results and show that PSO detection significantly influences statistics for fixations (like average duration) that are commonly used to analyze different properties of the text being read (like complexity of the text). We postulate that PSOs should be taken into account in future eye movements when reading research.