Visual attention is the cognitive process that allows individuals to selectively concentrate on specific visual information while ignoring other stimuli. It is crucial for efficiently processing complex visual environments and is influenced by both bottom-up sensory inputs and top-down cognitive processes.
Usability testing is a method used to evaluate a product by testing it on real users to identify any usability problems, collect qualitative and quantitative data, and determine the participant's satisfaction with the product. It is essential for ensuring that products are user-friendly, efficient, and effective, ultimately enhancing user experience and satisfaction.
Machine learning in eye tracking enhances the accuracy and efficiency of interpreting gaze data, enabling applications such as user behavior analysis, accessibility improvements, and psychological research. By leveraging algorithms to predict and classify gaze patterns, it transforms raw eye movement data into actionable insights, facilitating advancements in human-computer interaction and personalized user experiences.
A scanning pattern refers to the systematic way in which the eyes move to gather visual information from a scene or text. It is crucial for understanding visual perception, reading strategies, and human-computer interaction, as it reveals how individuals prioritize and process information visually.
Eye-tracking is a technology that measures where and how long a person looks at certain areas, providing insights into visual attention and cognitive processes. It is widely used in fields such as psychology, marketing, and human-computer interaction to understand behavior and optimize user experiences.