Скачать или смотреть Feature Integration Theory FIT and Guided Search

This lecture will explore two influential models in the study of visual attention: Feature Integration Theory (FIT), developed by Anne Treisman, and Guided Search, proposed by Jeremy Wolfe. FIT explains how we perceive and identify objects by integrating features like color, shape, and orientation through a two-stage process involving pre-attentive parallel processing and focused attention for binding features. We will also discuss Wolfe's Guided Search model, which builds on FIT, proposing that parallel processes guide the deployment of attention during visual search, making conjunction searches more efficient.

Key topics include:

FIT’s two-stage process: the role of pre-attentive feature detection and focused attention for feature binding.
Pop-out effects in single-feature searches vs. serial processing in conjunction searches.
How Guided Search explains the flexibility of visual attention in complex environments by combining parallel and serial processes.
Practical applications of these theories in real-world visual tasks, such as interface design, visual search tasks, and security screening.
By the end of the lecture, students will understand how these models contribute to our understanding of how we perceive, identify, and search for objects in cluttered environments.